From ddfc3c7e1b61013dca28fc9ba99cfb6caab5906e Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Fri, 6 Jan 2023 13:23:06 -0500 Subject: [PATCH] update tfjs --- CHANGELOG.md | 2 +- demo/faceid/index.js | 356 +- demo/faceid/index.js.map | 4 +- demo/typescript/index.js | 98 +- demo/typescript/index.js.map | 4 +- dist/human.esm-nobundle.js | 14363 +++++++++++++++- dist/human.esm.js | 26095 +++++++++++++++--------------- dist/human.esm.js.map | 6 +- dist/human.js | 1692 +- dist/human.node-gpu.js | 14467 ++++++++++++++++- dist/human.node-wasm.js | 14469 ++++++++++++++++- dist/human.node.js | 14467 ++++++++++++++++- dist/tfjs.esm.js | 1780 +- dist/tfjs.version.js | 33 +- test/build.log | 154 +- test/test.log | 2001 ++- typedoc/classes/GraphModel.html | 42 +- typedoc/classes/Tensor-1.html | 604 +- typedoc/enums/Rank.html | 16 +- typedoc/modules/Tensor.html | 278 +- typedoc/types/Tensor4D.html | 2 +- types/human.d.ts | 1 + 22 files changed, 74843 insertions(+), 16091 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9fe82647..dbf970cb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,7 @@ ## Changelog -### **HEAD -> main** 2022/12/29 mandic00@live.com +### **HEAD -> main** 2023/01/03 mandic00@live.com - default face.rotation disabled diff --git a/demo/faceid/index.js b/demo/faceid/index.js index 37f2e0ac..44cecc9a 100644 --- a/demo/faceid/index.js +++ b/demo/faceid/index.js @@ -4,6 +4,358 @@ author: ' */ -import*as S from"../../dist/human.esm.js";var l,L="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(L,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:0,modelBasePath:"../../models",filter:{enabled:!0,equalization:!0},debug:!0,face:{enabled:!0,detector:{rotation:!0,return:!0,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},r={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,distanceMin:.4,distanceMax:1,mask:g.face.detector.mask,rotation:g.face.detector.rotation,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},distance:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.distance.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},y={detect:0,draw:0},I=0,d=(...a)=>{t.log.innerText+=a.join(" ")+` -`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&d("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function z(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>r.blinkMin&&Math.abs(u.end-u.start)=r.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=r.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=r.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=r.minSize,e.distance.val=s.result.face[0].distance||0,e.distance.status=e.distance.val>=r.distanceMin&&e.distance.val<=r.distanceMax,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=r.minConfidence}return e.timeout.status=e.elapsedMs.val<=r.maxTime,z(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function F(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),d("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),d("known face records:",await b())}else d("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,r.minSize,r.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),d(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${100*(c.face.distance||0)}cm/${Math.round(100*(c.face.distance||0)/2.54)}in`),await s.tf.browser.toPixels(c.face.tensor,t.canvas),await b()===0)return d("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match.find(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(d(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>r.threshold?"darkgreen":"maroon",o.similarity>r.threshold}async function M(){var a,n,o,i;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((n=(a=c.face)==null?void 0:a.tensor)==null?void 0:n.shape[1])||r.minSize,t.canvas.height=((i=(o=c.face)==null?void 0:o.tensor)==null?void 0:i.shape[0])||r.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(d("did not find valid face"),!1)}async function q(){var a,n;d("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),d("options:",JSON.stringify(r).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),d("initializing webcam..."),await H(),d("loading human models..."),await s.load(),d("initializing human..."),d("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),d("loading face database..."),d("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",F),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q; + +// demo/faceid/index.ts +import * as H from "../../dist/human.esm.js"; + +// demo/faceid/indexdb.ts +var db; +var database = "human"; +var table = "person"; +var log = (...msg) => console.log("indexdb", ...msg); +async function open() { + if (db) + return true; + return new Promise((resolve) => { + const request = indexedDB.open(database, 1); + request.onerror = (evt) => log("error:", evt); + request.onupgradeneeded = (evt) => { + log("create:", evt.target); + db = evt.target.result; + db.createObjectStore(table, { keyPath: "id", autoIncrement: true }); + }; + request.onsuccess = (evt) => { + db = evt.target.result; + log("open:", db); + resolve(true); + }; + }); +} +async function load() { + const faceDB = []; + if (!db) + await open(); + return new Promise((resolve) => { + const cursor = db.transaction([table], "readwrite").objectStore(table).openCursor(null, "next"); + cursor.onerror = (evt) => log("load error:", evt); + cursor.onsuccess = (evt) => { + if (evt.target.result) { + faceDB.push(evt.target.result.value); + evt.target.result.continue(); + } else { + resolve(faceDB); + } + }; + }); +} +async function count() { + if (!db) + await open(); + return new Promise((resolve) => { + const store = db.transaction([table], "readwrite").objectStore(table).count(); + store.onerror = (evt) => log("count error:", evt); + store.onsuccess = () => resolve(store.result); + }); +} +async function save(faceRecord) { + if (!db) + await open(); + const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; + db.transaction([table], "readwrite").objectStore(table).put(newRecord); + log("save:", newRecord); +} +async function remove(faceRecord) { + if (!db) + await open(); + db.transaction([table], "readwrite").objectStore(table).delete(faceRecord.id); + log("delete:", faceRecord); +} + +// demo/faceid/index.ts +var humanConfig = { + cacheSensitivity: 0, + modelBasePath: "../../models", + filter: { enabled: true, equalization: true }, + debug: true, + face: { + enabled: true, + detector: { rotation: true, return: true, mask: false }, + description: { enabled: true }, + iris: { enabled: true }, + emotion: { enabled: false }, + antispoof: { enabled: true }, + liveness: { enabled: true } + }, + body: { enabled: false }, + hand: { enabled: false }, + object: { enabled: false }, + gesture: { enabled: true } +}; +var matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; +var options = { + minConfidence: 0.6, + minSize: 224, + maxTime: 3e4, + blinkMin: 10, + blinkMax: 800, + threshold: 0.5, + distanceMin: 0.4, + distanceMax: 1, + mask: humanConfig.face.detector.mask, + rotation: humanConfig.face.detector.rotation, + ...matchOptions +}; +var ok = { + faceCount: { status: false, val: 0 }, + faceConfidence: { status: false, val: 0 }, + facingCenter: { status: false, val: 0 }, + lookingCenter: { status: false, val: 0 }, + blinkDetected: { status: false, val: 0 }, + faceSize: { status: false, val: 0 }, + antispoofCheck: { status: false, val: 0 }, + livenessCheck: { status: false, val: 0 }, + distance: { status: false, val: 0 }, + age: { status: false, val: 0 }, + gender: { status: false, val: 0 }, + timeout: { status: true, val: 0 }, + descriptor: { status: false, val: 0 }, + elapsedMs: { status: void 0, val: 0 }, + detectFPS: { status: void 0, val: 0 }, + drawFPS: { status: void 0, val: 0 } +}; +var allOk = () => ok.faceCount.status && ok.faceSize.status && ok.blinkDetected.status && ok.facingCenter.status && ok.lookingCenter.status && ok.faceConfidence.status && ok.antispoofCheck.status && ok.livenessCheck.status && ok.distance.status && ok.descriptor.status && ok.age.status && ok.gender.status; +var current = { face: null, record: null }; +var blink = { + start: 0, + end: 0, + time: 0 +}; +var human = new H.Human(humanConfig); +human.env.perfadd = false; +human.draw.options.font = 'small-caps 18px "Lato"'; +human.draw.options.lineHeight = 20; +var dom = { + video: document.getElementById("video"), + canvas: document.getElementById("canvas"), + log: document.getElementById("log"), + fps: document.getElementById("fps"), + match: document.getElementById("match"), + name: document.getElementById("name"), + save: document.getElementById("save"), + delete: document.getElementById("delete"), + retry: document.getElementById("retry"), + source: document.getElementById("source"), + ok: document.getElementById("ok") +}; +var timestamp = { detect: 0, draw: 0 }; +var startTime = 0; +var log2 = (...msg) => { + dom.log.innerText += msg.join(" ") + "\n"; + console.log(...msg); +}; +async function webCam() { + const cameraOptions = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } }; + const stream = await navigator.mediaDevices.getUserMedia(cameraOptions); + const ready = new Promise((resolve) => { + dom.video.onloadeddata = () => resolve(true); + }); + dom.video.srcObject = stream; + void dom.video.play(); + await ready; + dom.canvas.width = dom.video.videoWidth; + dom.canvas.height = dom.video.videoHeight; + dom.canvas.style.width = "50%"; + dom.canvas.style.height = "50%"; + if (human.env.initial) + log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label); + dom.canvas.onclick = () => { + if (dom.video.paused) + void dom.video.play(); + else + dom.video.pause(); + }; +} +async function detectionLoop() { + var _a; + if (!dom.video.paused) { + if ((_a = current.face) == null ? void 0 : _a.tensor) + human.tf.dispose(current.face.tensor); + await human.detect(dom.video); + const now = human.now(); + ok.detectFPS.val = Math.round(1e4 / (now - timestamp.detect)) / 10; + timestamp.detect = now; + requestAnimationFrame(detectionLoop); + } +} +function drawValidationTests() { + let y = 32; + for (const [key, val] of Object.entries(ok)) { + let el = document.getElementById(`ok-${key}`); + if (!el) { + el = document.createElement("div"); + el.id = `ok-${key}`; + el.innerText = key; + el.className = "ok"; + el.style.top = `${y}px`; + dom.ok.appendChild(el); + } + if (typeof val.status === "boolean") + el.style.backgroundColor = val.status ? "lightgreen" : "lightcoral"; + const status = val.status ? "ok" : "fail"; + el.innerText = `${key}: ${val.val === 0 ? status : val.val}`; + y += 28; + } +} +async function validationLoop() { + var _a; + const interpolated = human.next(human.result); + human.draw.canvas(dom.video, dom.canvas); + await human.draw.all(dom.canvas, interpolated); + const now = human.now(); + ok.drawFPS.val = Math.round(1e4 / (now - timestamp.draw)) / 10; + timestamp.draw = now; + ok.faceCount.val = human.result.face.length; + ok.faceCount.status = ok.faceCount.val === 1; + if (ok.faceCount.status) { + const gestures = Object.values(human.result.gesture).map((gesture) => gesture.gesture); + if (gestures.includes("blink left eye") || gestures.includes("blink right eye")) + blink.start = human.now(); + if (blink.start > 0 && !gestures.includes("blink left eye") && !gestures.includes("blink right eye")) + blink.end = human.now(); + ok.blinkDetected.status = ok.blinkDetected.status || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax; + if (ok.blinkDetected.status && blink.time === 0) + blink.time = Math.trunc(blink.end - blink.start); + ok.facingCenter.status = gestures.includes("facing center"); + ok.lookingCenter.status = gestures.includes("looking center"); + ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0; + ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence; + ok.antispoofCheck.val = human.result.face[0].real || 0; + ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence; + ok.livenessCheck.val = human.result.face[0].live || 0; + ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence; + ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]); + ok.faceSize.status = ok.faceSize.val >= options.minSize; + ok.distance.val = human.result.face[0].distance || 0; + ok.distance.status = ok.distance.val >= options.distanceMin && ok.distance.val <= options.distanceMax; + ok.descriptor.val = ((_a = human.result.face[0].embedding) == null ? void 0 : _a.length) || 0; + ok.descriptor.status = ok.descriptor.val > 0; + ok.age.val = human.result.face[0].age || 0; + ok.age.status = ok.age.val > 0; + ok.gender.val = human.result.face[0].genderScore || 0; + ok.gender.status = ok.gender.val >= options.minConfidence; + } + ok.timeout.status = ok.elapsedMs.val <= options.maxTime; + drawValidationTests(); + if (allOk() || !ok.timeout.status) { + dom.video.pause(); + return human.result.face[0]; + } + ok.elapsedMs.val = Math.trunc(human.now() - startTime); + return new Promise((resolve) => { + setTimeout(async () => { + await validationLoop(); + resolve(human.result.face[0]); + }, 30); + }); +} +async function saveRecords() { + var _a, _b, _c, _d; + if (dom.name.value.length > 0) { + const image = (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, dom.canvas.width, dom.canvas.height); + const rec = { id: 0, name: dom.name.value, descriptor: (_b = current.face) == null ? void 0 : _b.embedding, image }; + await save(rec); + log2("saved face record:", rec.name, "descriptor length:", (_d = (_c = current.face) == null ? void 0 : _c.embedding) == null ? void 0 : _d.length); + log2("known face records:", await count()); + } else { + log2("invalid name"); + } +} +async function deleteRecord() { + if (current.record && current.record.id > 0) { + await remove(current.record); + } +} +async function detectFace() { + var _a, _b, _c, _d; + dom.canvas.style.height = ""; + (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize); + if (!((_b = current == null ? void 0 : current.face) == null ? void 0 : _b.tensor) || !((_c = current == null ? void 0 : current.face) == null ? void 0 : _c.embedding)) + return false; + console.log("face record:", current.face); + log2(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`); + await human.tf.browser.toPixels(current.face.tensor, dom.canvas); + if (await count() === 0) { + log2("face database is empty: nothing to compare face with"); + document.body.style.background = "black"; + dom.delete.style.display = "none"; + return false; + } + const db2 = await load(); + const descriptors = db2.map((rec) => rec.descriptor).filter((desc) => desc.length > 0); + const res = human.match.find(current.face.embedding, descriptors, matchOptions); + current.record = db2[res.index] || null; + if (current.record) { + log2(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1e3 * res.similarity) / 10}%`); + dom.name.value = current.record.name; + dom.source.style.display = ""; + (_d = dom.source.getContext("2d")) == null ? void 0 : _d.putImageData(current.record.image, 0, 0); + } + document.body.style.background = res.similarity > options.threshold ? "darkgreen" : "maroon"; + return res.similarity > options.threshold; +} +async function main() { + var _a, _b, _c, _d; + ok.faceCount.status = false; + ok.faceConfidence.status = false; + ok.facingCenter.status = false; + ok.blinkDetected.status = false; + ok.faceSize.status = false; + ok.antispoofCheck.status = false; + ok.livenessCheck.status = false; + ok.age.status = false; + ok.gender.status = false; + ok.elapsedMs.val = 0; + dom.match.style.display = "none"; + dom.retry.style.display = "none"; + dom.source.style.display = "none"; + dom.canvas.style.height = "50%"; + document.body.style.background = "black"; + await webCam(); + await detectionLoop(); + startTime = human.now(); + current.face = await validationLoop(); + dom.canvas.width = ((_b = (_a = current.face) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape[1]) || options.minSize; + dom.canvas.height = ((_d = (_c = current.face) == null ? void 0 : _c.tensor) == null ? void 0 : _d.shape[0]) || options.minSize; + dom.source.width = dom.canvas.width; + dom.source.height = dom.canvas.height; + dom.canvas.style.width = ""; + dom.match.style.display = "flex"; + dom.save.style.display = "flex"; + dom.delete.style.display = "flex"; + dom.retry.style.display = "block"; + if (!allOk()) { + log2("did not find valid face"); + return false; + } + return detectFace(); +} +async function init() { + var _a, _b; + log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); + log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " ")); + log2("initializing webcam..."); + await webCam(); + log2("loading human models..."); + await human.load(); + log2("initializing human..."); + log2("face embedding model:", humanConfig.face.description.enabled ? "faceres" : "", ((_a = humanConfig.face["mobilefacenet"]) == null ? void 0 : _a.enabled) ? "mobilefacenet" : "", ((_b = humanConfig.face["insightface"]) == null ? void 0 : _b.enabled) ? "insightface" : ""); + log2("loading face database..."); + log2("known face records:", await count()); + dom.retry.addEventListener("click", main); + dom.save.addEventListener("click", saveRecords); + dom.delete.addEventListener("click", deleteRecord); + await human.warmup(); + await main(); +} +window.onload = init; //# sourceMappingURL=index.js.map diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map index a089c033..12daf6a7 100644 --- a/demo/faceid/index.js.map +++ b/demo/faceid/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "indexdb.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n cacheSensitivity: 0,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: true }, // lets run with histogram equilizer\n debug: true,\n face: {\n enabled: true,\n detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 30000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm\n distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n ...matchOptions,\n};\n\nconst ok: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n distance: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.distance.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) void dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.distance.val = human.result.face[0].distance || 0;\n ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);\n await human.tf.browser.toPixels(current.face.tensor, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n const res = human.match.find(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n }\n return detectFace();\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], - "mappings": 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+ "names": ["log", "db"] } diff --git a/demo/typescript/index.js b/demo/typescript/index.js index 2bf3f49d..14dfc71c 100644 --- a/demo/typescript/index.js +++ b/demo/typescript/index.js @@ -4,6 +4,100 @@ author: ' */ -import*as m from"../../dist/human.esm.js";var v=1920,b={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!0},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+` -`,console.log(...t)},r=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function p(){var d,i,c;if(!a.video.paused){let l=e.next(e.result),u=await e.image(a.video);e.draw.canvas(u.canvas,a.canvas);let w={bodyLabels:`person confidence [score] and ${(c=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,w),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(p,30)}async function y(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!1,width:v,id:d}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function h(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),r("initializing..."),await e.warmup(),await y(),await f(),await p()}window.onload=h; + +// demo/typescript/index.ts +import * as H from "../../dist/human.esm.js"; +var width = 1920; +var humanConfig = { + modelBasePath: "../../models", + filter: { enabled: true, equalization: false, flip: false }, + face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } }, + body: { enabled: true }, + hand: { enabled: false }, + object: { enabled: false }, + segmentation: { enabled: false }, + gesture: { enabled: true } +}; +var human = new H.Human(humanConfig); +human.env.perfadd = false; +human.draw.options.font = 'small-caps 18px "Lato"'; +human.draw.options.lineHeight = 20; +var dom = { + video: document.getElementById("video"), + canvas: document.getElementById("canvas"), + log: document.getElementById("log"), + fps: document.getElementById("status"), + perf: document.getElementById("performance") +}; +var timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; +var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; +var log = (...msg) => { + dom.log.innerText += msg.join(" ") + "\n"; + console.log(...msg); +}; +var status = (msg) => dom.fps.innerText = msg; +var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors.toString() + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | "); +async function detectionLoop() { + if (!dom.video.paused) { + if (timestamp.start === 0) + timestamp.start = human.now(); + await human.detect(dom.video); + const tensors = human.tf.memory().numTensors; + if (tensors - timestamp.tensors !== 0) + log("allocated tensors:", tensors - timestamp.tensors); + timestamp.tensors = tensors; + fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3; + fps.frames++; + fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3; + if (fps.frames % 100 === 0 && !dom.video.paused) + log("performance", { ...fps, tensors: timestamp.tensors }); + } + timestamp.detect = human.now(); + requestAnimationFrame(detectionLoop); +} +async function drawLoop() { + var _a, _b, _c; + if (!dom.video.paused) { + const interpolated = human.next(human.result); + const processed = await human.image(dom.video); + human.draw.canvas(processed.canvas, dom.canvas); + const opt = { bodyLabels: `person confidence [score] and ${(_c = (_b = (_a = human.result) == null ? void 0 : _a.body) == null ? void 0 : _b[0]) == null ? void 0 : _c.keypoints.length} keypoints` }; + await human.draw.all(dom.canvas, interpolated, opt); + perf(interpolated.performance); + } + const now = human.now(); + fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3; + timestamp.draw = now; + status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`); + setTimeout(drawLoop, 30); +} +async function webCam() { + const devices = await human.webcam.enumerate(); + const id = devices[0].deviceId; + await human.webcam.start({ element: dom.video, crop: false, width, id }); + dom.canvas.width = human.webcam.width; + dom.canvas.height = human.webcam.height; + dom.canvas.onclick = async () => { + if (human.webcam.paused) + await human.webcam.play(); + else + human.webcam.pause(); + }; +} +async function main() { + log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); + log("platform:", human.env.platform, "| agent:", human.env.agent); + status("loading..."); + await human.load(); + log("backend:", human.tf.getBackend(), "| available:", human.env.backends); + log("models stats:", human.models.stats()); + log("models loaded:", human.models.loaded()); + log("environment", human.env); + status("initializing..."); + await human.warmup(); + await webCam(); + await detectionLoop(); + await drawLoop(); +} +window.onload = main; //# sourceMappingURL=index.js.map diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index 622fbf92..62861400 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n // backend: 'webgpu',\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: true },\n // hand: { enabled: true },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 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V5=` +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; +var __export = (target, all2) => { + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __reExport = (target, mod3, secondTarget) => (__copyProps(target, mod3, "default"), secondTarget && __copyProps(secondTarget, mod3, "default")); +var __publicField = (obj, key, value) => { + __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); + return value; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + version: () => version7 +}); +__reExport(tfjs_esm_exports, dist_star); 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version5, + "tfjs-backend-webgpu": version6 +}; + +// src/util/util.ts +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`modelpath error: expecting json file: ${path}`); + return path; +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function validate(defaults, config3, parent = "config", msgs = []) { + for (const key of Object.keys(config3)) { + if (typeof config3[key] === "object") { + validate(defaults[key], config3[key], key, msgs); + } else { + const defined = defaults && typeof defaults[key] !== "undefined"; + if (!defined) + msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); + const same = defaults && typeof defaults[key] === typeof config3[key]; + if (defined && !same) + msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); + } + } + if (config3.debug && parent === "config" && msgs.length > 0) + log("invalid configuration", msgs); + return msgs; +} +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "", + modelBasePath: "", + cacheModels: true, + validateModels: true, + wasmPath: "", + wasmPlatformFetch: false, + debug: false, + async: true, + warmup: "full", + cacheSensitivity: 0.7, + skipAllowed: false, + deallocate: false, + flags: {}, + softwareKernels: false, + filter: { + enabled: true, + equalization: false, + width: 0, + height: 0, + flip: false, + return: true, + autoBrightness: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: false, + maxDetected: 1, + skipFrames: 99, + skipTime: 2500, + minConfidence: 0.2, + iouThreshold: 0.1, + mask: false, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json", + keepInvalid: false + }, + attention: { + enabled: false, + modelPath: "facemesh-attention.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 99, + skipTime: 1500, + modelPath: "emotion.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 99, + skipTime: 3e3, + minConfidence: 0.1 + }, + antispoof: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "antispoof.json" + }, + liveness: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "liveness.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: -1, + minConfidence: 0.3, + skipFrames: 1, + skipTime: 200 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 99, + skipTime: 1e3, + minConfidence: 0.5, + iouThreshold: 0.2, + maxDetected: -1, + landmarks: true, + detector: { + modelPath: "handtrack.json" + }, + skeleton: { + modelPath: "handlandmark-lite.json" + } + }, + object: { + enabled: false, + modelPath: "centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 99, + skipTime: 2e3 + }, + segmentation: { + enabled: false, + modelPath: "rvm.json", + ratio: 0.5, + mode: "default" + } +}; + +// src/image/imagefxshaders.ts +var vertexIdentity = ` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -14,7 +278,8 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var Z5=` +`; +var colorMatrixWithAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -26,7 +291,8 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,X5=` +`; +var colorMatrixWithoutAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -38,7 +304,8 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,q5=` +`; +var pixelate = ` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -51,7 +318,8 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,U5=` +`; +var blur = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -74,7 +342,8 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`,Y5=` +`; +var convolution = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -96,20 +365,6046 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`;var at=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ 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0.3)":G.color,t.fill())}}function Cr(e,t){var n;if(G.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*be(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*be(e.rotation.angle.pitch)/90,A=new Path2D(` - M ${e.box[0]+e.box[2]/2} ${e.box[1]} +`; + +// src/image/imagefx.ts +var collect = (source, prefix, collection) => { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match2, name) => { + collection[name] = 0; + return match2; + }); +}; +var GLProgram = class { + constructor(gl, vertexSource, fragmentSource) { + __publicField(this, "uniform", {}); + __publicField(this, "attribute", {}); + __publicField(this, "gl"); + __publicField(this, "id"); + __publicField(this, "compile", (source, type) => { + const shader = this.gl.createShader(type); + if (!shader) { + log("filter: could not create shader"); + return null; + } + this.gl.shaderSource(shader, source); + this.gl.compileShader(shader); + if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { + log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); + return null; + } + return shader; + }); + this.gl = gl; + const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); + const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); + this.id = this.gl.createProgram(); + if (!vertexShader || !fragmentShader) + return; + if (!this.id) { + log("filter: could not create webgl program"); + return; + } + this.gl.attachShader(this.id, vertexShader); + this.gl.attachShader(this.id, fragmentShader); + this.gl.linkProgram(this.id); + if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { + log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); + return; + } + this.gl.useProgram(this.id); + collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = this.gl.getAttribLocation(this.id, a); + collect(vertexSource, "uniform", this.uniform); + collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = this.gl.getUniformLocation(this.id, u); + } +}; +function GLImageFilter() { + let drawCount = 0; + let sourceTexture = null; + let lastInChain = false; + let currentFramebufferIndex = -1; + let tempFramebuffers = [null, null]; + let filterChain = []; + let vertexBuffer = null; + let currentProgram = null; + const fxcanvas = canvas(100, 100); + const shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = fxcanvas.getContext("webgl"); + if (!gl) { + log("filter: cannot get webgl context"); + return; + } + this.gl = gl; + function resize(width, height) { + if (width === fxcanvas.width && height === fxcanvas.height) + return; + fxcanvas.width = width; + fxcanvas.height = height; + if (!vertexBuffer) { + const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); + vertexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); + tempFramebuffers = [null, null]; + } + function createFramebufferTexture(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + } + function getTempFramebuffer(index2) { + tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); + return tempFramebuffers[index2]; + } + function draw(flags = 0) { + if (!currentProgram) + return; + let source = null; + let target = null; + let flipY = false; + if (drawCount === 0) + source = sourceTexture; + else + source = getTempFramebuffer(currentFramebufferIndex).texture || null; + drawCount++; + if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = drawCount % 2 === 0; + } else { + currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; + target = getTempFramebuffer(currentFramebufferIndex).fbo || null; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + } + function compileShader(fragmentSource) { + if (shaderProgramCache[fragmentSource]) { + currentProgram = shaderProgramCache[fragmentSource]; + gl.useProgram((currentProgram ? currentProgram.id : null) || null); + return currentProgram; + } + currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); + if (!currentProgram) { + log("filter: could not get webgl program"); + return null; + } + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(currentProgram.attribute["pos"]); + gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(currentProgram.attribute["uv"]); + gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); + shaderProgramCache[fragmentSource] = currentProgram; + return currentProgram; + } + const filter = { + colorMatrix: (matrix) => { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; + const program = compileShader(shader); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + draw(); + }, + brightness: (brightness) => { + const b = (brightness || 0) + 1; + filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + saturation: (amount) => { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturate: () => { + filter.saturation(-1); + }, + contrast: (amount) => { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }, + negative: () => { + filter.contrast(-2); + }, + hue: (rotation) => { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturateLuminance: () => { + filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }, + sepia: () => { + filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + brownie: () => { + filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }, + vintagePinhole: () => { + filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }, + kodachrome: () => { + filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }, + technicolor: () => { + filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }, + polaroid: () => { + filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + shiftToBGR: () => { + filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + convolution: (matrix) => { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / fxcanvas.width; + const pixelSizeY = 1 / fxcanvas.height; + const program = compileShader(convolution); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); + draw(); + }, + detectEdges: () => { + filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }, + sobelX: () => { + filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }, + sobelY: () => { + filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }, + sharpen: (amount) => { + const a = amount || 1; + filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }, + emboss: (size2) => { + const s = size2 || 1; + filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }, + blur: (size2) => { + const blurSizeX = size2 / 7 / fxcanvas.width; + const blurSizeY = size2 / 7 / fxcanvas.height; + const program = compileShader(blur); + if (!program) + return; + gl.uniform2f(program.uniform["px"], 0, blurSizeY); + draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform["px"], blurSizeX, 0); + draw(); + }, + pixelate: (size2) => { + const blurSizeX = size2 / fxcanvas.width; + const blurSizeY = size2 / fxcanvas.height; + const program = compileShader(pixelate); + if (!program) + return; + gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); + draw(); + } + }; + this.add = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const func = filter[name]; + filterChain.push({ func, args }); + }; + this.reset = function() { + filterChain = []; + }; + this.get = function() { + return filterChain; + }; + this.apply = function(image28) { + resize(image28.width, image28.height); + drawCount = 0; + if (!sourceTexture) + sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); + for (let i = 0; i < filterChain.length; i++) { + lastInChain = i === filterChain.length - 1; + const f = filterChain[i]; + f.func.apply(this, f.args || []); + } + return fxcanvas; + }; + this.draw = function(image28) { + this.add("brightness", 0); + return this.apply(image28); + }; +} + +// src/image/enhance.ts +async function histogramEqualization(inputImage) { + const squeeze14 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage; + const rgb2 = tfjs_esm_exports.split(squeeze14, 3, 2); + const min2 = [tfjs_esm_exports.min(rgb2[0]), tfjs_esm_exports.min(rgb2[1]), tfjs_esm_exports.min(rgb2[2])]; + const max5 = [tfjs_esm_exports.max(rgb2[0]), tfjs_esm_exports.max(rgb2[1]), tfjs_esm_exports.max(rgb2[2])]; + const absMax = await Promise.all(max5.map((channel) => channel.data())); + const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); + const maxRange = maxValue > 1 ? 255 : 1; + const factor = maxRange / maxValue; + let final; + if (factor > 1) { + const sub11 = [tfjs_esm_exports.sub(rgb2[0], min2[0]), tfjs_esm_exports.sub(rgb2[1], min2[1]), tfjs_esm_exports.sub(rgb2[2], min2[2])]; + const range = [tfjs_esm_exports.sub(max5[0], min2[0]), tfjs_esm_exports.sub(max5[1], min2[1]), tfjs_esm_exports.sub(max5[2], min2[2])]; + const enh = [tfjs_esm_exports.mul(sub11[0], factor), tfjs_esm_exports.mul(sub11[1], factor), tfjs_esm_exports.mul(sub11[2], factor)]; + const stack5 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2); + final = tfjs_esm_exports.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); + tfjs_esm_exports.dispose([...sub11, ...range, ...enh]); + } else { + final = tfjs_esm_exports.expandDims(squeeze14, 0); + } + tfjs_esm_exports.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); + return final; +} + +// src/image/image.ts +var maxSize = 3840; +var inCanvas = null; +var outCanvas = null; +var tmpCanvas = null; +var fx; +var last = { + inputSum: 0, + cacheDiff: 1, + sumMethod: 0, + inputTensor: void 0 +}; +function reset() { + last.inputSum = 0; + last.cacheDiff = 1; + last.sumMethod = 0; + last.inputTensor = void 0; +} +function canvas(width, height) { + let c; + if (env.browser) { + if (env.worker) { + if (typeof OffscreenCanvas === "undefined") + throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); + c = new OffscreenCanvas(width, height); + } else { + if (typeof document === "undefined") + throw new Error("canvas error: attempted to run in browser but DOM is not defined"); + c = document.createElement("canvas"); + c.width = width; + c.height = height; + } + } else { + if (typeof env.Canvas !== "undefined") + c = new env.Canvas(width, height); + else if (typeof globalThis.Canvas !== "undefined") + c = new globalThis.Canvas(width, height); + } + return c; +} +function copy(input, output) { + const outputCanvas = output || canvas(input.width, input.height); + const ctx = outputCanvas.getContext("2d"); + ctx.drawImage(input, 0, 0); + return outputCanvas; +} +async function process2(input, config3, getTensor = true) { + var _a, _b, _c; + if (!input) { + if (config3.debug) + log("input error: input is missing"); + return { tensor: null, canvas: null }; + } + if (!(input instanceof tfjs_esm_exports.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("input error: type not recognized"); + } + if (input instanceof tfjs_esm_exports.Tensor) { + let tensor7 = null; + if (input["isDisposedInternal"]) + throw new Error("input error: attempted to use tensor but it is disposed"); + if (!input.shape) + throw new Error("input error: attempted to use tensor without a shape"); + if (input.shape.length === 3) { + if (input.shape[2] === 3) { + tensor7 = tfjs_esm_exports.expandDims(input, 0); + } else if (input.shape[2] === 4) { + const rgb2 = tfjs_esm_exports.slice3d(input, [0, 0, 0], [-1, -1, 3]); + tensor7 = tfjs_esm_exports.expandDims(rgb2, 0); + tfjs_esm_exports.dispose(rgb2); + } + } else if (input.shape.length === 4) { + if (input.shape[3] === 3) { + tensor7 = tfjs_esm_exports.clone(input); + } else if (input.shape[3] === 4) { + tensor7 = tfjs_esm_exports.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + } + } + if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) + throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); + if (tensor7.dtype === "int32") { + const cast8 = tfjs_esm_exports.cast(tensor7, "float32"); + tfjs_esm_exports.dispose(tensor7); + tensor7 = cast8; + } + return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; + } + if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { + if (config3.debug) + log("input stream is not ready"); + return { tensor: null, canvas: inCanvas }; + } + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) { + if (config3.debug) + log("cannot determine input dimensions"); + return { tensor: null, canvas: inCanvas }; + } + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); + } + if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) + targetWidth = config3.filter.width; + else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) + targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); + if ((config3.filter.height || 0) > 0) + targetHeight = config3.filter.height; + else if ((config3.filter.width || 0) > 0) + targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("input error: cannot determine dimension"); + if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) + inCanvas = canvas(targetWidth, targetHeight); + const inCtx = inCanvas.getContext("2d"); + if (typeof ImageData !== "undefined" && input instanceof ImageData) { + inCtx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof inCtx.translate !== "undefined") { + inCtx.translate(originalWidth, 0); + inCtx.scale(-1, 1); + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + inCtx.setTransform(1, 0, 0, 1, 0, 0); + } else { + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + } + } + if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) + outCanvas = canvas(inCanvas.width, inCanvas.height); + if (config3.filter.enabled && env.webgl.supported) { + if (!fx) + fx = env.browser ? new GLImageFilter() : null; + env.filter = !!fx; + if (!(fx == null ? void 0 : fx.add)) { + if (config3.debug) + log("input process error: cannot initialize filters"); + env.webgl.supported = false; + config3.filter.enabled = false; + copy(inCanvas, outCanvas); + } else { + fx.reset(); + if (config3.filter.brightness !== 0) + fx.add("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.add("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.add("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.add("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.add("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.add("hue", config3.filter.hue); + if (config3.filter.negative) + fx.add("negative"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.vintage) + fx.add("brownie"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.kodachrome) + fx.add("kodachrome"); + if (config3.filter.technicolor) + fx.add("technicolor"); + if (config3.filter.polaroid) + fx.add("polaroid"); + if (config3.filter.pixelate !== 0) + fx.add("pixelate", config3.filter.pixelate); + if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) + outCanvas = fx.apply(inCanvas); + else + outCanvas = fx.draw(inCanvas); + } + } else { + copy(inCanvas, outCanvas); + if (fx) + fx = null; + env.filter = !!fx; + } + if (!getTensor) + return { tensor: null, canvas: outCanvas }; + if (!outCanvas) + throw new Error("canvas error: cannot create output"); + let pixels; + let depth = 3; + if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { + if (env.browser && tfjs_esm_exports.browser) { + pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(input) : null; + } else { + depth = input.data.length / input.height / input.width; + const arr = new Uint8Array(input.data.buffer); + pixels = tfjs_esm_exports.tensor(arr, [input.height, input.width, depth], "int32"); + } + } else { + if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) + tmpCanvas = canvas(outCanvas.width, outCanvas.height); + if (tfjs_esm_exports.browser && env.browser) { + if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { + pixels = tfjs_esm_exports.browser.fromPixels(outCanvas); + } else { + tmpCanvas = copy(outCanvas); + pixels = tfjs_esm_exports.browser.fromPixels(tmpCanvas); + } + } else { + const tempCanvas = copy(outCanvas); + const tempCtx = tempCanvas.getContext("2d"); + const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); + depth = tempData.data.length / targetWidth / targetHeight; + const arr = new Uint8Array(tempData.data.buffer); + pixels = tfjs_esm_exports.tensor(arr, [targetWidth, targetHeight, depth]); + } + } + if (depth === 4) { + const rgb2 = tfjs_esm_exports.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); + tfjs_esm_exports.dispose(pixels); + pixels = rgb2; + } + if (!pixels) + throw new Error("input error: cannot create tensor"); + const casted = tfjs_esm_exports.cast(pixels, "float32"); + const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0); + tfjs_esm_exports.dispose([pixels, casted]); + if (config3.filter.autoBrightness) { + const max5 = tfjs_esm_exports.max(tensor6); + const maxVal = await max5.data(); + config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; + tfjs_esm_exports.dispose(max5); + } + return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; +} +async function skip(config3, input) { + let skipFrame = false; + if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) + return skipFrame; + if (!last.inputTensor) { + last.inputTensor = tfjs_esm_exports.clone(input); + } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { + tfjs_esm_exports.dispose(last.inputTensor); + last.inputTensor = tfjs_esm_exports.clone(input); + } else { + const t2 = {}; + t2.diff = tfjs_esm_exports.sub(input, last.inputTensor); + t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); + t2.sum = tfjs_esm_exports.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; + tfjs_esm_exports.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); + last.inputTensor = tfjs_esm_exports.clone(input); + skipFrame = diffRelative <= (config3.cacheSensitivity || 0); + } + return skipFrame; +} +async function compare(config3, input1, input2) { + const t2 = {}; + if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { + if (!config3.debug) + log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); + return 0; + } + if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { + if (!config3.debug) + log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); + return 0; + } + t2.input1 = tfjs_esm_exports.clone(input1); + t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2); + t2.diff = tfjs_esm_exports.sub(t2.input1, t2.input2); + t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); + t2.sum = tfjs_esm_exports.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; + tfjs_esm_exports.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); + return diffRelative; +} + +// src/util/env.ts +var _canvas, _image, _imageData; +var Env = class { + constructor() { + __publicField(this, "browser"); + __publicField(this, "node"); + __publicField(this, "worker"); + __publicField(this, "platform", ""); + __publicField(this, "agent", ""); + __publicField(this, "backends", []); + __publicField(this, "initial"); + __publicField(this, "filter"); + __publicField(this, "tfjs"); + __publicField(this, "offscreen"); + __publicField(this, "perfadd", false); + __publicField(this, "tensorflow", { + version: void 0, + gpu: void 0 + }); + __publicField(this, "wasm", { + supported: void 0, + backend: void 0, + simd: void 0, + multithread: void 0 + }); + __publicField(this, "webgl", { + supported: void 0, + backend: void 0, + version: void 0, + renderer: void 0, + shader: void 0, + vendor: void 0 + }); + __publicField(this, "webgpu", { + supported: void 0, + backend: void 0, + adapter: void 0 + }); + __publicField(this, "cpu", { + model: void 0, + flags: [] + }); + __publicField(this, "kernels", []); + __privateAdd(this, _canvas, void 0); + __privateAdd(this, _image, void 0); + __privateAdd(this, _imageData, void 0); + this.browser = typeof navigator !== "undefined"; + this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; + this.tfjs = { version: version7["tfjs-core"] }; + this.offscreen = typeof OffscreenCanvas !== "undefined"; + this.initial = true; + this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw == null ? void 0 : raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; + this.agent = navigator.userAgent.replace(raw[0], ""); + if (this.platform[1]) + this.agent = this.agent.replace(raw[1], ""); + this.agent = this.agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + this.platform = `${process.platform} ${process.arch}`; + this.agent = `NodeJS ${process.version}`; + } + } + get Canvas() { + return __privateGet(this, _canvas); + } + set Canvas(val) { + __privateSet(this, _canvas, val); + globalThis.Canvas = val; + } + get Image() { + return __privateGet(this, _image); + } + set Image(val) { + __privateSet(this, _image, val); + globalThis.Image = val; + } + get ImageData() { + return __privateGet(this, _imageData); + } + set ImageData(val) { + __privateSet(this, _imageData, val); + globalThis.ImageData = val; + } + async updateBackend() { + this.backends = Object.keys(tfjs_esm_exports.engine().registryFactory); + try { + this.tensorflow = { + version: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].TF_Version : void 0, + gpu: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].isUsingGpuDevice() : void 0 + }; + } catch (e) { + } + this.wasm.supported = typeof WebAssembly !== "undefined"; + this.wasm.backend = this.backends.includes("wasm"); + if (this.wasm.supported && this.wasm.backend) { + this.wasm.simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + } + const c = canvas(100, 100); + const gl = c ? c.getContext("webgl2") : void 0; + this.webgl.supported = typeof gl !== "undefined"; + this.webgl.backend = this.backends.includes("webgl"); + if (this.webgl.supported && this.webgl.backend && gl) { + this.webgl.version = gl.getParameter(gl.VERSION); + this.webgl.vendor = gl.getParameter(gl.VENDOR); + this.webgl.renderer = gl.getParameter(gl.RENDERER); + this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); + } + this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; + this.webgpu.backend = this.backends.includes("webgpu"); + try { + if (this.webgpu.supported) { + const adapter = await navigator.gpu.requestAdapter(); + this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); + } + } catch (e) { + this.webgpu.supported = false; + } + try { + this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); + } catch (e) { + } + } + updateCPU() { + const cpu = { model: "", flags: [] }; + if (this.node && this.platform.startsWith("linux")) { + } + if (!this.cpu) + Object.defineProperty(this, "cpu", { value: cpu }); + else + this.cpu = cpu; + } +}; +_canvas = new WeakMap(); +_image = new WeakMap(); +_imageData = new WeakMap(); +var env = new Env(); + +// src/util/webcam.ts +var WebCam = class { + constructor() { + __publicField(this, "config"); + __publicField(this, "element"); + __publicField(this, "stream"); + __publicField(this, "devices", []); + __publicField(this, "enumerate", async () => { + try { + const devices = await navigator.mediaDevices.enumerateDevices(); + this.devices = devices.filter((device) => device.kind === "videoinput"); + } catch (e) { + this.devices = []; + } + return this.devices; + }); + __publicField(this, "start", async (webcamConfig) => { + var _a, _b; + if (webcamConfig == null ? void 0 : webcamConfig.debug) + this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; + if (webcamConfig == null ? void 0 : webcamConfig.crop) + this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; + if (webcamConfig == null ? void 0 : webcamConfig.mode) + this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; + if (webcamConfig == null ? void 0 : webcamConfig.width) + this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; + if (webcamConfig == null ? void 0 : webcamConfig.height) + this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; + if (webcamConfig == null ? void 0 : webcamConfig.id) + this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; + if (webcamConfig == null ? void 0 : webcamConfig.element) { + if (typeof webcamConfig.element === "string") { + const el = document.getElementById(webcamConfig.element); + if (el && el instanceof HTMLVideoElement) { + this.element = el; + } else { + if (this.config.debug) + log("webcam", "cannot get dom element", webcamConfig.element); + return; + } + } else if (webcamConfig.element instanceof HTMLVideoElement) { + this.element = webcamConfig.element; + } else { + if (this.config.debug) + log("webcam", "unknown dom element", webcamConfig.element); + return; + } + } else { + this.element = document.createElement("video"); + } + const requestedConstraints = { + audio: false, + video: { + facingMode: this.config.mode === "front" ? "user" : "environment", + resizeMode: this.config.crop ? "crop-and-scale" : "none" + } + }; + if (((_a = this.config) == null ? void 0 : _a.width) > 0) + requestedConstraints.video.width = { ideal: this.config.width }; + if (((_b = this.config) == null ? void 0 : _b.height) > 0) + requestedConstraints.video.height = { ideal: this.config.height }; + if (this.config.id) + requestedConstraints.video.deviceId = this.config.id; + this.element.addEventListener("play", () => { + if (this.config.debug) + log("webcam", "play"); + }); + this.element.addEventListener("pause", () => { + if (this.config.debug) + log("webcam", "pause"); + }); + this.element.addEventListener("click", async () => { + if (!this.element || !this.stream) + return; + if (this.element.paused) + await this.element.play(); + else + this.element.pause(); + }); + if (!(navigator == null ? void 0 : navigator.mediaDevices)) { + if (this.config.debug) + log("webcam", "no devices"); + return; + } + try { + this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); + } catch (err) { + log("webcam", err); + return; + } + if (!this.stream) { + if (this.config.debug) + log("webcam", "no stream"); + return; + } + this.element.srcObject = this.stream; + const ready3 = new Promise((resolve) => { + if (!this.element) + resolve(false); + else + this.element.onloadeddata = () => resolve(true); + }); + await ready3; + await this.element.play(); + if (this.config.debug) { + log("webcam", { + width: this.width, + height: this.height, + label: this.label, + stream: this.stream, + track: this.track, + settings: this.settings, + constraints: this.constraints, + capabilities: this.capabilities + }); + } + }); + __publicField(this, "pause", () => { + if (this.element) + this.element.pause(); + }); + __publicField(this, "play", async () => { + if (this.element) + await this.element.play(); + }); + __publicField(this, "stop", () => { + if (this.config.debug) + log("webcam", "stop"); + if (this.track) + this.track.stop(); + }); + this.config = { + element: void 0, + debug: true, + mode: "front", + crop: false, + width: 0, + height: 0 + }; + } + get track() { + if (!this.stream) + return void 0; + return this.stream.getVideoTracks()[0]; + } + get capabilities() { + if (!this.track) + return void 0; + return this.track.getCapabilities ? this.track.getCapabilities() : void 0; + } + get constraints() { + if (!this.track) + return void 0; + return this.track.getConstraints ? this.track.getConstraints() : void 0; + } + get settings() { + if (!this.stream) + return void 0; + const track = this.stream.getVideoTracks()[0]; + return track.getSettings ? track.getSettings() : void 0; + } + get label() { + if (!this.track) + return ""; + return this.track.label; + } + get paused() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.paused) || false; + } + get width() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; + } + get height() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; + } +}; + +// models/models.json +var models_exports = {}; +__export(models_exports, { + age: () => age, + "anti-spoofing": () => anti_spoofing, + antispoof: () => antispoof, + blazeface: () => blazeface, + "blazeface-back": () => blazeface_back, + "blazeface-front": () => blazeface_front, + "blazepose-detector": () => blazepose_detector, + "blazepose-full": () => blazepose_full, + "blazepose-heavy": () => blazepose_heavy, + "blazepose-lite": () => blazepose_lite, + centernet: () => centernet, + default: () => models_default, + efficientpose: () => efficientpose, + "efficientpose-i-lite": () => efficientpose_i_lite, + "efficientpose-ii-lite": () => efficientpose_ii_lite, + "efficientpose-iv": () => efficientpose_iv, + emotion: () => emotion, + faceboxes: () => faceboxes, + facemesh: () => facemesh, + "facemesh-attention": () => facemesh_attention, + "facemesh-attention-pinto": () => facemesh_attention_pinto, + "facemesh-detection-full": () => facemesh_detection_full, + "facemesh-detection-short": () => facemesh_detection_short, + faceres: () => faceres, + "faceres-deep": () => faceres_deep, + gear: () => gear, + gender: () => gender, + "gender-ssrnet-imdb": () => gender_ssrnet_imdb, + handdetect: () => handdetect, + "handlandmark-full": () => handlandmark_full, + "handlandmark-lite": () => handlandmark_lite, + "handlandmark-sparse": () => handlandmark_sparse, + handskeleton: () => handskeleton, + handtrack: () => handtrack, + "insightface-efficientnet-b0": () => insightface_efficientnet_b0, + "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, + "insightface-mobilenet-emore": () => insightface_mobilenet_emore, + "insightface-mobilenet-swish": () => insightface_mobilenet_swish, + iris: () => iris, + liveness: () => liveness, + meet: () => meet, + mobileface: () => mobileface, + mobilefacenet: () => mobilefacenet, + models: () => models, + "movenet-lightning": () => movenet_lightning, + "movenet-multipose": () => movenet_multipose, + "movenet-thunder": () => movenet_thunder, + nanodet: () => nanodet, + "nanodet-e": () => nanodet_e, + "nanodet-g": () => nanodet_g, + "nanodet-m": () => nanodet_m, + "nanodet-t": () => nanodet_t, + posenet: () => posenet, + rvm: () => rvm, + selfie: () => selfie +}); +var antispoof = 853098; +var blazeface = 538928; +var centernet = 4030290; +var emotion = 820516; +var facemesh = 1477958; +var faceres = 6978814; +var handlandmark_lite = 2023432; +var handtrack = 2964837; +var iris = 2599092; +var liveness = 592976; +var models = 0; +var movenet_lightning = 4650216; +var age = 161240; +var blazeface_back = 538928; +var blazeface_front = 402048; +var blazepose_detector = 5928856; +var blazepose_full = 6339202; +var blazepose_heavy = 27502466; +var blazepose_lite = 2726402; +var efficientpose = 5651240; +var faceboxes = 2013002; +var facemesh_attention_pinto = 2387598; +var facemesh_attention = 2382414; +var facemesh_detection_full = 1026192; +var facemesh_detection_short = 201268; +var faceres_deep = 13957620; +var gear = 1498916; +var gender_ssrnet_imdb = 161236; +var gender = 201808; +var handdetect = 3515612; +var handlandmark_full = 5431368; +var handlandmark_sparse = 5286322; +var handskeleton = 5502280; +var meet = 372228; +var mobileface = 2183192; +var mobilefacenet = 5171976; +var movenet_multipose = 9448838; +var movenet_thunder = 12477112; +var nanodet = 7574558; +var posenet = 5032780; +var rvm = 3739355; +var selfie = 212886; +var anti_spoofing = 853098; +var efficientpose_i_lite = 2269064; +var efficientpose_ii_lite = 5651240; +var efficientpose_iv = 25643252; +var insightface_efficientnet_b0 = 13013224; +var insightface_ghostnet_strides1 = 8093408; +var insightface_ghostnet_strides2 = 8049584; +var insightface_mobilenet_emore = 6938536; +var insightface_mobilenet_swish = 12168584; +var nanodet_e = 12319156; +var nanodet_g = 7574558; +var nanodet_m = 1887474; +var nanodet_t = 5294216; +var models_default = { + antispoof, + blazeface, + centernet, + emotion, + facemesh, + faceres, + "handlandmark-lite": handlandmark_lite, + handtrack, + iris, + liveness, + models, + "movenet-lightning": movenet_lightning, + age, + "blazeface-back": blazeface_back, + "blazeface-front": blazeface_front, + "blazepose-detector": blazepose_detector, + "blazepose-full": blazepose_full, + "blazepose-heavy": blazepose_heavy, + "blazepose-lite": blazepose_lite, + efficientpose, + faceboxes, + "facemesh-attention-pinto": facemesh_attention_pinto, + "facemesh-attention": facemesh_attention, + "facemesh-detection-full": facemesh_detection_full, + "facemesh-detection-short": facemesh_detection_short, + "faceres-deep": faceres_deep, + gear, + "gender-ssrnet-imdb": gender_ssrnet_imdb, + gender, + handdetect, + "handlandmark-full": handlandmark_full, + "handlandmark-sparse": handlandmark_sparse, + handskeleton, + meet, + mobileface, + mobilefacenet, + "movenet-multipose": movenet_multipose, + "movenet-thunder": movenet_thunder, + nanodet, + posenet, + rvm, + selfie, + "anti-spoofing": anti_spoofing, + "efficientpose-i-lite": efficientpose_i_lite, + "efficientpose-ii-lite": efficientpose_ii_lite, + "efficientpose-iv": efficientpose_iv, + "insightface-efficientnet-b0": insightface_efficientnet_b0, + "insightface-ghostnet-strides1": insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": insightface_ghostnet_strides2, + "insightface-mobilenet-emore": insightface_mobilenet_emore, + "insightface-mobilenet-swish": insightface_mobilenet_swish, + "nanodet-e": nanodet_e, + "nanodet-g": nanodet_g, + "nanodet-m": nanodet_m, + "nanodet-t": nanodet_t +}; + +// src/tfjs/load.ts +var options = { + cacheModels: true, + cacheSupported: true, + verbose: true, + debug: false, + modelBasePath: "" +}; +var modelStats = {}; +async function httpHandler(url, init4) { + if (options.debug) + log("load model fetch:", url, init4); + return fetch(url, init4); +} +function setModelLoadOptions(config3) { + options.cacheModels = config3.cacheModels; + options.verbose = config3.debug; + options.modelBasePath = config3.modelBasePath; +} +async function loadModel(modelPath) { + var _a, _b, _c, _d; + let modelUrl = join(options.modelBasePath, modelPath || ""); + if (!modelUrl.toLowerCase().endsWith(".json")) + modelUrl += ".json"; + const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); + const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); + const cachedModelName = "indexeddb://" + shortModelName; + modelStats[shortModelName] = { + name: shortModelName, + sizeFromManifest: 0, + sizeLoadedWeights: 0, + sizeDesired: models_exports[shortModelName], + inCache: false, + url: "" + }; + options.cacheSupported = typeof indexedDB !== "undefined"; + let cachedModels = {}; + try { + cachedModels = options.cacheSupported && options.cacheModels ? await tfjs_esm_exports.io.listModels() : {}; + } catch (e) { + options.cacheSupported = false; + } + modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); + modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; + const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; + let model23 = new tfjs_esm_exports.GraphModel(modelStats[shortModelName].url, tfLoadOptions); + let loaded = false; + try { + model23.findIOHandler(); + if (options.debug) + log("model load handler:", model23["handler"]); + } catch (err) { + log("error finding model i/o handler:", modelUrl, err); + } + try { + const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; + modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; + if (artifacts) + model23.loadSync(artifacts); + else + model23 = await tfjs_esm_exports.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; + if (options.verbose) + log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); + loaded = true; + } catch (err) { + log("error loading model:", modelUrl, err); + } + if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { + try { + const saveResult = await model23.save(cachedModelName); + if (options.debug) + log("model saved:", cachedModelName, saveResult); + } catch (err) { + log("error saving model:", modelUrl, err); + } + } + return model23; +} + +// package.json +var version8 = "3.0.2"; + +// src/tfjs/humangl.ts +var config2 = { + name: "humangl", + priority: 999, + canvas: null, + gl: null, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +function register(instance) { + var _a; + if (instance.config.backend !== "humangl") + return; + if (config2.name in tfjs_esm_exports.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { + log("humangl error: backend invalid context"); + instance.models.reset(); + } + if (!tfjs_esm_exports.findBackend(config2.name)) { + try { + config2.canvas = canvas(100, 100); + } catch (err) { + log("humangl error: cannot create canvas:", err); + return; + } + try { + config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); + if (!config2.gl) { + log("humangl error: cannot get webgl context"); + return; + } + const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); + if (!glv2) { + log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); + instance.config.backend = "webgl"; + return; + } + if (config2.canvas) { + config2.canvas.addEventListener("webglcontextlost", (e) => { + log("humangl error:", e.type); + log("possible browser memory leak using webgl or conflict with multiple backend registrations"); + instance.emit("error"); + throw new Error("backend error: webgl context lost"); + }); + config2.canvas.addEventListener("webglcontextrestored", (e) => { + log("humangl error: context restored:", e); + }); + config2.canvas.addEventListener("webglcontextcreationerror", (e) => { + log("humangl error: context create:", e); + }); + } + } catch (err) { + log("humangl error: cannot get webgl context:", err); + return; + } + try { + tfjs_esm_exports.setWebGLContext(2, config2.gl); + } catch (err) { + log("humangl error: cannot set webgl context:", err); + return; + } + try { + const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl); + tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("humangl error: cannot register webgl backend:", err); + return; + } + try { + const kernels = tfjs_esm_exports.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tfjs_esm_exports.registerKernel(newKernelConfig); + }); + } catch (err) { + log("humangl error: cannot update webgl backend registration:", err); + return; + } + try { + if (tfjs_esm_exports.env().flagRegistry.WEBGL_VERSION) + tfjs_esm_exports.env().set("WEBGL_VERSION", 2); + } catch (err) { + log("humangl error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + const backend4 = tfjs_esm_exports.backend(); + const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; + if (current) { + if (instance.config.debug) + log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); + } else { + log("humangl error: no current gl context:", current, config2.gl); + } + } +} + +// src/tfjs/constants.ts +var constants = { + tf255: 255, + tf1: 1, + tf2: 2, + tf05: 0.5, + tf127: 127.5, + rgb: [0.2989, 0.587, 0.114] +}; +function init() { + constants.tf255 = tfjs_esm_exports.scalar(255, "float32"); + constants.tf1 = tfjs_esm_exports.scalar(1, "float32"); + constants.tf2 = tfjs_esm_exports.scalar(2, "float32"); + constants.tf05 = tfjs_esm_exports.scalar(0.5, "float32"); + constants.tf127 = tfjs_esm_exports.scalar(127.5, "float32"); + constants.rgb = tfjs_esm_exports.tensor1d([0.2989, 0.587, 0.114], "float32"); +} + +// src/tfjs/backend.ts +async function getBestBackend() { + var _a; + await env.updateBackend(); + if ((_a = env.tensorflow) == null ? void 0 : _a.version) + return "tensorflow"; + if (env.webgpu.supported && env.webgpu.backend) + return "webgpu"; + if (env.webgl.supported && env.webgl.backend) + return "webgl"; + if (env.wasm.supported && env.wasm.backend) + return "wasm"; + return "cpu"; +} +function registerCustomOps(config3) { + const newKernels = []; + if (!env.kernels.includes("mod")) { + const kernelMod = { + kernelName: "Mod", + backendName: tfjs_esm_exports.getBackend(), + kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b))) + }; + tfjs_esm_exports.registerKernel(kernelMod); + env.kernels.push("mod"); + newKernels.push("mod"); + } + if (!env.kernels.includes("floormod")) { + const kernelFloorMod = { + kernelName: "FloorMod", + backendName: tfjs_esm_exports.getBackend(), + kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tfjs_esm_exports.mod(op.inputs.a, op.inputs.b))) + }; + tfjs_esm_exports.registerKernel(kernelFloorMod); + env.kernels.push("floormod"); + newKernels.push("floormod"); + } + if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { + const kernelRotateWithOffset = { + kernelName: "RotateWithOffset", + backendName: tfjs_esm_exports.getBackend(), + kernelFunc: (op) => tfjs_esm_exports.tidy(() => { + const backend4 = tfjs_esm_exports.getBackend(); + tfjs_esm_exports.setBackend("cpu"); + const t2 = tfjs_esm_exports.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); + tfjs_esm_exports.setBackend(backend4); + return t2; + }) + }; + tfjs_esm_exports.registerKernel(kernelRotateWithOffset); + env.kernels.push("rotatewithoffset"); + newKernels.push("rotatewithoffset"); + } + if (newKernels.length > 0 && config3.debug) + log("registered kernels:", newKernels); +} +var defaultFlags = {}; +async function check(instance, force = false) { + var _a; + instance.state = "backend"; + if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) + instance.config.backend = await getBestBackend(); + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance.config.backend) { + const timeStamp = now(); + if (instance.config.backend && instance.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { + if (instance.config.debug) + log("running inside web worker"); + } + if (env.browser && instance.config.backend === "tensorflow") { + if (instance.config.debug) + log("override: backend set to tensorflow while running in browser"); + instance.config.backend = "webgl"; + } + if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { + if (instance.config.debug) + log(`override: backend set to ${instance.config.backend} while running in nodejs`); + instance.config.backend = "tensorflow"; + } + if (env.browser && instance.config.backend === "webgpu") { + if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { + log("override: backend set to webgpu but browser does not support webgpu"); + instance.config.backend = "webgl"; + } else { + const adapter = await navigator.gpu.requestAdapter(); + if (instance.config.debug) + log("enumerated webgpu adapter:", adapter); + if (!adapter) { + log("override: backend set to webgpu but browser reports no available gpu"); + instance.config.backend = "webgl"; + } else { + const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; + log("webgpu adapter info:", adapterInfo); + } + } + } + let available = Object.keys(tfjs_esm_exports.engine().registryFactory); + if (instance.config.backend === "humangl" && !available.includes("humangl")) { + register(instance); + available = Object.keys(tfjs_esm_exports.engine().registryFactory); + } + if (instance.config.debug) + log("available backends:", available); + if (!available.includes(instance.config.backend)) { + log(`error: backend ${instance.config.backend} not found in registry`); + instance.config.backend = env.node ? "tensorflow" : "webgl"; + if (instance.config.debug) + log(`override: setting backend ${instance.config.backend}`); + } + if (instance.config.debug) + log("setting backend:", [instance.config.backend]); + if (instance.config.backend === "wasm") { + if (tfjs_esm_exports.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) + tfjs_esm_exports.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); + if (instance.config.debug) + log("wasm path:", instance.config.wasmPath); + if (typeof tfjs_esm_exports.setWasmPaths !== "undefined") + tfjs_esm_exports.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); + else + throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); + let mt = false; + let simd = false; + try { + mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + if (instance.config.debug) + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (instance.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } catch (e) { + log("wasm detection failed"); + } + } + try { + await tfjs_esm_exports.setBackend(instance.config.backend); + await tfjs_esm_exports.ready(); + } catch (err) { + log("error: cannot set backend:", instance.config.backend, err); + return false; + } + if (instance.config.debug) + defaultFlags = JSON.parse(JSON.stringify(tfjs_esm_exports.env().flags)); + } + if (tfjs_esm_exports.getBackend() === "humangl" || tfjs_esm_exports.getBackend() === "webgl") { + if (tfjs_esm_exports.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + tfjs_esm_exports.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (tfjs_esm_exports.env().flagRegistry.WEBGL_EXP_CONV) + tfjs_esm_exports.env().set("WEBGL_EXP_CONV", true); + if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + tfjs_esm_exports.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + } + if (tfjs_esm_exports.getBackend() === "webgpu") { + } + if (instance.config.debug) { + const newFlags = tfjs_esm_exports.env().flags; + const updatedFlags = {}; + for (const key of Object.keys(newFlags)) { + if (defaultFlags[key] === newFlags[key]) + continue; + updatedFlags[key] = newFlags[key]; + } + if (instance.config.debug && Object.keys(updatedFlags).length > 0) + log("backend:", tfjs_esm_exports.getBackend(), "flags:", updatedFlags); + } + if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { + if (instance.config.debug) + log("flags:", instance.config["flags"]); + for (const [key, val] of Object.entries(instance.config.flags)) { + tfjs_esm_exports.env().set(key, val); + } + } + tfjs_esm_exports.enableProdMode(); + init(); + instance.performance.initBackend = Math.trunc(now() - timeStamp); + instance.config.backend = tfjs_esm_exports.getBackend(); + await env.updateBackend(); + registerCustomOps(instance.config); + env.initial = false; + } + return true; +} +function fakeOps(kernelNames, config3) { + for (const kernelName of kernelNames) { + const kernelConfig = { + kernelName, + backendName: config3.backend, + kernelFunc: (param) => { + var _a; + if (config3.debug) + log("kernelFunc", kernelName, config3.backend, param); + return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; + } + }; + tfjs_esm_exports.registerKernel(kernelConfig); + } + env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); +} + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body, + canvas: () => canvas2, + face: () => face, + gesture: () => gesture, + hand: () => hand, + init: () => init2, + object: () => object, + options: () => options2, + person: () => person +}); + +// src/draw/primitives.ts +var getCanvasContext = (input) => { + if (!input) + log("draw error: invalid canvas"); + else if (!input.getContext) + log("draw error: canvas context not defined"); + else { + const ctx = input.getContext("2d"); + if (!ctx) + log("draw error: cannot get canvas context"); + else + return ctx; + } + return null; +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); +var colorDepth = (z, opt) => { + if (!opt.useDepth || typeof z === "undefined") + return opt.color; + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); + return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; +}; +function labels(ctx, str, startX, startY, localOptions2) { + const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); + const x = Math.max(0, startX); + for (let i = line.length - 1; i >= 0; i--) { + const y = i * localOptions2.lineHeight + startY; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(line[i], x + 5, y + 16); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(line[i], x + 4, y + 15); + } +} +function point(ctx, x, y, z, localOptions2) { + ctx.fillStyle = colorDepth(z, localOptions2); + ctx.beginPath(); + ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions2) { + ctx.beginPath(); + ctx.lineWidth = localOptions2.lineWidth; + if (localOptions2.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.moveTo(x + localOptions2.roundRect, y); + ctx.lineTo(x + width - localOptions2.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); + ctx.lineTo(x + width, y + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); + ctx.lineTo(x + localOptions2.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); + ctx.lineTo(x, y + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); + ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); + } + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.lineWidth = localOptions2.lineWidth; + if (!localOptions2.useCurves || points.length <= 2) { + lines(ctx, points, localOptions2); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function arrow(ctx, from, to, radius = 5) { + let angle; + let x; + let y; + ctx.beginPath(); + ctx.moveTo(from[0], from[1]); + ctx.lineTo(to[0], to[1]); + angle = Math.atan2(to[1] - from[1], to[0] - from[0]); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + ctx.closePath(); + ctx.stroke(); + ctx.fill(); +} + +// src/draw/options.ts +var options2 = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + alpha: 0.5, + font: 'small-caps 16px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawAttention: true, + drawGestures: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false, + faceLabels: "", + bodyLabels: "", + bodyPartLabels: "", + objectLabels: "", + handLabels: "", + fingerLabels: "", + gestureLabels: "" +}; + +// src/face/facemeshcoords.ts +var meshAnnotations = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], + lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], + lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], + lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], + lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisIndices = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, + { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, + { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, + { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, + { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, + { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, + { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, + { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } +]; +var UV468 = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + [0.289712011814117, 0.380764007568359], + [0.499954998493195, 0.312398016452789], + [0.499987006187439, 0.269918978214264], + [0.500023007392883, 0.107050001621246], + [0.500023007392883, 0.666234016418457], + [0.5000159740448, 0.679224014282227], + [0.500023007392883, 0.692348003387451], + [0.499976992607117, 0.695277988910675], + [0.499976992607117, 0.70593398809433], + [0.499976992607117, 0.719385027885437], + [0.499976992607117, 0.737019002437592], + [0.499967992305756, 0.781370997428894], + [0.499816000461578, 0.562981009483337], + [0.473773002624512, 0.573909997940063], + [0.104906998574734, 0.254140973091125], + [0.365929991006851, 0.409575998783112], + [0.338757991790771, 0.41302502155304], + [0.311120003461838, 0.409460008144379], + [0.274657994508743, 0.389131009578705], + [0.393361985683441, 0.403706014156342], + [0.345234006643295, 0.344011008739471], 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256, + 341, + 452, + 414, + 413, + 463, + 286, + 441, + 414, + 286, + 258, + 441, + 258, + 257, + 442, + 257, + 259, + 443, + 259, + 260, + 444, + 260, + 467, + 445, + 309, + 459, + 250, + 305, + 289, + 290, + 305, + 290, + 460, + 401, + 376, + 435, + 309, + 250, + 392, + 376, + 411, + 433, + 453, + 341, + 464, + 357, + 453, + 465, + 343, + 357, + 412, + 437, + 343, + 399, + 344, + 360, + 440, + 420, + 437, + 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); +function connectionsToIndices(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var pairsLips = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var pairsFaceContour = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +var contourKeypoints = { + lips: connectionsToIndices(pairsLips), + leftEye: connectionsToIndices(pairsLeftEye), + leftEyebrow: connectionsToIndices(pairsLeftEyebrow), + leftIris: connectionsToIndices(pairsLeftIris), + rightEye: connectionsToIndices(pairsRightEye), + rightEyebrow: connectionsToIndices(pairsRightEyebrow), + rightIris: connectionsToIndices(pairsRightIris), + faceOval: connectionsToIndices(pairsFaceContour) +}; + +// src/face/constants.ts +var LIPS_CONNECTIONS = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var FACE_OVAL_CONNECTIONS = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +function connectionsToIndices2(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { + lips: connectionsToIndices2(LIPS_CONNECTIONS), + leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), + leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), + leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), + rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), + rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), + rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), + faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) +}; +var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); +var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); +var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ + 61, + 146, + 91, + 181, + 84, + 17, + 314, + 405, + 321, + 375, + 291, + 185, + 40, + 39, + 37, + 0, + 267, + 269, + 270, + 409, + 78, + 95, + 88, + 178, + 87, + 14, + 317, + 402, + 318, + 324, + 308, + 191, + 80, + 81, + 82, + 13, + 312, + 311, + 310, + 415, + 76, + 77, + 90, + 180, + 85, + 16, + 315, + 404, + 320, + 307, + 306, + 184, + 74, + 73, + 72, + 11, + 302, + 303, + 304, + 408, + 62, + 96, + 89, + 179, + 86, + 15, + 316, + 403, + 319, + 325, + 292, + 183, + 42, + 41, + 38, + 12, + 268, + 271, + 272, + 407 +]; +var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ + 33, + 7, + 163, + 144, + 145, + 153, + 154, + 155, + 133, + 246, + 161, + 160, + 159, + 158, + 157, + 173, + 130, + 25, + 110, + 24, + 23, + 22, + 26, + 112, + 243, + 247, + 30, + 29, + 27, + 28, + 56, + 190, + 226, + 31, + 228, + 229, + 230, + 231, + 232, + 233, + 244, + 113, + 225, + 224, + 223, + 222, + 221, + 189, + 35, + 124, + 46, + 53, + 52, + 65, + 143, + 111, + 117, + 118, + 119, + 120, + 121, + 128, + 245, + 156, + 70, + 63, + 105, + 66, + 107, + 55, + 193 +]; +var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ + 263, + 249, + 390, + 373, + 374, + 380, + 381, + 382, + 362, + 466, + 388, + 387, + 386, + 385, + 384, + 398, + 359, + 255, + 339, + 254, + 253, + 252, + 256, + 341, + 463, + 467, + 260, + 259, + 257, + 258, + 286, + 414, + 446, + 261, + 448, + 449, + 450, + 451, + 452, + 453, + 464, + 342, + 445, + 444, + 443, + 442, + 441, + 413, + 265, + 353, + 276, + 283, + 282, + 295, + 372, + 340, + 346, + 347, + 348, + 349, + 350, + 357, + 465, + 383, + 300, + 293, + 334, + 296, + 336, + 285, + 417 +]; + +// src/draw/face.ts +var localOptions; +function drawLabels(f, ctx) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i; + if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) + return; + let l = localOptions.faceLabels.slice(); + if (f.score) + l = replace(l, "[score]", 100 * f.score); + if (f.gender) + l = replace(l, "[gender]", f.gender); + if (f.genderScore) + l = replace(l, "[genderScore]", 100 * f.genderScore); + if (f.age) + l = replace(l, "[age]", f.age); + if (f.distance) + l = replace(l, "[distance]", 100 * f.distance); + if (f.real) + l = replace(l, "[real]", 100 * f.real); + if (f.live) + l = replace(l, "[live]", 100 * f.live); + if (f.emotion && f.emotion.length > 0) { + const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion2.length > 3) + emotion2.length = 3; + l = replace(l, "[emotions]", emotion2.join(" ")); + } + if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) + l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); + if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) + l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); + if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) + l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); + if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) + l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); + labels(ctx, l, f.box[0], f.box[1], localOptions); +} +function drawIrisElipse(f, ctx) { + var _a, _b, _c, _d; + if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } +} +function drawGazeSpheres(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") { + ctx.strokeStyle = "pink"; + const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90; + const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90; + const pathV = new Path2D(` + M ${f.box[0] + f.box[2] / 2} ${f.box[1]} C - ${o} ${e.box[1]}, - ${o} ${e.box[1]+e.box[3]}, - ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} - `),a=new Path2D(` - M ${e.box[0]} ${e.box[1]+e.box[3]/2} + ${valX} ${f.box[1]}, + ${valX} ${f.box[1] + f.box[3]}, + ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} + `); + const pathH = new Path2D(` + M ${f.box[0]} ${f.box[1] + f.box[3] / 2} C - ${e.box[0]} ${s}, - ${e.box[0]+e.box[2]} ${s}, - ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(a),t.stroke(A)}}function Wr(e,t){var n;if(G.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];yt(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let s=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];yt(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[s[0],s[1]],4)}}function Dr(e,t){if(G.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;ne.mesh[s]);xt(t,o,G)}Or(e,t)}}function Fr(e,t){if(G.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Fr(s,o),Dr(s,o),Cr(s,o),Wr(s,o))}}function b2(e,t,n){var A,a;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round";for(let i=0;i0)){let c=o.bodyLabels.slice();c=V(c,"[score]",100*t[i].score),O0(s,c,t[i].box[0],t[i].box[1],o)}if(o.drawPoints&&t[i].keypoints)for(let c=0;c0&&t[i].keypoints){s.font=o.font;for(let c of t[i].keypoints){if(!c.score||c.score===0)continue;let d=o.bodyPartLabels.slice();d=V(d,"[label]",c.part),d=V(d,"[score]",100*c.score),O0(s,d,c.position[0],c.position[1],o)}}if(o.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let c of Object.values(t[i].annotations))for(let d of c)o1(s,d,o)}}}function g2(e,t,n){var A,a;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let i of t){if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,X0(s,i.box[0],i.box[1],i.box[2],i.box[3],o),o.drawLabels&&((A=o.handLabels)==null?void 0:A.length)>0){let c=o.handLabels.slice();c=V(c,"[label]",i.label),c=V(c,"[score]",100*i.score),O0(s,c,i.box[0],i.box[1],o)}s.stroke()}if(o.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let c of i.keypoints)s.fillStyle=ge(c[2],o),K0(s,c[0],c[1],0,o);if(o.drawLabels&&i.annotations&&((a=o.fingerLabels)==null?void 0:a.length)>0)for(let[c,d]of Object.entries(i.annotations)){let y=o.fingerLabels.slice();y=V(y,"[label]",c),O0(s,y,d[d.length-1][0],d[d.length-1][1],o)}if(o.drawPolygons&&i.annotations){let c=d=>{if(!(!d||d.length===0||!d[0]))for(let y=0;y0?y-1:0][0],d[y>0?y-1:0][1]),s.lineTo(d[y][0],d[y][1]),s.stroke()}};s.lineWidth=o.lineWidth,c(i.annotations.index),c(i.annotations.middle),c(i.annotations.ring),c(i.annotations.pinky),c(i.annotations.thumb)}}}}function T2(e,t,n){var A;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let a of t)if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,X0(s,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((A=o.objectLabels)==null?void 0:A.length)>0){let i=o.objectLabels.slice();i=V(i,"[label]",a.label),i=V(i,"[score]",100*a.score),O0(s,i,a.box[0],a.box[1],o)}s.stroke()}}}function v2(e,t,n){var s;let o=Q(e0,n);if(!(!t||!e)&&o.drawGestures&&((s=o.gestureLabels)==null?void 0:s.length)>0){let A=S0(e);if(!A)return;A.font=o.font,A.fillStyle=o.color;let a=1;for(let i=0;i1&&d[1].length>0){let y=c[1]>0?`#${c[1]}`:"",l=o.gestureLabels.slice();l=V(l,"[where]",c[0]),l=V(l,"[who]",y),l=V(l,"[what]",d[1]),O0(A,l,8,2+a*o.lineHeight,o),a+=1}}}}var Ae={face:`face + ${f.box[0]} ${valY}, + ${f.box[0] + f.box[2]} ${valY}, + ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} + `); + ctx.stroke(pathH); + ctx.stroke(pathV); + } +} +function drawGazeArrows(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { + ctx.strokeStyle = "pink"; + ctx.fillStyle = "pink"; + const leftGaze = [ + f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); + const rightGaze = [ + f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); + } +} +function drawFacePolygons(f, ctx) { + if (localOptions.drawPolygons && f.mesh.length >= 468) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); + lines(ctx, points, localOptions); + } + drawIrisElipse(f, ctx); + } +} +function drawFacePoints(f, ctx) { + if (localOptions.drawPoints && f.mesh.length >= 468) { + for (let i = 0; i < f.mesh.length; i++) { + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); + if (localOptions.drawAttention) { + if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); + if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + } + } + } +} +function drawFaceBoxes(f, ctx) { + if (localOptions.drawBoxes) { + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); + } +} +function face(inCanvas2, result, drawOptions) { + localOptions = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + for (const f of result) { + drawFaceBoxes(f, ctx); + drawLabels(f, ctx); + if (f.mesh && f.mesh.length > 0) { + drawFacePoints(f, ctx); + drawFacePolygons(f, ctx); + drawGazeSpheres(f, ctx); + drawGazeArrows(f, ctx); + } + } +} + +// src/draw/body.ts +function body(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + ctx.lineWidth = localOptions2.lineWidth; + ctx.font = localOptions2.font; + if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.bodyLabels.slice(); + l = replace(l, "[score]", 100 * result[i].score); + labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); + } + } + if (localOptions2.drawPoints && result[i].keypoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) + continue; + ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); + } + } + if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { + ctx.font = localOptions2.font; + for (const pt of result[i].keypoints) { + if (!pt.score || pt.score === 0) + continue; + let l = localOptions2.bodyPartLabels.slice(); + l = replace(l, "[label]", pt.part); + l = replace(l, "[score]", 100 * pt.score); + labels(ctx, l, pt.position[0], pt.position[1], localOptions2); + } + } + if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { + for (const part of Object.values(result[i].annotations)) { + for (const connected4 of part) + curves(ctx, connected4, localOptions2); + } + } + } +} + +// src/draw/hand.ts +function hand(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.handLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + if (localOptions2.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = colorDepth(pt[2], localOptions2); + point(ctx, pt[0], pt[1], 0, localOptions2); + } + } + } + if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { + for (const [part, pt] of Object.entries(h.annotations)) { + let l = localOptions2.fingerLabels.slice(); + l = replace(l, "[label]", part); + labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); + } + } + if (localOptions2.drawPolygons && h.annotations) { + const addHandLine = (part) => { + if (!part || part.length === 0 || !part[0]) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions2); + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions2.lineWidth; + addHandLine(h.annotations.index); + addHandLine(h.annotations.middle); + addHandLine(h.annotations.ring); + addHandLine(h.annotations.pinky); + addHandLine(h.annotations.thumb); + } + } +} + +// src/draw/object.ts +function object(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.objectLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + } +} + +// src/draw/gesture.ts +function gesture(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions2.font; + ctx.fillStyle = localOptions2.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + const [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + let l = localOptions2.gestureLabels.slice(); + l = replace(l, "[where]", where[0]); + l = replace(l, "[who]", who); + l = replace(l, "[what]", what[1]); + labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); + i += 1; + } + } + } +} + +// src/draw/labels.ts +var defaultLabels = { + face: `face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -118,7 +6413,7473 @@ var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Objec live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var ut=0;function Hr(e,t,n){let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let A=0;ATt,kpt:()=>gt});var gt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Tt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var j0,Pe=224,A1,Zr=5,M2=[8,16,32,32,32];function Xr(){let e=[],t=0;for(;tn.x)),y:r.tensor1d(e.map(n=>n.y))}}async function a1(e){if(M.initial&&(j0=null),!j0&&e.body.detector&&e.body.detector.modelPath){j0=await L(e.body.detector.modelPath);let t=j0!=null&&j0.executor?Object.values(j0.modelSignature.inputs):void 0;Pe=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&j0&&h("cached model:",j0.modelUrl);return Xr(),j0}var s1=[5,5];function qr(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,Pe),t.x),s=r.add(r.div(s,Pe),t.y),A=r.mul(r.div(A,Pe),s1[0]),a=r.mul(r.div(a,Pe),s1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),d=r.add(i,A),y=r.add(c,a);return r.stack([i,c,d,y],1)})}async function Ur(e,t,n,o){var d,y;let s=[],A={};A.boxes=qr(e,A1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 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t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,Ot),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,De),t.centersNormalized=r.div(t.centers,De),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,De),t.endNormalized=r.mul(t.ends,De);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function N1(e,t){var i,c,d,y;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[ie,ie]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=B0==null?void 0:B0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let 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a=N2(I2(P1([e[n],e[o]]),is)),i=We(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[le,le]);if(A&&M.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},C1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[C0[`${n}EyeUpper0`][He.upperCenter]][2],s=e[C0[`${n}EyeLower0`][He.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function F1(e,t,n){if(!(R0!=null&&R0.executor))return e;let{box:o,boxSize:s,crop:A}=O1(e,t,Be.leftBounds[0],Be.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=O1(e,t,Be.rightBounds[0],Be.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=R0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let f=l.slice(0,He.numCoordinates*3),{rawCoords:x,iris:u}=C1(f,o,s,!0),m=l.slice(He.numCoordinates*3),{rawCoords:g,iris:P}=C1(m,a,i,!1),v=ls(e);Math.abs(v)<30?(L2(e,x,"left",null),L2(e,g,"right",null)):v<1?L2(e,x,"left",["EyeUpper0","EyeLower0"]):L2(e,g,"right",["EyeUpper0","EyeLower0"]);let p=W1(e,u,"left"),b=W1(e,P,"right");return e.concat(p).concat(b)}async function H1(e,t){var A,a,i,c,d,y,l,f,x,u;let n={lips:await((a=(A=t.filter(m=>m.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(m=>m.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(m=>m.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((f=(l=t.filter(m=>m.size===10))==null?void 0:l[1])==null?void 0:f.data()),eyeR:await((u=(x=t.filter(m=>m.size===142))==null?void 0:x[1])==null?void 0:u.data())};for(let m of Object.values(n))if(!m)return e;let o=Re.reduce((m,g)=>m+=e[g][2],0)/Re.length;for(let m=0;mm+=e[g][2],0)/Me.length;for(let m=0;mT()-q0.timestamp,o=q0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||q0.boxes.length===0?(q0.boxes=await N1(e,t),q0.timestamp=T(),q0.skipped=0):q0.skipped++;let s=[],A=[],a=0,i=n2;for(let v=0;vW.shape[W.shape.length-1]===1).data();if(k.faceScore=Math.round(100*q[0])/100,k.faceScore<(((u=t.face.detector)==null?void 0:u.minConfidence)||1)){if(p.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=S2(p,e),k.boxRaw=j2(p,e),k.score=k.boxScore,k.mesh=p.landmarks.map(W=>[(p.startPoint[0]+p.endPoint[0])/2+(p.endPoint[0]+p.startPoint[0])*W[0]/Fe(),(p.startPoint[1]+p.endPoint[1])/2+(p.endPoint[1]+p.startPoint[1])*W[1]/Fe()]),k.meshRaw=k.mesh.map(W=>[W[0]/(e.shape[2]||1),W[1]/(e.shape[1]||1),(W[2]||0)/i]);for(let W of Object.keys(Te))k.annotations[W]=[k.mesh[Te[W]]]}}else{let W=N.find(D=>D.shape[D.shape.length-1]===1404),Z=r.reshape(W,[-1,3]),J=await Z.array();r.dispose(Z),(m=t.face.attention)!=null&&m.enabled?J=await 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y;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let l={},f=I0!=null&&I0.inputs[0].shape?I0.inputs[0].shape[2]:0;l.resize=r.image.resizeBilinear(e,[f,f],!1),l.channels=r.mul(l.resize,O.rgb),l.grayscale=r.sum(l.channels,3,!0),l.grayscaleSub=r.sub(l.grayscale,O.tf05),l.grayscaleMul=r.mul(l.grayscaleSub,O.tf2),l.emotion=I0==null?void 0:I0.execute(l.grayscaleMul),U1=T();let x=await l.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*x[u])/100),emotion:ds[u]});d.sort((u,m)=>m.score-u.score),Object.keys(l).forEach(u=>r.dispose(l[u]))}O2[n]=d,q1=o,c(d)}))}var f0,ce=[],J1=0,Q1=0,Ht=Number.MAX_SAFE_INTEGER;async function _1(e){var t;return M.initial&&(f0=null),f0?e.debug&&h("cached model:",f0.modelUrl):f0=await L((t=e.face.description)==null?void 0:t.modelPath),f0}function xs(e){let t=e.image||e.tensor||e;if(!(f0!=null&&f0.inputs[0].shape))return t;let n=r.image.resizeBilinear(t,[f0.inputs[0].shape[2],f0.inputs[0].shape[1]],!1),o=r.mul(n,O.tf255);return r.dispose(n),o}async function Gt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(f0!=null&&f0.executor))return s;let A=Ht<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>T()-J1;return t.skipAllowed&&A&&a&&Q1===o&&((d=ce==null?void 0:ce[n])==null?void 0:d.age)>0&&((y=ce==null?void 0:ce[n])==null?void 0:y.genderScore)>0?(Ht++,ce[n]):(Ht=0,new Promise(async l=>{var f;if((f=t.face.description)!=null&&f.enabled){let x=xs(e),u=f0==null?void 0:f0.execute(x);J1=T(),r.dispose(x);let g=await u.find(B=>B.shape[1]===1).data(),P=Math.trunc(200*Math.abs(g[0]-.5))/100;P>(t.face.description.minConfidence||0)&&(s.gender=g[0]<=.5?"female":"male",s.genderScore=Math.min(.99,P));let v=r.argMax(u.find(B=>B.shape[1]===100),1),p=(await v.data())[0];r.dispose(v);let j=await u.find(B=>B.shape[1]===100).data();s.age=Math.round(j[p-1]>j[p+1]?10*p-100*j[p-1]:10*p+100*j[p+1])/10,(Number.isNaN(g[0])||Number.isNaN(j[0]))&&h("faceres error:",{model:f0,result:u});let k=u.find(B=>B.shape[1]===1024),N=k?await k.data():[];s.descriptor=Array.from(N),u.forEach(B=>r.dispose(B))}ce[n]=s,Q1=o,l(s)}))}var Ge=.1,Vt=.5;function ys(e,t,n){let o=!1,s=n.length-1;for(let A=0;At!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(o=!o);return o}async function e3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of C0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ge&&Ge>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ge:a.x-Ge,y:a.y>.5?a.y+Ge:a.y-Ge})));for(let a=0;aT()-n3,A=Zt<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&t3===o&&C2[n]?(Zt++,C2[n]):(Zt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0,d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[1]:0],!1),y=d0==null?void 0:d0.execute(d),l=(await y.data())[0];C2[n]=Math.round(100*l)/100,t3=o,n3=T(),r.dispose([d,y]),c(C2[n])}))}var x0,W2=[],qt=Number.MAX_SAFE_INTEGER,s3=0,A3=0;async function a3(e){var t;return M.initial&&(x0=null),x0?e.debug&&h("cached model:",x0.modelUrl):x0=await L((t=e.face.liveness)==null?void 0:t.modelPath),x0}async function Ut(e,t,n,o){var a,i;if(!(x0!=null&&x0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>T()-A3,A=qt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&s3===o&&W2[n]?(qt++,W2[n]):(qt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[2]:0,x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[1]:0],!1),y=x0==null?void 0:x0.execute(d),l=(await y.data())[0];W2[n]=Math.round(100*l)/100,s3=o,A3=T(),r.dispose([d,y]),c(W2[n])}))}var W0,Yt=[],ms=["white","black","asian","indian","other"],ps=[15,23,28,35.5,45.5,55.5,65],l3=0,c3=0,Kt=Number.MAX_SAFE_INTEGER;async function d3(e){var t;return M.initial&&(W0=null),W0?e.debug&&h("cached model:",W0.modelUrl):W0=await L((t=e.face.gear)==null?void 0:t.modelPath),W0}async function Jt(e,t,n,o){var a,i;if(!W0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=Kt<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>T()-c3;return t.skipAllowed&&A&&s&&l3===o&&Yt[n]?(Kt++,Yt[n]):(Kt=0,new Promise(async c=>{var P,v;if(!(W0!=null&&W0.inputs[0].shape))return;let d={},y=[[0,.1,.9,.9]];d.resize=r.image.cropAndResize(e,y,[0],[W0.inputs[0].shape[2],W0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(P=t.face.gear)!=null&&P.enabled&&([d.age,d.gender,d.race]=W0.execute(d.resize,["age_output","gender_output","race_output"]));let f=await d.gender.data();l.gender=f[0]>f[1]?"male":"female",l.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let x=await d.race.data();for(let p=0;p(((v=t.face.gear)==null?void 0:v.minConfidence)||.2)&&l.race.push({score:Math.round(100*x[p])/100,race:ms[p]});l.race.sort((p,b)=>b.score-p.score);let m=Array.from(await d.age.data()).map((p,b)=>[ps[b],p]).sort((p,b)=>b[1]-p[1]),g=m[0][0];for(let p=1;pr.dispose(d[p])),Yt[n]=l,l3=o,c3=T(),c(l)}))}var M0,D2=[],y3=0,f3=0,Qt=Number.MAX_SAFE_INTEGER;async function m3(e){return M.initial&&(M0=null),M0?e.debug&&h("cached model:",M0.modelUrl):M0=await L(e.face.ssrnet.modelPathAge),M0}async function _t(e,t,n,o){var a,i,c,d;if(!M0)return{age:0};let s=Qt<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>T()-f3;return t.skipAllowed&&s&&A&&y3===o&&((c=D2[n])==null?void 0:c.age)&&((d=D2[n])==null?void 0:d.age)>0?(Qt++,D2[n]):(Qt=0,new Promise(async y=>{var x;if(!(M0!=null&&M0.inputs)||!M0.inputs[0]||!M0.inputs[0].shape)return;let l={};l.resize=r.image.resizeBilinear(e,[M0.inputs[0].shape[2],M0.inputs[0].shape[1]],!1),l.enhance=r.mul(l.resize,O.tf255);let f={age:0};if((x=t.face.ssrnet)!=null&&x.enabled&&(l.age=M0.execute(l.enhance)),l.age){let u=await l.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(l).forEach(u=>r.dispose(l[u])),D2[n]=f,y3=o,f3=T(),y(f)}))}var D0,F2=[],u3=0,h3=0,$t=Number.MAX_SAFE_INTEGER,e5=[.2989,.587,.114];async function b3(e){var t;return M.initial&&(D0=null),D0?e.debug&&h("cached model:",D0.modelUrl):D0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),D0}async function t5(e,t,n,o){var a,i,c,d;if(!D0)return{gender:"unknown",genderScore:0};let s=$t<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>T()-h3;return t.skipAllowed&&s&&A&&u3===o&&((c=F2[n])==null?void 0:c.gender)&&((d=F2[n])==null?void 0:d.genderScore)>0?($t++,F2[n]):($t=0,new Promise(async y=>{var u;if(!(D0!=null&&D0.inputs[0].shape))return;let l={};l.resize=r.image.resizeBilinear(e,[D0.inputs[0].shape[2],D0.inputs[0].shape[1]],!1),l.enhance=r.tidy(()=>{let[m,g,P]=r.split(l.resize,3,3),v=r.mul(m,e5[0]),p=r.mul(g,e5[1]),b=r.mul(P,e5[2]),j=r.addN([v,p,b]);return r.mul(r.sub(j,O.tf05),2)});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(l.gender=D0.execute(l.enhance));let x=await l.gender.data();f.gender=x[0]>x[1]?"female":"male",f.genderScore=x[0]>x[1]?Math.trunc(100*x[0])/100:Math.trunc(100*x[1])/100,Object.keys(l).forEach(m=>r.dispose(l[m])),F2[n]=f,u3=o,h3=T(),y(f)}))}var P0,n5=[],T3=0,v3=0,R3=Number.MAX_SAFE_INTEGER;async function M3(e){var t;return M.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):P0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),P0}async function o5(e,t,n,o){var a,i;if(!(P0!=null&&P0.executor))return[];let s=R3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>T()-v3;return t.skipAllowed&&A&&s&&T3===o&&n5[n]?(R3++,n5[n]):new Promise(async c=>{var y;let d=[];if(((y=t.face.mobilefacenet)==null?void 0:y.enabled)&&(P0==null?void 0:P0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[P0.inputs[0].shape[2],P0.inputs[0].shape[1]],!1),l.data=P0.execute(l.crop);let f=await l.data.data();d=Array.from(f),Object.keys(l).forEach(x=>r.dispose(l[x]))}n5[n]=d,T3=o,v3=T(),c(d)})}var k0,r5=[],k3=0,w3=0,E3=Number.MAX_SAFE_INTEGER;async function z3(e){return M.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L(e.face.insightface.modelPath),k0}async function s5(e,t,n,o){var a,i;if(!(k0!=null&&k0.executor))return[];let s=E3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>T()-w3;return t.skipAllowed&&A&&s&&k3===o&&r5[n]?(E3++,r5[n]):new Promise(async c=>{var y;let 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P=Math.abs(e[a].mesh[145][1]-e[a].annotations.rightEyeIris[0][1])/e[a].box[3],v=Math.abs(e[a].mesh[374][1]-e[a].annotations.leftEyeIris[0][1])/e[a].box[3];(v<.01||P<.01||v>.022||P>.022)&&(x=!1),(v<.01||P<.01)&&t.push({iris:a,gesture:"looking down"}),(v>.022||P>.022)&&t.push({iris:a,gesture:"looking up"}),x&&t.push({iris:a,gesture:"looking center"})}return t},Z3=e=>{if(!e)return[];let t=[];for(let n=0;n0){let s=o.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:n,gesture:`${s.name} forward`});let A=o.reduce((a,i)=>a.position[1][A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:s,confidence:e.confidence}}function G2(e,t=1.5){let n=o2(e),o=H2(e),s=[t*o[0]/2,t*o[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function V2(e){let t=o2(e),n=H2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function ws(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function K3(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ws(n)}var X3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function pe(e,t){let n=0;for(let o=0;o[a.x,a.y]),this.anchorsTensor=r.tensor2d(this.anchors),this.inputSize=((A=(s=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:s.shape)==null?void 0:A[2])||0,this.inputSizeTensor=r.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=r.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=r.slice(t,[0,0],[-1,2]),n.boxSizes=r.slice(t,[0,2],[-1,2]),n.div=r.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=r.add(n.div,this.anchorsTensor),n.halfBoxSizes=r.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=r.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=r.mul(n.sub,this.inputSizeTensor),n.add=r.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=r.mul(n.add,this.inputSizeTensor);let o=r.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>r.dispose(n[s])),o}normalizeLandmarks(t,n){let o={};o.reshape=r.reshape(t,[-1,7,2]),o.div=r.div(o.reshape,this.inputSizeTensor),o.landmarks=r.add(o.div,this.anchors[n]?this.anchors[n]:0);let s=r.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}async predict(t,n){var i;let o={};o.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=r.div(o.resize,O.tf127),o.image=r.sub(o.div,O.tf1),o.batched=this.model.execute(o.image),o.predictions=r.squeeze(o.batched),o.slice=r.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=r.sigmoid(o.slice),o.scores=r.squeeze(o.sigmoid);let s=await o.scores.data();o.boxes=r.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await r.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),a=[];for(let c of A){let d={};d.box=r.slice(o.norm,[c,0],[1,-1]),d.slice=r.slice(o.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=r.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),f=y.slice(2,4),x=await d.palmLandmarks.array(),u={startPoint:l,endPoint:f,palmLandmarks:x,confidence:s[c]},m=Y3(u,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(m),Object.keys(d).forEach(g=>r.dispose(d[g]))}return Object.keys(o).forEach(c=>r.dispose(o[c])),a}};var js=5,$3=1.65,en=[0,5,9,13,17,1,2],Is=0,Ns=2,tn=0,X2=class{constructor(t,n){E(this,"handDetector");E(this,"handPoseModel");E(this,"inputSize");E(this,"storedBoxes");E(this,"skipped");E(this,"detectedHands");var o,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>c5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return G2(V2(s),js)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=G2(V2(n),$3);o.palmLandmarks=[];for(let s=0;s[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=l5(o,[0,0]),d=i.map(x=>[...c5(x,c),x[2]]),y=J3(s),l=[...o2(n),1],f=[pe(l,y[0]),pe(l,y[1])];return d.map(x=>[Math.trunc(x[0]+f[0]),Math.trunc(x[1]+f[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>T()-tn,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a&&(s=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let j=r.reshape(p,[-1,3]),k=await j.array();r.dispose(p),r.dispose(j);let N=this.transformRawCoords(k,m,y,u),B=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...B,confidence:b};let q={landmarks:N,confidence:b,boxConfidence:d.confidence,fingerConfidence:b,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};i.push(q)}else this.storedBoxes[c]=null;r.dispose(p)}else{let y=G2(V2(d),$3),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var nn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},je,Ie,on;async function d5(e,t){let n=await on.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=B2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function rn(e){var n,o;M.initial&&(je=null,Ie=null),!je||!Ie?[je,Ie]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",je.modelUrl),e.debug&&h("cached model:",Ie.modelUrl));let t=je?new Z2(je):void 0;return t&&Ie&&(on=new X2(t,Ie)),[je,Ie]}var n0=[null,null],Os=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ue=[[0,0],[0,0]],Cs=["hand","fist","pinch","point","face","tip","pinchtip"],An=4,an=1.6,Ws=512,Ds=1.4,q2=Number.MAX_SAFE_INTEGER,x5=0,$0=[0,0],t0={boxes:[],hands:[]},ln={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function cn(e){var t;if(M.initial&&(n0[0]=null),n0[0])e.debug&&h("cached model:",n0[0].modelUrl);else{u2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),n0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=n0[0].executor?Object.values(n0[0].modelSignature.inputs):void 0;ue[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,ue[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return n0[0]}async function dn(e){var t;if(M.initial&&(n0[1]=null),n0[1])e.debug&&h("cached model:",n0[1].modelUrl);else{n0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=n0[1].executor?Object.values(n0[1].modelSignature.inputs):void 0;ue[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,ue[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return n0[1]}async function Fs(e,t){let n=[];if(!e||!n0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Ws),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await n0[0].executeAsync(o.cast,Os),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[An]),i.splice(An,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let f of Array.from(d)){let x=r.slice(o.boxes,f,1),u=await x.data();r.dispose(x);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],g=P2(m,Ds),P=[Math.trunc(m[0]*$0[0]),Math.trunc(m[1]*$0[1]),Math.trunc(m[2]*$0[0]),Math.trunc(m[3]*$0[1])],v=y[f],p=Cs[l[f]],b={id:c++,score:v,box:P,boxRaw:g,label:p};n.push(b)}return Object.keys(o).forEach(f=>r.dispose(o[f])),n.sort((f,x)=>x.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function y5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&n0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[ue[1][0],ue[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=n0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/ue[1][1],l[1]/ue[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[$0[0]*(l[0]+t.boxRaw[0]),$0[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=B2(o.keypoints);for(let l of Object.keys(ln))o.annotations[l]=ln[l].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function f5(e,t){var s,A;if(!((s=n0[0])!=null&&s.executor)||!((A=n0[1])!=null&&A.executor)||!n0[0].inputs[0].shape||!n0[1].inputs[0].shape)return[];$0=[e.shape[2]||0,e.shape[1]||0],q2++;let n=(t.hand.skipTime||0)>T()-x5,o=q2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?t0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>T()-x5,c=q2<3*(t.hand.skipFrames||0);t.skipAllowed&&t0.hands.length===t.hand.maxDetected?t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))):t.skipAllowed&&i&&c&&t0.hands.length>0?t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))):(t0.boxes=await Fs(e,t),x5=T(),t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))),q2=0);let d=[...t0.boxes];if(t0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y.05&&l.box[3]/(e.shape[1]||1)>.05&&t0.hands[y].fingerScore&&t0.hands[y].fingerScore>(t.hand.minConfidence||0)){let f=P2(l.box,an),x=P2(l.boxRaw,an);t0.boxes.push({...d[y],box:f,boxRaw:x})}}for(let y=0;y({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var r2={};oe(r2,{connected:()=>Y2,horizontal:()=>m5,kpt:()=>U2,relative:()=>u5,vertical:()=>p5});var U2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],m5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],p5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],u5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Y2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ee(),h5=0;function yn(e,t){var a,i,c,d,y,l,f,x,u,m,g,P,v,p,b,j,k,N,B,q,W,Z,J;let n=T();if(!e)return ee();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R((s-1)*z.body[R].box[F]+C)/s),U=e.body[R].boxRaw.map((C,F)=>((s-1)*z.body[R].boxRaw[F]+C)/s),H=e.body[R].keypoints.map((C,F)=>{var 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0:f.box);let v=Math.min(...m),p=Math.min(...g);u.box=[v,p,Math.max(...m)-v,Math.max(...g)-p],(s==null?void 0:s[1])&&(s==null?void 0:s[2])&&(u.boxRaw=[u.box[0]/s[2],u.box[1]/s[1],u.box[2]/s[2],u.box[3]/s[1]]),a.push(u)}return a}var nt=` + gaze: [gaze]\xB0`, + body: "body [score]%", + bodyPart: "[label] [score]%", + object: "[label] [score]%", + hand: "[label] [score]%", + finger: "[label]", + gesture: "[where] [who]: [what]" +}; + +// src/draw/draw.ts +var drawTime = 0; +function person(inCanvas2, result, drawOptions) { + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (let i = 0; i < result.length; i++) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels) { + const label = `person #${i}`; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +function canvas2(input, output) { + if (!input || !output) + return; + const ctx = getCanvasContext(output); + if (!ctx) + return; + ctx.drawImage(input, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + if (!(result == null ? void 0 : result.performance) || !inCanvas2) + return null; + const timeStamp = now(); + const localOptions2 = mergeDeep(options2, drawOptions); + const promise = Promise.all([ + face(inCanvas2, result.face, localOptions2), + body(inCanvas2, result.body, localOptions2), + hand(inCanvas2, result.hand, localOptions2), + object(inCanvas2, result.object, localOptions2), + gesture(inCanvas2, result.gesture, localOptions2) + ]); + drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); + result.performance.draw = drawTime; + return promise; +} +function init2() { + options2.faceLabels = defaultLabels.face; + options2.bodyLabels = defaultLabels.body; + options2.bodyPartLabels = defaultLabels.bodyPart; + options2.handLabels = defaultLabels.hand; + options2.fingerLabels = defaultLabels.finger; + options2.objectLabels = defaultLabels.object; + options2.gestureLabels = defaultLabels.gesture; +} + +// src/body/blazeposecoords.ts +var blazeposecoords_exports = {}; +__export(blazeposecoords_exports, { + connected: () => connected, + kpt: () => kpt +}); +var kpt = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftPinky", + "rightPinky", + "leftIndex", + "rightIndex", + "leftThumb", + "rightThumb", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle", + "leftHeel", + "rightHeel", + "leftFoot", + "rightFoot", + "bodyCenter", + "bodyTop", + "leftPalm", + "leftHand", + "rightPalm", + "rightHand" +]; +var connected = { + shoulders: ["leftShoulder", "rightShoulder"], + hips: ["rightHip", "leftHip"], + mouth: ["leftMouth", "rightMouth"], + leftLegUpper: ["leftHip", "leftKnee"], + leftLegLower: ["leftKnee", "leftAnkle"], + leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], + leftTorso: ["leftShoulder", "leftHip"], + leftArmUpper: ["leftShoulder", "leftElbow"], + leftArmLower: ["leftElbow", "leftWrist"], + leftHand: ["leftWrist", "leftPalm"], + leftHandPinky: ["leftPalm", "leftPinky"], + leftHandIndex: ["leftPalm", "leftIndex"], + leftHandThumb: ["leftPalm", "leftThumb"], + leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], + rightLegUpper: ["rightHip", "rightKnee"], + rightLegLower: ["rightKnee", "rightAnkle"], + rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], + rightTorso: ["rightShoulder", "rightHip"], + rightArmUpper: ["rightShoulder", "rightElbow"], + rightArmLower: ["rightElbow", "rightWrist"], + rightHand: ["rightWrist", "rightPalm"], + rightHandPinky: ["rightPalm", "rightPinky"], + rightHandIndex: ["rightPalm", "rightIndex"], + rightHandThumb: ["rightPalm", "rightThumb"], + rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] +}; + +// src/body/blazeposedetector.ts +var model; +var inputSize = 224; +var anchorTensor; +var numLayers = 5; +var strides = [8, 16, 32, 32, 32]; +function createAnchors() { + const anchors3 = []; + let layerId = 0; + while (layerId < numLayers) { + let anchorCount = 0; + let lastSameStrideLayer = layerId; + while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { + anchorCount += 2; + lastSameStrideLayer++; + } + const stride = strides[layerId]; + const featureMapHeight = Math.ceil(inputSize / stride); + const featureMapWidth = Math.ceil(inputSize / stride); + for (let y = 0; y < featureMapHeight; ++y) { + for (let x = 0; x < featureMapWidth; ++x) { + for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); + } + } + } + layerId = lastSameStrideLayer; + } + anchorTensor = { x: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.x)), y: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.y)) }; +} +async function loadDetector(config3) { + if (env.initial) + model = null; + if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { + model = await loadModel(config3.body["detector"].modelPath); + const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; + inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug && model) + log("cached model:", model["modelUrl"]); + createAnchors(); + return model; +} +var cropFactor = [5, 5]; +function decodeBoxes(boxesTensor, anchor) { + return tfjs_esm_exports.tidy(() => { + const split6 = tfjs_esm_exports.split(boxesTensor, 12, 1); + let xCenter = tfjs_esm_exports.squeeze(split6[0]); + let yCenter = tfjs_esm_exports.squeeze(split6[1]); + let width = tfjs_esm_exports.squeeze(split6[2]); + let height = tfjs_esm_exports.squeeze(split6[3]); + xCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(xCenter, inputSize), anchor.x); + yCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(yCenter, inputSize), anchor.y); + width = tfjs_esm_exports.mul(tfjs_esm_exports.div(width, inputSize), cropFactor[0]); + height = tfjs_esm_exports.mul(tfjs_esm_exports.div(height, inputSize), cropFactor[1]); + const xMin = tfjs_esm_exports.sub(xCenter, tfjs_esm_exports.div(width, 2)); + const yMin = tfjs_esm_exports.sub(yCenter, tfjs_esm_exports.div(height, 2)); + const xMax = tfjs_esm_exports.add(xMin, width); + const yMax = tfjs_esm_exports.add(yMin, height); + const boxes = tfjs_esm_exports.stack([xMin, yMin, xMax, yMax], 1); + return boxes; + }); +} +async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { + var _a, _b; + const detectedBoxes = []; + const t2 = {}; + t2.boxes = decodeBoxes(boxesTensor, anchorTensor); + t2.scores = tfjs_esm_exports.sigmoid(logitsTensor); + t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); + const nms = await t2.nms.data(); + const scores = await t2.scores.data(); + const boxes = await t2.boxes.array(); + for (const i of Array.from(nms)) { + const score = scores[i]; + const boxRaw = boxes[i]; + const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; + const detectedBox = { score, boxRaw, box }; + detectedBoxes.push(detectedBox); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return detectedBoxes; +} +async function detectBoxes(input, config3, outputSize2) { + const t2 = {}; + t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t2.logitsRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 0], [1, -1, 1]); + t2.boxesRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 1], [1, -1, -1]); + t2.logits = tfjs_esm_exports.squeeze(t2.logitsRaw); + t2.boxes = tfjs_esm_exports.squeeze(t2.boxesRaw); + const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return boxes; +} + +// src/util/box.ts +function calc(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function square(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; + const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); + const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function scale(box, scaleFact) { + const dist = [box[2] * scaleFact, box[3] * scaleFact]; + const newBox = [ + box[0] - (dist[0] - box[2]) / 2, + box[1] - (dist[1] - box[3]) / 2, + dist[0], + dist[1] + ]; + return newBox; +} + +// src/body/blazepose.ts +var model2; +var inputSize2 = 256; +var skipped = Number.MAX_SAFE_INTEGER; +var outputNodes = { + landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], + detector: [] +}; +var cache = []; +var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; +var lastTime = 0; +var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); +var loadDetect = (config3) => loadDetector(config3); +async function loadPose(config3) { + if (env.initial) + model2 = null; + if (!model2) { + model2 = await loadModel(config3.body.modelPath); + const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; + inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug) + log("cached model:", model2["modelUrl"]); + return model2; +} +function prepareImage(input, size2, cropBox) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + let final; + if (cropBox) { + t2.cropped = tfjs_esm_exports.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + } + if (input.shape[1] !== input.shape[2]) { + const height = [ + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 + ]; + const width = [ + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 + ]; + padding = [ + [0, 0], + height, + width, + [0, 0] + ]; + t2.pad = tfjs_esm_exports.pad(t2.cropped || input, padding); + t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [size2, size2]); + final = tfjs_esm_exports.div(t2.resize, constants.tf255); + } else if (input.shape[1] !== size2) { + t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.cropped || input, [size2, size2]); + final = tfjs_esm_exports.div(t2.resize, constants.tf255); + } else { + final = tfjs_esm_exports.div(t2.cropped || input, constants.tf255); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return final; +} +function rescaleKeypoints(keypoints, outputSize2, cropBox) { + for (const kpt4 of keypoints) { + kpt4.position = [ + Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), + Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), + kpt4.position[2] + ]; + kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; + } + if (cropBox) { + const width = cropBox[2] - cropBox[0]; + const height = cropBox[3] - cropBox[1]; + for (const kpt4 of keypoints) { + kpt4.positionRaw = [ + kpt4.positionRaw[0] / height + cropBox[1], + kpt4.positionRaw[1] / width + cropBox[0], + kpt4.positionRaw[2] + ]; + kpt4.position = [ + Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), + Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), + kpt4.positionRaw[2] + ]; + } + } + return keypoints; +} +function fixKeypoints(keypoints) { + const leftPalm = keypoints.find((k) => k.part === "leftPalm"); + const leftWrist = keypoints.find((k) => k.part === "leftWrist"); + const leftIndex = keypoints.find((k) => k.part === "leftIndex"); + leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; + const rightPalm = keypoints.find((k) => k.part === "rightPalm"); + const rightWrist = keypoints.find((k) => k.part === "rightWrist"); + const rightIndex = keypoints.find((k) => k.part === "rightIndex"); + rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; +} +async function detectLandmarks(input, config3, outputSize2) { + if (!(model2 == null ? void 0 : model2["executor"])) + return null; + const t2 = {}; + [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); + const poseScore = (await t2.poseflag.data())[0]; + const points = await t2.ld.data(); + const distances = await t2.world.data(); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + const keypointsRelative = []; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + const score = sigmoid2(points[depth * i + 3]); + const presence = sigmoid2(points[depth * i + 4]); + const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; + const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; + const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; + const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; + keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); + } + if (poseScore < (config3.body.minConfidence || 0)) + return null; + fixKeypoints(keypointsRelative); + const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); + const kpts = keypoints.map((k) => k.position); + const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; + return body4; +} +async function predict(input, config3) { + var _a, _b, _c; + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime; + const skipFrame = skipped < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { + skipped++; + } else { + let boxes = []; + if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { + const preparedImage = prepareImage(input, 224); + boxes = await detectBoxes(preparedImage, config3, outputSize2); + tfjs_esm_exports.dispose(preparedImage); + } else { + boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; + } + for (let i = 0; i < boxes.length; i++) { + const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); + cache.length = 0; + const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); + tfjs_esm_exports.dispose(preparedBox); + if (!bodyResult) + continue; + bodyResult.id = i; + cache.push(bodyResult); + } + lastTime = now(); + skipped = 0; + } + return cache; +} + +// src/object/labels.ts +var labels2 = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/centernet.ts +var model3; +var inputSize3 = 0; +var last2 = []; +var lastTime2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +async function load(config3) { + if (env.initial) + model3 = null; + if (!model3) { + model3 = await loadModel(config3.object.modelPath); + const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; + inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} +async function process3(res, outputShape, config3) { + if (!res) + return []; + const t2 = {}; + const results = []; + const detections = await res.array(); + t2.squeeze = tfjs_esm_exports.squeeze(res); + const arr = tfjs_esm_exports.split(t2.squeeze, 6, 1); + t2.stack = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1); + t2.boxes = tfjs_esm_exports.squeeze(t2.stack); + t2.scores = tfjs_esm_exports.squeeze(arr[4]); + t2.classes = tfjs_esm_exports.squeeze(arr[5]); + tfjs_esm_exports.dispose([res, ...arr]); + t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t2.nms.data(); + let i = 0; + for (const id of Array.from(nms)) { + const score = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + if (Number.isNaN(classVal)) + continue; + const label = labels2[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize3, + detections[0][id][1] / inputSize3 + ]; + const boxRaw = [ + x, + y, + detections[0][id][2] / inputSize3 - x, + detections[0][id][3] / inputSize3 - y + ]; + const box = [ + Math.trunc(boxRaw[0] * outputShape[0]), + Math.trunc(boxRaw[1] * outputShape[1]), + Math.trunc(boxRaw[2] * outputShape[0]), + Math.trunc(boxRaw[3] * outputShape[1]) + ]; + results.push({ id: i++, score, class: classVal, label, box, boxRaw }); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return results; +} +async function predict2(input, config3) { + if (!(model3 == null ? void 0 : model3["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; + const skipFrame = skipped2 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { + skipped2++; + return last2; + } + skipped2 = 0; + return new Promise(async (resolve) => { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize3, inputSize3]); + const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; + lastTime2 = now(); + tfjs_esm_exports.dispose(resize); + const obj = await process3(objectT, outputSize2, config3); + last2 = obj; + resolve(obj); + }); +} + +// src/body/efficientposecoords.ts +var efficientposecoords_exports = {}; +__export(efficientposecoords_exports, { + connected: () => connected2, + kpt: () => kpt2 +}); +var kpt2 = [ + "head", + "neck", + "rightShoulder", + "rightElbow", + "rightWrist", + "chest", + "leftShoulder", + "leftElbow", + "leftWrist", + "bodyCenter", + "rightHip", + "rightKnee", + "rightAnkle", + "leftHip", + "leftKnee", + "leftAnkle" +]; +var connected2 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/efficientpose.ts +var model4; +var lastTime3 = 0; +var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; +var skipped3 = Number.MAX_SAFE_INTEGER; +async function load2(config3) { + if (env.initial) + model4 = null; + if (!model4) + model4 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]); + const max5 = tfjs_esm_exports.max(reshaped, 0); + const newScore = (await max5.data())[0]; + if (newScore > minScore) { + const coordinates = tfjs_esm_exports.argMax(reshaped, 0); + const mod3 = tfjs_esm_exports.mod(coordinates, width); + const x = (await mod3.data())[0]; + const div15 = tfjs_esm_exports.div(coordinates, width); + const y = (await div15.data())[0]; + tfjs_esm_exports.dispose([reshaped, max5, coordinates, mod3, div15]); + return [x, y, newScore]; + } + tfjs_esm_exports.dispose([reshaped, max5]); + return [0, 0, newScore]; +} +async function predict3(image28, config3) { + if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) + return []; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; + const skipFrame = skipped3 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { + skipped3++; + return [cache2]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + const tensor6 = tfjs_esm_exports.tidy(() => { + var _a, _b; + const resize = tfjs_esm_exports.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); + const enhance2 = tfjs_esm_exports.mul(resize, constants.tf2); + const norm = tfjs_esm_exports.sub(enhance2, constants.tf1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = model4 == null ? void 0 : model4.execute(tensor6); + lastTime3 = now(); + tfjs_esm_exports.dispose(tensor6); + if (resT) { + cache2.keypoints.length = 0; + const squeeze14 = tfjs_esm_exports.squeeze(resT); + tfjs_esm_exports.dispose(resT); + const stack5 = tfjs_esm_exports.unstack(squeeze14, 2); + tfjs_esm_exports.dispose(squeeze14); + for (let id = 0; id < stack5.length; id++) { + const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); + if (partScore > (config3.body.minConfidence || 0)) { + cache2.keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: kpt2[id], + positionRaw: [ + x2 / model4.inputs[0].shape[2], + y2 / model4.inputs[0].shape[1] + ], + position: [ + Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), + Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) + ] + }); + } + } + stack5.forEach((s) => tfjs_esm_exports.dispose(s)); + } + cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = cache2.keypoints.map((a) => a.position[0]); + const y = cache2.keypoints.map((a) => a.position[1]); + cache2.box = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); + const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); + cache2.boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + for (const [name, indexes] of Object.entries(connected2)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + cache2.annotations[name] = pt; + } + resolve([cache2]); + }); +} + +// src/face/facemeshutil.ts +var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; +var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; +var clampBox = (box, input) => box ? [ + Math.trunc(Math.max(0, box.startPoint[0])), + Math.trunc(Math.max(0, box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) +] : [0, 0, 0, 0]; +var getRawBox = (box, input) => box ? [ + box.startPoint[0] / (input.shape[2] || 0), + box.startPoint[1] / (input.shape[1] || 0), + (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), + (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) +] : [0, 0, 0, 0]; +var scaleBoxCoordinates = (box, factor) => { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; +}; +var cutAndResize = (box, image28, cropSize) => { + const h = image28.shape[1]; + const w = image28.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; + const crop = tfjs_esm_exports.image.cropAndResize(image28, [cutBox], [0], cropSize); + const norm = tfjs_esm_exports.div(crop, constants.tf255); + tfjs_esm_exports.dispose(crop); + return norm; +}; +var enlargeBox = (box, factor) => { + const center = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; +}; +var squarifyBox = (box) => { + const centers = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = Math.max(...size2) / 2; + return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; +}; +var calculateLandmarksBoundingBox = (landmarks) => { + const x = landmarks.map((d) => d[0]); + const y = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; +}; +var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); +var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +var dot = (v1, v2) => { + let product = 0; + for (let i = 0; i < v1.length; i++) + product += v1[i] * v2[i]; + return product; +}; +var getColumnFrom2DArr = (arr, columnIndex) => { + const column = []; + for (let i = 0; i < arr.length; i++) + column.push(arr[i][columnIndex]); + return column; +}; +var multiplyTransformMatrices = (mat1, mat2) => { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + return product; +}; +var buildRotationMatrix = (rotation, center) => { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +}; +var invertTransformMatrix = (matrix) => { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; + return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; +}; +var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; +function generateAnchors(inputSize10) { + const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize10 + stride - 1) / stride); + const gridCols = Math.floor((inputSize10 + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) + anchors3.push([anchorX, anchorY]); + } + } + } + return anchors3; +} +function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { + const boxSize = getBoxSize(box); + const coordsScaled = coordsRaw.map((coord) => [ + boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), + boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), + coord[2] || 0 + ]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; + const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; + const boxCenter = getBoxCenter(box); + const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + offsets[0]), + Math.trunc(coord[1] + offsets[1]), + Math.trunc(coord[2] || 0) + ]); +} +function correctFaceRotation(rotate, box, input, inputSize10) { + const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + let angle = 0; + let rotationMatrix = fixedRotationMatrix; + let face4; + if (rotate && env.kernels.includes("rotatewithoffset")) { + angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + if (largeAngle) { + const center = getBoxCenter(box); + const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; + const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + rotationMatrix = buildRotationMatrix(-angle, center); + face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); + tfjs_esm_exports.dispose(rotated); + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + return [angle, rotationMatrix, face4]; +} +var findFaceCenter = (mesh) => { + const x = mesh.map((m) => m[0]); + const y = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; +}; +var calculateFaceBox = (mesh, previousBox) => { + const center = findFaceCenter(mesh); + const boxSize = getBoxSize(previousBox); + const calculatedBox = { + startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], + endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] + }; + return calculatedBox; +}; + +// src/face/blazeface.ts +var keypointsCount = 6; +var faceBoxScaleFactor = 1.4; +var model5; +var anchors = null; +var inputSize4 = 0; +var inputSizeT = null; +var size = () => inputSize4; +async function load3(config3) { + var _a; + if (env.initial) + model5 = null; + if (!model5) + model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model5["modelUrl"]); + inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; + inputSizeT = tfjs_esm_exports.scalar(inputSize4, "int32"); + anchors = tfjs_esm_exports.tensor2d(generateAnchors(inputSize4)); + return model5; +} +function decodeBoxes2(boxOutputs) { + if (!anchors || !inputSizeT) + return tfjs_esm_exports.zeros([0, 0]); + const t2 = {}; + t2.boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]); + t2.centers = tfjs_esm_exports.add(t2.boxStarts, anchors); + t2.boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]); + t2.boxSizesNormalized = tfjs_esm_exports.div(t2.boxSizes, inputSizeT); + t2.centersNormalized = tfjs_esm_exports.div(t2.centers, inputSizeT); + t2.halfBoxSize = tfjs_esm_exports.div(t2.boxSizesNormalized, constants.tf2); + t2.starts = tfjs_esm_exports.sub(t2.centersNormalized, t2.halfBoxSize); + t2.ends = tfjs_esm_exports.add(t2.centersNormalized, t2.halfBoxSize); + t2.startNormalized = tfjs_esm_exports.mul(t2.starts, inputSizeT); + t2.endNormalized = tfjs_esm_exports.mul(t2.ends, inputSizeT); + const boxes = tfjs_esm_exports.concat2d([t2.startNormalized, t2.endNormalized], 1); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return boxes; +} +async function getBoxes(inputImage, config3) { + var _a, _b, _c, _d; + if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return []; + const t2 = {}; + t2.resized = tfjs_esm_exports.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t2.div = tfjs_esm_exports.div(t2.resized, constants.tf127); + t2.normalized = tfjs_esm_exports.sub(t2.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t2.normalized); + if (Array.isArray(res) && res.length > 2) { + const sorted = res.sort((a, b) => a.size - b.size); + t2.concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2); + t2.concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2); + t2.concat = tfjs_esm_exports.concat([t2.concat512, t2.concat384], 1); + t2.batch = tfjs_esm_exports.squeeze(t2.concat, [0]); + } else if (Array.isArray(res)) { + t2.batch = tfjs_esm_exports.squeeze(res[0]); + } else { + t2.batch = tfjs_esm_exports.squeeze(res); + } + tfjs_esm_exports.dispose(res); + t2.boxes = decodeBoxes2(t2.batch); + t2.logits = tfjs_esm_exports.slice(t2.batch, [0, 0], [-1, 1]); + t2.sigmoid = tfjs_esm_exports.sigmoid(t2.logits); + t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); + t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); + const nms = await t2.nms.array(); + const boxes = []; + const scores = await t2.scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scores[nms[i]]; + if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { + const b = {}; + b.bbox = tfjs_esm_exports.slice(t2.boxes, [nms[i], 0], [1, -1]); + b.slice = tfjs_esm_exports.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = tfjs_esm_exports.squeeze(b.slice); + b.landmarks = tfjs_esm_exports.reshape(b.squeeze, [keypointsCount, -1]); + const points = await b.bbox.data(); + const rawBox = { + startPoint: [points[0], points[1]], + endPoint: [points[2], points[3]], + landmarks: await b.landmarks.array(), + confidence + }; + const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); + const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); + const squaredBox = squarifyBox(enlargedBox); + boxes.push(squaredBox); + Object.keys(b).forEach((tensor6) => tfjs_esm_exports.dispose(b[tensor6])); + } + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return boxes; +} + +// src/face/iris.ts +var model6; +var inputSize5 = 0; +var irisEnlarge = 2.3; +var leftOutline = meshAnnotations.leftEyeLower0; +var rightOutline = meshAnnotations.rightEyeLower0; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +async function load4(config3) { + var _a, _b; + if (env.initial) + model6 = null; + if (!model6) + model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model6["modelUrl"]); + inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; + if (inputSize5 === -1) + inputSize5 = 64; + return model6; +} +function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < irisIndices.length; i++) { + const { key, indices } = irisIndices[i]; + const originalIndices = meshAnnotations[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index2 = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index2][0], + newCoords[index2][1], + (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var getLeftToRightEyeDepthDifference = (rawCoords) => { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; +}; +var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { + const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); + const boxSize = getBoxSize(box); + let crop = tfjs_esm_exports.image.cropAndResize(face4, [[ + box.startPoint[1] / meshSize, + box.startPoint[0] / meshSize, + box.endPoint[1] / meshSize, + box.endPoint[0] / meshSize + ]], [0], [inputSize5, inputSize5]); + if (flip && env.kernels.includes("flipleftright")) { + const flipped = tfjs_esm_exports.image.flipLeftRight(crop); + tfjs_esm_exports.dispose(crop); + crop = flipped; + } + return { box, boxSize, crop }; +}; +var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; +}; +var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { + const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); +}; +async function augmentIris(rawCoords, face4, meshSize) { + if (!(model6 == null ? void 0 : model6["executor"])) + return rawCoords; + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); + const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]); + tfjs_esm_exports.dispose(leftEyeCrop); + tfjs_esm_exports.dispose(rightEyeCrop); + const eyePredictions = model6.execute(combined); + tfjs_esm_exports.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tfjs_esm_exports.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); + const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; +} + +// src/face/attention.ts +async function augment(rawCoords, results) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + const t2 = { + lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), + irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), + eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), + irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), + eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) + }; + for (const val of Object.values(t2)) { + if (!val) + return rawCoords; + } + const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisL.length / 2; i++) + rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); + const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisR.length / 2; i++) + rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t2.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + return rawCoords; +} + +// src/face/facemesh.ts +var cache3 = { + boxes: [], + skipped: Number.MAX_SAFE_INTEGER, + timestamp: 0 +}; +var model7 = null; +var inputSize6 = 0; +async function predict4(input, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + if (!(model7 == null ? void 0 : model7["executor"])) + return []; + const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; + const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); + if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { + cache3.boxes = await getBoxes(input, config3); + cache3.timestamp = now(); + cache3.skipped = 0; + } else { + cache3.skipped++; + } + const faces = []; + const newCache = []; + let id = 0; + const size2 = inputSize6; + for (let i = 0; i < cache3.boxes.length; i++) { + const box = cache3.boxes[i]; + let angle = 0; + let rotationMatrix; + const face4 = { + id: id++, + mesh: [], + meshRaw: [], + box: [0, 0, 0, 0], + boxRaw: [0, 0, 0, 0], + score: 0, + boxScore: 0, + faceScore: 0, + annotations: {} + }; + [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); + if (config3.filter.equalization) { + const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; + tfjs_esm_exports.dispose(face4.tensor); + if (equilized) + face4.tensor = equilized; + } + face4.boxScore = Math.round(100 * box.confidence) / 100; + if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } else if (!model7) { + if (config3.debug) + log("face mesh detection requested, but model is not loaded"); + } else { + if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { + config3.face.attention.enabled = false; + tfjs_esm_exports.dispose(face4.tensor); + return faces; + } + const results = model7.execute(face4.tensor); + const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); + const faceConfidence = await confidenceT.data(); + face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; + if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { + box.confidence = face4.faceScore; + if (config3.face.mesh.keepInvalid) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } + } else { + const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); + const coordsReshaped = tfjs_esm_exports.reshape(meshT, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + tfjs_esm_exports.dispose(coordsReshaped); + if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { + rawCoords = await augment(rawCoords, results); + } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); + } + face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(meshAnnotations)) + face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); + face4.score = face4.faceScore; + const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; + face4.box = clampBox(calculatedBox, input); + face4.boxRaw = getRawBox(calculatedBox, input); + newCache.push(calculatedBox); + } + tfjs_esm_exports.dispose(results); + } + if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) + faces.push(face4); + else + tfjs_esm_exports.dispose(face4.tensor); + } + cache3.boxes = newCache; + return faces; +} +async function load5(config3) { + var _a, _b, _c, _d, _e, _f; + if (env.initial) + model7 = null; + if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { + if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) + model7 = null; + } + if (!model7) { + if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) + model7 = await loadModel(config3.face.attention.modelPath); + else + model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); + } else if (config3.debug) { + log("cached model:", model7["modelUrl"]); + } + inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; + return model7; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/gear/emotion.ts +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model8; +var last3 = []; +var lastCount = 0; +var lastTime4 = 0; +var skipped4 = Number.MAX_SAFE_INTEGER; +async function load6(config3) { + var _a; + if (env.initial) + model8 = null; + if (!model8) + model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model8["modelUrl"]); + return model8; +} +async function predict5(image28, config3, idx, count2) { + var _a, _b; + if (!model8) + return []; + const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; + if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { + skipped4++; + return last3[idx]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + var _a2; + const obj = []; + if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { + const t2 = {}; + const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; + t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize10, inputSize10], false); + t2.channels = tfjs_esm_exports.mul(t2.resize, constants.rgb); + t2.grayscale = tfjs_esm_exports.sum(t2.channels, 3, true); + t2.grayscaleSub = tfjs_esm_exports.sub(t2.grayscale, constants.tf05); + t2.grayscaleMul = tfjs_esm_exports.mul(t2.grayscaleSub, constants.tf2); + t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); + lastTime4 = now(); + const data = await t2.emotion.data(); + for (let i = 0; i < data.length; i++) { + if (data[i] > (config3.face.emotion.minConfidence || 0)) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + } + last3[idx] = obj; + lastCount = count2; + resolve(obj); + }); +} + +// src/face/faceres.ts +var model9; +var last4 = []; +var lastTime5 = 0; +var lastCount2 = 0; +var skipped5 = Number.MAX_SAFE_INTEGER; +async function load7(config3) { + var _a; + if (env.initial) + model9 = null; + if (!model9) + model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +function enhance(input) { + const tensor6 = input.image || input.tensor || input; + if (!(model9 == null ? void 0 : model9.inputs[0].shape)) + return tensor6; + const crop = tfjs_esm_exports.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + const norm = tfjs_esm_exports.mul(crop, constants.tf255); + tfjs_esm_exports.dispose(crop); + return norm; +} +async function predict6(image28, config3, idx, count2) { + var _a, _b, _c, _d; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if (!(model9 == null ? void 0 : model9["executor"])) + return obj; + const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; + if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped5++; + return last4[idx]; + } + skipped5 = 0; + return new Promise(async (resolve) => { + var _a2; + if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { + const enhanced = enhance(image28); + const resT = model9 == null ? void 0 : model9.execute(enhanced); + lastTime5 = now(); + tfjs_esm_exports.dispose(enhanced); + const genderT = resT.find((t2) => t2.shape[1] === 1); + const gender2 = await genderT.data(); + const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; + if (confidence > (config3.face.description.minConfidence || 0)) { + obj.gender = gender2[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tfjs_esm_exports.argMax(resT.find((t2) => t2.shape[1] === 100), 1); + const ageIdx = (await argmax.data())[0]; + tfjs_esm_exports.dispose(argmax); + const ageT = resT.find((t2) => t2.shape[1] === 100); + const all2 = await ageT.data(); + obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; + if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) + log("faceres error:", { model: model9, result: resT }); + const desc = resT.find((t2) => t2.shape[1] === 1024); + const descriptor = desc ? await desc.data() : []; + obj.descriptor = Array.from(descriptor); + resT.forEach((t2) => tfjs_esm_exports.dispose(t2)); + } + last4[idx] = obj; + lastCount2 = count2; + resolve(obj); + }); +} + +// src/face/mask.ts +var expandFact = 0.1; +var alpha = 0.5; +function insidePoly(x, y, polygon) { + let inside = false; + let j = polygon.length - 1; + for (let i = 0; i < polygon.length; j = i++) { + if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + inside = !inside; + } + return inside; +} +async function mask(face4) { + if (!face4.tensor) + return face4.tensor; + if (!face4.mesh || face4.mesh.length < 100) + return face4.tensor; + const width = face4.tensor.shape[2] || 0; + const height = face4.tensor.shape[1] || 0; + const buffer = await face4.tensor.buffer(); + let silhouette = []; + for (const pt of meshAnnotations.silhouette) + silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); + if (expandFact && expandFact > 0) + silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); + for (let x = 0; x < width; x++) { + for (let y = 0; y < height; y++) { + const inside = insidePoly(x / width, y / width, silhouette); + if (!inside) { + buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); + buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); + buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); + } + } + } + const output = buffer.toTensor(); + return output; +} + +// src/face/antispoof.ts +var model10; +var cached = []; +var skipped6 = Number.MAX_SAFE_INTEGER; +var lastCount3 = 0; +var lastTime6 = 0; +async function load8(config3) { + var _a; + if (env.initial) + model10 = null; + if (!model10) + model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model10["modelUrl"]); + return model10; +} +async function predict7(image28, config3, idx, count2) { + var _a, _b; + if (!(model10 == null ? void 0 : model10["executor"])) + return 0; + const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; + const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { + skipped6++; + return cached[idx]; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const res = model10 == null ? void 0 : model10.execute(resize); + const num = (await res.data())[0]; + cached[idx] = Math.round(100 * num) / 100; + lastCount3 = count2; + lastTime6 = now(); + tfjs_esm_exports.dispose([resize, res]); + resolve(cached[idx]); + }); +} + +// src/face/liveness.ts +var model11; +var cached2 = []; +var skipped7 = Number.MAX_SAFE_INTEGER; +var lastCount4 = 0; +var lastTime7 = 0; +async function load9(config3) { + var _a; + if (env.initial) + model11 = null; + if (!model11) + model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model11["modelUrl"]); + return model11; +} +async function predict8(image28, config3, idx, count2) { + var _a, _b; + if (!(model11 == null ? void 0 : model11["executor"])) + return 0; + const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; + const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { + skipped7++; + return cached2[idx]; + } + skipped7 = 0; + return new Promise(async (resolve) => { + const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const res = model11 == null ? void 0 : model11.execute(resize); + const num = (await res.data())[0]; + cached2[idx] = Math.round(100 * num) / 100; + lastCount4 = count2; + lastTime7 = now(); + tfjs_esm_exports.dispose([resize, res]); + resolve(cached2[idx]); + }); +} + +// src/gear/gear.ts +var model12; +var last5 = []; +var raceNames = ["white", "black", "asian", "indian", "other"]; +var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; +var lastCount5 = 0; +var lastTime8 = 0; +var skipped8 = Number.MAX_SAFE_INTEGER; +async function load10(config3) { + var _a; + if (env.initial) + model12 = null; + if (!model12) + model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model12["modelUrl"]); + return model12; +} +async function predict9(image28, config3, idx, count2) { + var _a, _b; + if (!model12) + return { age: 0, gender: "unknown", genderScore: 0, race: [] }; + const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; + if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { + skipped8++; + return last5[idx]; + } + skipped8 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + if (!(model12 == null ? void 0 : model12.inputs[0].shape)) + return; + const t2 = {}; + const box = [[0, 0.1, 0.9, 0.9]]; + t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; + if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) + [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t2.gender.data(); + obj.gender = gender2[0] > gender2[1] ? "male" : "female"; + obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; + const race = await t2.race.data(); + for (let i = 0; i < race.length; i++) { + if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) + obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); + } + obj.race.sort((a, b) => b.score - a.score); + const ageDistribution = Array.from(await t2.age.data()); + const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); + let age2 = ageSorted[0][0]; + for (let i = 1; i < ageSorted.length; i++) + age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); + obj.age = Math.round(10 * age2) / 10; + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + last5[idx] = obj; + lastCount5 = count2; + lastTime8 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-age.ts +var model13; +var last6 = []; +var lastCount6 = 0; +var lastTime9 = 0; +var skipped9 = Number.MAX_SAFE_INTEGER; +async function load11(config3) { + if (env.initial) + model13 = null; + if (!model13) + model13 = await loadModel(config3.face["ssrnet"].modelPathAge); + else if (config3.debug) + log("cached model:", model13["modelUrl"]); + return model13; +} +async function predict10(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model13) + return { age: 0 }; + const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; + if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { + skipped9++; + return last6[idx]; + } + skipped9 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) + return; + const t2 = {}; + t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t2.enhance = tfjs_esm_exports.mul(t2.resize, constants.tf255); + const obj = { age: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.age = model13.execute(t2.enhance); + if (t2.age) { + const data = await t2.age.data(); + obj.age = Math.trunc(10 * data[0]) / 10; + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + last6[idx] = obj; + lastCount6 = count2; + lastTime9 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-gender.ts +var model14; +var last7 = []; +var lastCount7 = 0; +var lastTime10 = 0; +var skipped10 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load12(config3) { + var _a; + if (env.initial) + model14 = null; + if (!model14) + model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); + else if (config3.debug) + log("cached model:", model14["modelUrl"]); + return model14; +} +async function predict11(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model14) + return { gender: "unknown", genderScore: 0 }; + const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; + if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped10++; + return last7[idx]; + } + skipped10 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model14 == null ? void 0 : model14.inputs[0].shape)) + return; + const t2 = {}; + t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t2.enhance = tfjs_esm_exports.tidy(() => { + const [red, green, blue] = tfjs_esm_exports.split(t2.resize, 3, 3); + const redNorm = tfjs_esm_exports.mul(red, rgb[0]); + const greenNorm = tfjs_esm_exports.mul(green, rgb[1]); + const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]); + const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]); + const normalize2 = tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, constants.tf05), 2); + return normalize2; + }); + const obj = { gender: "unknown", genderScore: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.gender = model14.execute(t2.enhance); + const data = await t2.gender.data(); + obj.gender = data[0] > data[1] ? "female" : "male"; + obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + last7[idx] = obj; + lastCount7 = count2; + lastTime10 = now(); + resolve(obj); + }); +} + +// src/face/mobilefacenet.ts +var model15; +var last8 = []; +var lastCount8 = 0; +var lastTime11 = 0; +var skipped11 = Number.MAX_SAFE_INTEGER; +async function load13(config3) { + var _a; + if (env.initial) + model15 = null; + if (!model15) + model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model15["modelUrl"]); + return model15; +} +async function predict12(input, config3, idx, count2) { + var _a, _b; + if (!(model15 == null ? void 0 : model15["executor"])) + return []; + const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; + if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { + skipped11++; + return last8[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { + const t2 = {}; + t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t2.data = model15.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + } + last8[idx] = data; + lastCount8 = count2; + lastTime11 = now(); + resolve(data); + }); +} + +// src/face/insightface.ts +var model16; +var last9 = []; +var lastCount9 = 0; +var lastTime12 = 0; +var skipped12 = Number.MAX_SAFE_INTEGER; +async function load14(config3) { + if (env.initial) + model16 = null; + if (!model16) + model16 = await loadModel(config3.face["insightface"].modelPath); + else if (config3.debug) + log("cached model:", model16["modelUrl"]); + return model16; +} +async function predict13(input, config3, idx, count2) { + var _a, _b; + if (!(model16 == null ? void 0 : model16["executor"])) + return []; + const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; + if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { + skipped12++; + return last9[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { + const t2 = {}; + t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t2.data = model16.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + } + last9[idx] = data; + lastCount9 = count2; + lastTime12 = now(); + resolve(data); + }); +} + +// src/face/angles.ts +var calculateGaze = (face4) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); + const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; + const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; + const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); + strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face4, imageSize) => { + const normalize2 = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + if (Number.isNaN(thetaX)) + thetaX = 0; + if (Number.isNaN(thetaY)) + thetaY = 0; + if (Number.isNaN(thetaZ)) + thetaZ = 0; + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const mesh = face4.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); + const yAxis = normalize2(subVectors(pts[1], pts[0])); + let xAxis = normalize2(subVectors(pts[3], pts[2])); + const zAxis = normalize2(crossVectors(xAxis, yAxis)); + xAxis = crossVectors(yAxis, zAxis); + const matrix = [ + xAxis[0], + xAxis[1], + xAxis[2], + yAxis[0], + yAxis[1], + yAxis[2], + zAxis[0], + zAxis[1], + zAxis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; + +// src/face/anthropometry.ts +function calculateCameraDistance(face4, width) { + const f = face4 == null ? void 0 : face4.annotations; + if (!f) + return 0; + const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; + const cameraDistance = Math.round(1.17 / irisSize) / 100; + return cameraDistance; +} + +// src/face/face.ts +var detectFace = async (instance, input) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + let timeStamp = now(); + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let mobilefacenetRes; + let insightfaceRes; + let antispoofRes; + let livenessRes; + let descRes; + const faceRes = []; + instance.state = "run:face"; + const faces = await predict4(input, instance.config); + instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + instance.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { + const masked = await mask(faces[i]); + tfjs_esm_exports.dispose(faces[i].tensor); + if (masked) + faces[i].tensor = masked; + } + const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; + instance.analyze("Start Emotion:"); + if (instance.config.async) { + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; + } else { + instance.state = "run:emotion"; + timeStamp = now(); + emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; + instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Emotion:"); + instance.analyze("Start AntiSpoof:"); + if (instance.config.async) { + antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:antispoof"; + timeStamp = now(); + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End AntiSpoof:"); + instance.analyze("Start Liveness:"); + if (instance.config.async) { + livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:liveness"; + timeStamp = now(); + livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Liveness:"); + instance.analyze("Start GEAR:"); + if (instance.config.async) { + gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:gear"; + timeStamp = now(); + gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.gear = Math.trunc(now() - timeStamp); + } + instance.analyze("End GEAR:"); + instance.analyze("Start SSRNet:"); + if (instance.config.async) { + ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:ssrnet"; + timeStamp = now(); + ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.ssrnet = Math.trunc(now() - timeStamp); + } + instance.analyze("End SSRNet:"); + instance.analyze("Start MobileFaceNet:"); + if (instance.config.async) { + mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End MobileFaceNet:"); + instance.analyze("Start InsightFace:"); + if (instance.config.async) { + insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End InsightFace:"); + instance.analyze("Start Description:"); + if (instance.config.async) { + descRes = predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); + } else { + instance.state = "run:description"; + timeStamp = now(); + descRes = await predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); + instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Description:"); + if (instance.config.async) { + [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); + } + instance.analyze("Finish Face:"); + if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { + descRes = { + ...descRes, + age: ageRes.age, + gender: genderRes.gender, + genderScore: genderRes.genderScore + }; + } + if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { + descRes = { + ...descRes, + age: gearRes.age, + gender: gearRes.gender, + genderScore: gearRes.genderScore, + race: gearRes.race + }; + } + if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { + descRes.descriptor = mobilefacenetRes; + } + if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { + descRes.descriptor = insightfaceRes; + } + const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; + const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tfjs_esm_exports.squeeze(faces[i].tensor) : null; + tfjs_esm_exports.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + const res = { + ...faces[i], + id: i + }; + if (descRes.age) + res.age = descRes.age; + if (descRes.gender) + res.gender = descRes.gender; + if (descRes.genderScore) + res.genderScore = descRes.genderScore; + if (descRes.descriptor) + res.embedding = descRes.descriptor; + if (descRes.race) + res.race = descRes.race; + if (emotionRes) + res.emotion = emotionRes; + if (antispoofRes) + res.real = antispoofRes; + if (livenessRes) + res.live = livenessRes; + if (irisSize > 0) + res.distance = irisSize; + if (rotation) + res.rotation = rotation; + if (tensor6) + res.tensor = tensor6; + faceRes.push(res); + instance.analyze("End Face"); + } + instance.analyze("End FaceMesh:"); + if (instance.config.async) { + if (instance.performance.face) + delete instance.performance.face; + if (instance.performance.age) + delete instance.performance.age; + if (instance.performance.gender) + delete instance.performance.gender; + if (instance.performance.emotion) + delete instance.performance.emotion; + } + return faceRes; +}; + +// src/hand/fingerdef.ts +var Finger = { + thumb: 0, + index: 1, + middle: 2, + ring: 3, + pinky: 4, + all: [0, 1, 2, 3, 4], + nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, + pointsMapping: { + 0: [[0, 1], [1, 2], [2, 3], [3, 4]], + 1: [[0, 5], [5, 6], [6, 7], [7, 8]], + 2: [[0, 9], [9, 10], [10, 11], [11, 12]], + 3: [[0, 13], [13, 14], [14, 15], [15, 16]], + 4: [[0, 17], [17, 18], [18, 19], [19, 20]] + }, + getName: (value) => Finger.nameMapping[value], + getPoints: (value) => Finger.pointsMapping[value] +}; +var FingerCurl = { + none: 0, + half: 1, + full: 2, + nameMapping: { 0: "none", 1: "half", 2: "full" }, + getName: (value) => FingerCurl.nameMapping[value] +}; +var FingerDirection = { + verticalUp: 0, + verticalDown: 1, + horizontalLeft: 2, + horizontalRight: 3, + diagonalUpRight: 4, + diagonalUpLeft: 5, + diagonalDownRight: 6, + diagonalDownLeft: 7, + nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, + getName: (value) => FingerDirection.nameMapping[value] +}; +var FingerGesture = class { + constructor(name) { + __publicField(this, "name"); + __publicField(this, "curls"); + __publicField(this, "directions"); + __publicField(this, "weights"); + __publicField(this, "weightsRelative"); + this.name = name; + this.curls = {}; + this.directions = {}; + this.weights = [1, 1, 1, 1, 1]; + this.weightsRelative = [1, 1, 1, 1, 1]; + } + curl(finger, curl, confidence) { + if (typeof this.curls[finger] === "undefined") + this.curls[finger] = []; + this.curls[finger].push([curl, confidence]); + } + direction(finger, position, confidence) { + if (!this.directions[finger]) + this.directions[finger] = []; + this.directions[finger].push([position, confidence]); + } + weight(finger, weight) { + this.weights[finger] = weight; + const total = this.weights.reduce((a, b) => a + b, 0); + this.weightsRelative = this.weights.map((el) => el * 5 / total); + } + matchAgainst(detectedCurls, detectedDirections) { + let confidence = 0; + for (const fingerIdx in detectedCurls) { + const detectedCurl = detectedCurls[fingerIdx]; + const expectedCurls = this.curls[fingerIdx]; + if (typeof expectedCurls === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedCurl, score] of expectedCurls) { + if (detectedCurl === expectedCurl) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + for (const fingerIdx in detectedDirections) { + const detectedDirection = detectedDirections[fingerIdx]; + const expectedDirections = this.directions[fingerIdx]; + if (typeof expectedDirections === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedDirection, score] of expectedDirections) { + if (detectedDirection === expectedDirection) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + return confidence / 10; + } +}; + +// src/hand/fingergesture.ts +var { thumb, index, middle, ring, pinky } = Finger; +var { none, half, full } = FingerCurl; +var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; +var ThumbsUp = new FingerGesture("thumbs up"); +ThumbsUp.curl(thumb, none, 1); +ThumbsUp.direction(thumb, verticalUp, 1); +ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); +ThumbsUp.direction(thumb, diagonalUpRight, 0.25); +for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { + ThumbsUp.curl(finger, full, 1); + ThumbsUp.direction(finger, horizontalLeft, 1); + ThumbsUp.direction(finger, horizontalRight, 1); +} +var Victory = new FingerGesture("victory"); +Victory.curl(thumb, half, 0.5); +Victory.curl(thumb, none, 0.5); +Victory.direction(thumb, verticalUp, 1); +Victory.direction(thumb, diagonalUpLeft, 1); +Victory.curl(index, none, 1); +Victory.direction(index, verticalUp, 0.75); +Victory.direction(index, diagonalUpLeft, 1); +Victory.curl(middle, none, 1); +Victory.direction(middle, verticalUp, 1); +Victory.direction(middle, diagonalUpLeft, 0.75); +Victory.curl(ring, full, 1); +Victory.direction(ring, verticalUp, 0.2); +Victory.direction(ring, diagonalUpLeft, 1); +Victory.direction(ring, horizontalLeft, 0.2); +Victory.curl(pinky, full, 1); +Victory.direction(pinky, verticalUp, 0.2); +Victory.direction(pinky, diagonalUpLeft, 1); +Victory.direction(pinky, horizontalLeft, 0.2); +Victory.weight(index, 2); +Victory.weight(middle, 2); +var Point = new FingerGesture("point"); +Point.curl(thumb, full, 1); +Point.curl(index, none, 0.5); +Point.curl(middle, full, 0.5); +Point.curl(ring, full, 0.5); +Point.curl(pinky, full, 0.5); +Point.weight(index, 2); +Point.weight(middle, 2); +var MiddleFinger = new FingerGesture("middle finger"); +MiddleFinger.curl(thumb, none, 1); +MiddleFinger.curl(index, full, 0.5); +MiddleFinger.curl(middle, full, 0.5); +MiddleFinger.curl(ring, full, 0.5); +MiddleFinger.curl(pinky, full, 0.5); +MiddleFinger.weight(index, 2); +MiddleFinger.weight(middle, 2); +var OpenPalm = new FingerGesture("open palm"); +OpenPalm.curl(thumb, none, 0.75); +OpenPalm.curl(index, none, 0.75); +OpenPalm.curl(middle, none, 0.75); +OpenPalm.curl(ring, none, 0.75); +OpenPalm.curl(pinky, none, 0.75); +var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; + +// src/hand/fingerpose.ts +var minConfidence = 0.7; +var options3 = { + HALF_CURL_START_LIMIT: 60, + NO_CURL_START_LIMIT: 130, + DISTANCE_VOTE_POWER: 1.1, + SINGLE_ANGLE_VOTE_POWER: 0.9, + TOTAL_ANGLE_VOTE_POWER: 1.6 +}; +function calculateSlope(point1x, point1y, point2x, point2y) { + const value = (point1y - point2y) / (point1x - point2x); + let slope = Math.atan(value) * 180 / Math.PI; + if (slope <= 0) + slope = -slope; + else if (slope > 0) + slope = 180 - slope; + return slope; +} +function getSlopes(point1, point2) { + if (!point1 || !point2) + return [0, 0]; + const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); + if (point1.length === 2) + return slopeXY; + const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); + return [slopeXY, slopeYZ]; +} +function angleOrientationAt(angle, weightageAt = 1) { + let isVertical = 0; + let isDiagonal = 0; + let isHorizontal = 0; + if (angle >= 75 && angle <= 105) + isVertical = 1 * weightageAt; + else if (angle >= 25 && angle <= 155) + isDiagonal = 1 * weightageAt; + else + isHorizontal = 1 * weightageAt; + return [isVertical, isDiagonal, isHorizontal]; +} +function estimateFingerCurl(startPoint, midPoint, endPoint) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const start_mid_z_dist = startPoint[2] - midPoint[2]; + const start_end_z_dist = startPoint[2] - endPoint[2]; + const mid_end_z_dist = midPoint[2] - endPoint[2]; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); + let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); + if (cos_in > 1) + cos_in = 1; + else if (cos_in < -1) + cos_in = -1; + let angleOfCurve = Math.acos(cos_in); + angleOfCurve = 57.2958 * angleOfCurve % 180; + let fingerCurl; + if (angleOfCurve > options3.NO_CURL_START_LIMIT) + fingerCurl = FingerCurl.none; + else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) + fingerCurl = FingerCurl.half; + else + fingerCurl = FingerCurl.full; + return fingerCurl; +} +function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + if (max_dist_x === Math.abs(start_end_x_dist)) { + if (start_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else if (max_dist_x === Math.abs(start_mid_x_dist)) { + if (start_mid_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else { + if (mid_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } + return estimatedDirection; +} +function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { + let estimatedDirection; + if (max_dist_y === Math.abs(start_end_y_dist)) { + if (start_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else if (max_dist_y === Math.abs(start_mid_y_dist)) { + if (start_mid_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else { + if (mid_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } + return estimatedDirection; +} +function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + if (reqd_vertical_direction === FingerDirection.verticalUp) { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalUpLeft; + else + estimatedDirection = FingerDirection.diagonalUpRight; + } else { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalDownLeft; + else + estimatedDirection = FingerDirection.diagonalDownRight; + } + return estimatedDirection; +} +function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); + const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); + let voteVertical = 0; + let voteDiagonal = 0; + let voteHorizontal = 0; + const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); + if (start_end_x_y_dist_ratio > 1.5) + voteVertical += options3.DISTANCE_VOTE_POWER; + else if (start_end_x_y_dist_ratio > 0.66) + voteDiagonal += options3.DISTANCE_VOTE_POWER; + else + voteHorizontal += options3.DISTANCE_VOTE_POWER; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); + const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); + let calc_start_point_x = startPoint[0]; + let calc_start_point_y = startPoint[1]; + let calc_end_point_x = endPoint[0]; + let calc_end_point_y = endPoint[1]; + if (max_dist === start_mid_dist) { + calc_end_point_x = endPoint[0]; + calc_end_point_y = endPoint[1]; + } else if (max_dist === mid_end_dist) { + calc_start_point_x = midPoint[0]; + calc_start_point_y = midPoint[1]; + } + const calcStartPoint = [calc_start_point_x, calc_start_point_y]; + const calcEndPoint = [calc_end_point_x, calc_end_point_y]; + const totalAngle = getSlopes(calcStartPoint, calcEndPoint); + const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); + voteVertical += votes[0]; + voteDiagonal += votes[1]; + voteHorizontal += votes[2]; + for (const fingerSlope of fingerSlopes) { + const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); + voteVertical += fingerVotes[0]; + voteDiagonal += fingerVotes[1]; + voteHorizontal += fingerVotes[2]; + } + let estimatedDirection; + if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } else { + estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } + return estimatedDirection; +} +function estimate(landmarks) { + const slopesXY = []; + const slopesYZ = []; + const fingerCurls = []; + const fingerDirections = []; + if (!landmarks) + return { curls: fingerCurls, directions: fingerDirections }; + for (const finger of Finger.all) { + const points = Finger.getPoints(finger); + const slopeAtXY = []; + const slopeAtYZ = []; + for (const point2 of points) { + const point1 = landmarks[point2[0]]; + const point22 = landmarks[point2[1]]; + const slopes = getSlopes(point1, point22); + const slopeXY = slopes[0]; + const slopeYZ = slopes[1]; + slopeAtXY.push(slopeXY); + slopeAtYZ.push(slopeYZ); + } + slopesXY.push(slopeAtXY); + slopesYZ.push(slopeAtYZ); + } + for (const finger of Finger.all) { + const pointIndexAt = finger === Finger.thumb ? 1 : 0; + const fingerPointsAt = Finger.getPoints(finger); + const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; + const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; + const endPoint = landmarks[fingerPointsAt[3][1]]; + const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); + const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); + fingerCurls[finger] = fingerCurled; + fingerDirections[finger] = fingerPosition; + } + return { curls: fingerCurls, directions: fingerDirections }; +} +function analyze(keypoints) { + if (!keypoints || keypoints.length === 0) + return null; + const estimatorRes = estimate(keypoints); + const landmarks = {}; + for (const fingerIdx of Finger.all) { + landmarks[Finger.getName(fingerIdx)] = { + curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), + direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) + }; + } + return landmarks; +} +function match(keypoints) { + const poses = []; + if (!keypoints || keypoints.length === 0) + return poses; + const estimatorRes = estimate(keypoints); + for (const gesture2 of fingergesture_default) { + const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); + if (confidence >= minConfidence) + poses.push({ name: gesture2.name, confidence }); + } + return poses; +} + +// src/gesture/gesture.ts +var body2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); + } + } + return gestures; +}; +var face2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 450) { + const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); + const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; + if (Math.abs(zDiff / xDiff) <= 0.15) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); + const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2] || 0; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris2 = (res) => { + var _a, _b, _c, _d; + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > rightIrisCenterX) { + if (leftIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking right" }); + } else { + if (rightIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking left" }); + } + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + if (res[i].annotations) { + for (const [finger, pos] of Object.entries(res[i].annotations)) { + if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + if (res[i].keypoints) { + const poses = match(res[i].keypoints); + for (const pose of poses) + gestures.push({ hand: i, gesture: pose.name }); + } + } + return gestures; +}; + +// src/hand/handposeutil.ts +function getBoxSize2(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; +} +function getBoxCenter2(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box, image28, cropSize) { + const h = image28.shape[1]; + const w = image28.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tfjs_esm_exports.image.cropAndResize(image28, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; +} +function enlargeBox2(box, factor = 1.5) { + const center = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function squarifyBox2(box) { + const centers = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const maxEdge = Math.max(...size2); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/hand/handposeanchors.ts +var anchors2 = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.171875, y: 0.078125 }, + { x: 0.171875, y: 0.078125 }, + { x: 0.203125, y: 0.078125 }, + { x: 0.203125, y: 0.078125 }, + { x: 0.234375, y: 0.078125 }, + { x: 0.234375, y: 0.078125 }, + { x: 0.265625, y: 0.078125 }, + { x: 0.265625, y: 0.078125 }, + { x: 0.296875, y: 0.078125 }, + { x: 0.296875, y: 0.078125 }, + { x: 0.328125, y: 0.078125 }, + { x: 0.328125, y: 0.078125 }, + { x: 0.359375, y: 0.078125 }, + { x: 0.359375, y: 0.078125 }, + { x: 0.390625, y: 0.078125 }, + { x: 0.390625, y: 0.078125 }, + { x: 0.421875, y: 0.078125 }, + { x: 0.421875, y: 0.078125 }, + { x: 0.453125, y: 0.078125 }, + { x: 0.453125, y: 0.078125 }, + { x: 0.484375, y: 0.078125 }, + { x: 0.484375, y: 0.078125 }, + { x: 0.515625, y: 0.078125 }, + { x: 0.515625, y: 0.078125 }, + { x: 0.546875, y: 0.078125 }, + { x: 0.546875, y: 0.078125 }, + { x: 0.578125, y: 0.078125 }, + { x: 0.578125, y: 0.078125 }, + { x: 0.609375, y: 0.078125 }, + { x: 0.609375, y: 0.078125 }, + { x: 0.640625, y: 0.078125 }, + { x: 0.640625, y: 0.078125 }, + { x: 0.671875, y: 0.078125 }, + { x: 0.671875, y: 0.078125 }, + { x: 0.703125, y: 0.078125 }, + { x: 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+ { x: 0.3125, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/hand/handposedetector.ts +var HandDetector = class { + constructor(model23) { + __publicField(this, "model"); + __publicField(this, "anchors"); + __publicField(this, "anchorsTensor"); + __publicField(this, "inputSize"); + __publicField(this, "inputSizeTensor"); + __publicField(this, "doubleInputSizeTensor"); + var _a, _b, _c, _d; + this.model = model23; + this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors); + this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; + this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + const t2 = {}; + t2.boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]); + t2.boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]); + t2.div = tfjs_esm_exports.div(t2.boxOffsets, this.inputSizeTensor); + t2.boxCenterPoints = tfjs_esm_exports.add(t2.div, this.anchorsTensor); + t2.halfBoxSizes = tfjs_esm_exports.div(t2.boxSizes, this.doubleInputSizeTensor); + t2.sub = tfjs_esm_exports.sub(t2.boxCenterPoints, t2.halfBoxSizes); + t2.startPoints = tfjs_esm_exports.mul(t2.sub, this.inputSizeTensor); + t2.add = tfjs_esm_exports.add(t2.boxCenterPoints, t2.halfBoxSizes); + t2.endPoints = tfjs_esm_exports.mul(t2.add, this.inputSizeTensor); + const res = tfjs_esm_exports.concat2d([t2.startPoints, t2.endPoints], 1); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return res; + } + normalizeLandmarks(rawPalmLandmarks, index2) { + const t2 = {}; + t2.reshape = tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]); + t2.div = tfjs_esm_exports.div(t2.reshape, this.inputSizeTensor); + t2.landmarks = tfjs_esm_exports.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = tfjs_esm_exports.mul(t2.landmarks, this.inputSizeTensor); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return res; + } + async predict(input, config3) { + var _a; + const t2 = {}; + t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]); + t2.div = tfjs_esm_exports.div(t2.resize, constants.tf127); + t2.image = tfjs_esm_exports.sub(t2.div, constants.tf1); + t2.batched = this.model.execute(t2.image); + t2.predictions = tfjs_esm_exports.squeeze(t2.batched); + t2.slice = tfjs_esm_exports.slice(t2.predictions, [0, 0], [-1, 1]); + t2.sigmoid = tfjs_esm_exports.sigmoid(t2.slice); + t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); + const scores = await t2.scores.data(); + t2.boxes = tfjs_esm_exports.slice(t2.predictions, [0, 1], [-1, 4]); + t2.norm = this.normalizeBoxes(t2.boxes); + t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t2.nms.array(); + const hands = []; + for (const index2 of nms) { + const p = {}; + p.box = tfjs_esm_exports.slice(t2.norm, [index2, 0], [1, -1]); + p.slice = tfjs_esm_exports.slice(t2.predictions, [index2, 5], [1, 14]); + p.norm = this.normalizeLandmarks(p.slice, index2); + p.palmLandmarks = tfjs_esm_exports.reshape(p.norm, [-1, 2]); + const box = await p.box.data(); + const startPoint = box.slice(0, 2); + const endPoint = box.slice(2, 4); + const palmLandmarks = await p.palmLandmarks.array(); + const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; + const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); + hands.push(scaled); + Object.keys(p).forEach((tensor6) => tfjs_esm_exports.dispose(p[tensor6])); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return hands; + } +}; + +// src/hand/handposepipeline.ts +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var lastTime13 = 0; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + __publicField(this, "handDetector"); + __publicField(this, "handPoseModel"); + __publicField(this, "inputSize"); + __publicField(this, "storedBoxes"); + __publicField(this, "skipped"); + __publicField(this, "detectedHands"); + var _a, _b, _c; + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; + this.storedBoxes = []; + this.skipped = Number.MAX_SAFE_INTEGER; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box2, angle, rotationMatrix) { + const boxSize = getBoxSize2(box2); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box2), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image28, config3) { + let useFreshBox = false; + let boxes; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; + const skipFrame = this.skipped < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + boxes = await this.handDetector.predict(image28, config3); + this.skipped = 0; + } + if (config3.skipAllowed) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tfjs_esm_exports.div(croppedInput, constants.tf255); + tfjs_esm_exports.dispose(croppedInput); + tfjs_esm_exports.dispose(rotatedImage); + const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); + lastTime13 = now(); + tfjs_esm_exports.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tfjs_esm_exports.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence / 4) { + const keypointsReshaped = tfjs_esm_exports.reshape(keypoints, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tfjs_esm_exports.dispose(keypoints); + tfjs_esm_exports.dispose(keypointsReshaped); + const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords, + confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tfjs_esm_exports.dispose(keypoints); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: 0, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, + landmarks: [] + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + if (hands.length > config3.hand.maxDetected) + hands.length = config3.hand.maxDetected; + return hands; + } +}; + +// src/hand/handpose.ts +var meshAnnotations2 = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palm: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict14(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations2 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations2)) { + annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); + } + } + const keypoints = predictions[i].landmarks; + let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw = [0, 0, 0, 0]; + if (keypoints && keypoints.length > 0) { + for (const pt of keypoints) { + if (pt[0] < box[0]) + box[0] = pt[0]; + if (pt[1] < box[1]) + box[1] = pt[1]; + if (pt[0] > box[2]) + box[2] = pt[0]; + if (pt[1] > box[3]) + box[3] = pt[1]; + } + box[2] -= box[0]; + box[3] -= box[1]; + boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; + } else { + box = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + const landmarks = analyze(keypoints); + hands.push({ + id: i, + score: Math.round(100 * predictions[i].confidence) / 100, + boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, + fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, + label: "hand", + box, + boxRaw, + keypoints, + annotations: annotations2, + landmarks + }); + } + return hands; +} +async function load15(config3) { + var _a, _b; + if (env.initial) { + handDetectorModel = null; + handPoseModel = null; + } + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, + config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null + ]); + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; + if (handDetector && handPoseModel) + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/hand/handtrack.ts +var models2 = [null, null]; +var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; +var inputSize7 = [[0, 0], [0, 0]]; +var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; +var faceIndex = 4; +var boxExpandFact = 1.6; +var maxDetectorResolution = 512; +var detectorExpandFact = 1.4; +var skipped13 = Number.MAX_SAFE_INTEGER; +var lastTime14 = 0; +var outputSize = [0, 0]; +var cache4 = { + boxes: [], + hands: [] +}; +var fingerMap = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + base: [0], + palm: [0, 17, 13, 9, 5, 1, 0] +}; +async function loadDetect2(config3) { + var _a; + if (env.initial) + models2[0] = null; + if (!models2[0]) { + fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); + models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); + const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; + inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[0]["modelUrl"]); + return models2[0]; +} +async function loadSkeleton(config3) { + var _a; + if (env.initial) + models2[1] = null; + if (!models2[1]) { + models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); + const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; + inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[1]["modelUrl"]); + return models2[1]; +} +async function detectHands(input, config3) { + const hands = []; + if (!input || !models2[0]) + return hands; + const t2 = {}; + const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); + const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); + const width = Math.round(height * ratio2 / 8) * 8; + t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [height, width]); + t2.cast = tfjs_esm_exports.cast(t2.resize, "int32"); + [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); + t2.boxes = tfjs_esm_exports.squeeze(t2.rawBoxes, [0, 2]); + t2.scores = tfjs_esm_exports.squeeze(t2.rawScores, [0]); + const classScores = tfjs_esm_exports.unstack(t2.scores, 1); + tfjs_esm_exports.dispose(classScores[faceIndex]); + classScores.splice(faceIndex, 1); + t2.filtered = tfjs_esm_exports.stack(classScores, 1); + tfjs_esm_exports.dispose(classScores); + t2.max = tfjs_esm_exports.max(t2.filtered, 1); + t2.argmax = tfjs_esm_exports.argMax(t2.filtered, 1); + let id = 0; + t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t2.nms.data(); + const scores = await t2.max.data(); + const classNum = await t2.argmax.data(); + for (const nmsIndex of Array.from(nms)) { + const boxSlice = tfjs_esm_exports.slice(t2.boxes, nmsIndex, 1); + const boxYX = await boxSlice.data(); + tfjs_esm_exports.dispose(boxSlice); + const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; + const boxRaw = scale(boxData, detectorExpandFact); + const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; + const score = scores[nmsIndex]; + const label = classes[classNum[nmsIndex]]; + const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; + hands.push(hand3); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + hands.sort((a, b) => b.score - a.score); + if (hands.length > (config3.hand.maxDetected || 1)) + hands.length = config3.hand.maxDetected || 1; + return hands; +} +async function detectFingers(input, h, config3) { + const hand3 = { + id: h.id, + score: Math.round(100 * h.score) / 100, + boxScore: Math.round(100 * h.score) / 100, + fingerScore: 0, + box: h.box, + boxRaw: h.boxRaw, + label: h.label, + keypoints: [], + landmarks: {}, + annotations: {} + }; + if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { + const t2 = {}; + const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; + t2.crop = tfjs_esm_exports.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t2.div = tfjs_esm_exports.div(t2.crop, constants.tf255); + [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); + const rawScore = (await t2.score.data())[0]; + const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; + if (score >= (config3.hand.minConfidence || 0)) { + hand3.fingerScore = score; + t2.reshaped = tfjs_esm_exports.reshape(t2.keypoints, [-1, 3]); + const coordsData = await t2.reshaped.array(); + const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); + const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); + hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); + hand3.landmarks = analyze(hand3.keypoints); + for (const key of Object.keys(fingerMap)) { + hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); + } + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + } + return hand3; +} +async function predict15(input, config3) { + var _a, _b; + if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) + return []; + outputSize = [input.shape[2] || 0, input.shape[1] || 0]; + skipped13++; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrame = skipped13 < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache4.hands; + } + return new Promise(async (resolve) => { + const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); + if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else { + cache4.boxes = await detectHands(input, config3); + lastTime14 = now(); + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + skipped13 = 0; + } + const oldCache = [...cache4.boxes]; + cache4.boxes.length = 0; + if (config3.cacheSensitivity > 0) { + for (let i = 0; i < cache4.hands.length; i++) { + const boxKpt = square(cache4.hands[i].keypoints, outputSize); + if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { + const boxScale = scale(boxKpt.box, boxExpandFact); + const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); + cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); + } + } + } + for (let i = 0; i < cache4.hands.length; i++) { + const bbox = calc(cache4.hands[i].keypoints, outputSize); + cache4.hands[i].box = bbox.box; + cache4.hands[i].boxRaw = bbox.boxRaw; + } + resolve(cache4.hands); + }); +} + +// src/result.ts +var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); + +// src/body/movenetcoords.ts +var movenetcoords_exports = {}; +__export(movenetcoords_exports, { + connected: () => connected3, + horizontal: () => horizontal, + kpt: () => kpt3, + relative: () => relative, + vertical: () => vertical +}); +var kpt3 = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var horizontal = [ + ["leftEye", "rightEye"], + ["leftEar", "rightEar"], + ["leftShoulder", "rightShoulder"], + ["leftElbow", "rightElbow"], + ["leftWrist", "rightWrist"], + ["leftHip", "rightHip"], + ["leftKnee", "rightKnee"], + ["leftAnkle", "rightAnkle"] +]; +var vertical = [ + ["leftKnee", "leftShoulder"], + ["rightKnee", "rightShoulder"], + ["leftAnkle", "leftKnee"], + ["rightAnkle", "rightKnee"] +]; +var relative = [ + [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], + [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] +]; +var connected3 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/util/interpolate.ts +var bufferedResult = empty(); +var interpolateTime = 0; +function calc2(newResult, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + const t0 = now(); + if (!newResult) + return empty(); + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; + if (newResult.canvas) + bufferedResult.canvas = newResult.canvas; + if (newResult.error) + bufferedResult.error = newResult.error; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { + var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + return { + score: newKpt.score, + part: newKpt.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + ], + distance: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + ] + }; + }); + const annotations2 = {}; + let coords = { connected: {} }; + if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) + coords = efficientposecoords_exports; + else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + coords = blazeposecoords_exports; + else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) + coords = movenetcoords_exports; + for (const [name, indexes] of Object.entries(coords.connected)) { + const pt = []; + for (let j = 0; j < indexes.length - 1; j++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[j]); + const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) + bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + let annotations2 = {}; + if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { + bufferedResult.hand[i].annotations = newResult.hand[i].annotations; + annotations2 = bufferedResult.hand[i].annotations; + } else if (newResult.hand[i].annotations) { + for (const key of Object.keys(newResult.hand[i].annotations)) { + annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + } + } + bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + if (newResult.face[i].rotation) { + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; + } else { + bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; + } + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + bufferedResult.width = newResult.width; + bufferedResult.height = newResult.height; + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); + if (newResult.performance) + bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; + return bufferedResult; +} + +// src/segmentation/meet.ts +var model17; +async function load16(config3) { + if (!model17 || env.initial) + model17 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model17["modelUrl"]); + return model17; +} +async function predict16(input, config3) { + var _a; + if (!model17) + model17 = await load16(config3); + if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); + t2.res = model17.execute(t2.norm); + t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); + [t2.bgRaw, t2.fgRaw] = tfjs_esm_exports.unstack(t2.squeeze, 2); + t2.fg = tfjs_esm_exports.softmax(t2.fgRaw); + t2.mul = tfjs_esm_exports.mul(t2.fg, constants.tf255); + t2.expand = tfjs_esm_exports.expandDims(t2.mul, 2); + t2.output = tfjs_esm_exports.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tfjs_esm_exports.squeeze(input); + t2.concat = tfjs_esm_exports.concat([t2.input, t2.output], -1); + rgba = tfjs_esm_exports.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tfjs_esm_exports.cast(t2.output, "int32"); + break; + default: + rgba = tfjs_esm_exports.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return rgba; +} + +// src/face/match.ts +var match_exports = {}; +__export(match_exports, { + distance: () => distance, + find: () => find, + similarity: () => similarity +}); +function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { + if (!descriptor1 || !descriptor1) + return Number.MAX_SAFE_INTEGER; + let sum3 = 0; + for (let i = 0; i < descriptor1.length; i++) { + const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); + sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; + } + return (options4.multiplier || 20) * sum3; +} +var normalizeDistance = (dist, order, min2, max5) => { + if (dist === 0) + return 1; + const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); + const norm = (1 - root / 100 - min2) / (max5 - min2); + const clamp2 = Math.max(Math.min(norm, 1), 0); + return clamp2; +}; +function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { + const dist = distance(descriptor1, descriptor2, options4); + return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); +} +function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { + if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { + return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; + } + let lowestDistance = Number.MAX_SAFE_INTEGER; + let index2 = -1; + for (let i = 0; i < descriptors.length; i++) { + const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; + if (res < lowestDistance) { + lowestDistance = res; + index2 = i; + } + if (lowestDistance < (options4.threshold || 0)) + break; + } + const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); + return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; +} + +// src/models.ts +var models_exports2 = {}; +__export(models_exports2, { + Models: () => Models, + validateModel: () => validateModel +}); + +// src/body/movenetfix.ts +var maxJitter = 5e-3; +var cache5 = { + keypoints: [], + padding: [[0, 0], [0, 0], [0, 0], [0, 0]] +}; +function bodyParts(body4) { + for (const pair of horizontal) { + const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); + if (body4.keypoints[left] && body4.keypoints[right]) { + if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } + } + for (const pair of vertical) { + const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + if (body4.keypoints[lower] && body4.keypoints[higher]) { + if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { + body4.keypoints.splice(lower, 1); + } + } + } + for (const [pair, compare2] of relative) { + const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); + const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); + if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) + continue; + const distanceLeft = body4.keypoints[left] ? [ + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) + ] : [0, 0]; + const distanceRight = body4.keypoints[right] ? [ + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) + ] : [0, 0]; + if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } +} +function jitter(keypoints) { + for (let i = 0; i < keypoints.length; i++) { + if (keypoints[i] && cache5.keypoints[i]) { + const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; + if (diff[0] < maxJitter && diff[1] < maxJitter) { + keypoints[i] = cache5.keypoints[i]; + } else { + cache5.keypoints[i] = keypoints[i]; + } + } else { + cache5.keypoints[i] = keypoints[i]; + } + } + return keypoints; +} +function padInput(input, inputSize10) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + cache5.padding = [ + [0, 0], + [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], + [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], + [0, 0] + ]; + t2.pad = tfjs_esm_exports.pad(input, cache5.padding); + t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); + const final = tfjs_esm_exports.cast(t2.resize, "int32"); + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return final; +} +function rescaleBody(body4, outputSize2) { + body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); + for (const kpt4 of body4.keypoints) { + kpt4.position = [ + kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], + kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] + ]; + kpt4.positionRaw = [ + kpt4.position[0] / outputSize2[0], + kpt4.position[1] / outputSize2[1] + ]; + } + const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); + body4.box = rescaledBoxes.box; + body4.boxRaw = rescaledBoxes.boxRaw; + return body4; +} + +// src/body/movenet.ts +var model18; +var inputSize8 = 0; +var skipped14 = Number.MAX_SAFE_INTEGER; +var cache6 = { + boxes: [], + bodies: [], + last: 0 +}; +async function load17(config3) { + var _a; + if (env.initial) + model18 = null; + if (!model18) { + fakeOps(["size"], config3); + model18 = await loadModel(config3.body.modelPath); + } else if (config3.debug) + log("cached model:", model18["modelUrl"]); + inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; + if (inputSize8 < 64) + inputSize8 = 256; + return model18; +} +function parseSinglePose(res, config3, image28) { + const kpt4 = res[0][0]; + const keypoints = []; + let score = 0; + for (let id = 0; id < kpt4.length; id++) { + score = kpt4[id][2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[id][1], kpt4[id][0]]; + keypoints.push({ + score: Math.round(100 * score) / 100, + part: kpt3[id], + positionRaw, + position: [ + Math.round((image28.shape[2] || 0) * positionRaw[0]), + Math.round((image28.shape[1] || 0) * positionRaw[1]) + ] + }); + } + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const bodies = []; + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + return bodies; +} +function parseMultiPose(res, config3, image28) { + const bodies = []; + for (let id = 0; id < res[0].length; id++) { + const kpt4 = res[0][id]; + const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; + if (totalScore > config3.body.minConfidence) { + const keypoints = []; + for (let i = 0; i < 17; i++) { + const score = kpt4[3 * i + 2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; + keypoints.push({ + part: kpt3[i], + score: Math.round(100 * score) / 100, + positionRaw, + position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] + }); + } + } + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + } + } + bodies.sort((a, b) => b.score - a.score); + if (bodies.length > config3.body.maxDetected) + bodies.length = config3.body.maxDetected; + return bodies; +} +async function predict17(input, config3) { + var _a; + if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) + return []; + if (!config3.skipAllowed) + cache6.boxes.length = 0; + skipped14++; + const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; + const skipFrame = skipped14 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache6.bodies; + } + return new Promise(async (resolve) => { + const t2 = {}; + skipped14 = 0; + t2.input = padInput(input, inputSize8); + t2.res = model18 == null ? void 0 : model18.execute(t2.input); + cache6.last = now(); + const res = await t2.res.array(); + cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + for (const body4 of cache6.bodies) { + rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); + jitter(body4.keypoints); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + resolve(cache6.bodies); + }); +} + +// src/object/nanodet.ts +var model19; +var last10 = []; +var lastTime15 = 0; +var skipped15 = Number.MAX_SAFE_INTEGER; +var inputSize9 = 0; +var scaleBox = 2.5; +async function load18(config3) { + if (!model19 || env.initial) { + model19 = await loadModel(config3.object.modelPath); + const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; + inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; + } else if (config3.debug) + log("cached model:", model19["modelUrl"]); + return model19; +} +async function process4(res, outputShape, config3) { + var _a, _b; + let id = 0; + let results = []; + const size2 = inputSize9; + for (const strideSize of [1, 2, 4]) { + const baseSize = strideSize * 13; + const scoresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scores = await scoresT.array(); + const featuresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const boxesMaxT = tfjs_esm_exports.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); + const boxIdxT = tfjs_esm_exports.argMax(boxesMaxT, 2); + const boxIdx = await boxIdxT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { + const score = scores[i][j]; + if (score > (config3.object.minConfidence || 0) && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw = [x, y, w, h]; + boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); + const box = [ + boxRaw[0] * outputShape[0], + boxRaw[1] * outputShape[1], + boxRaw[2] * outputShape[0], + boxRaw[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score) / 100, + class: j + 1, + label: labels2[j].label, + box: box.map((a) => Math.trunc(a)), + boxRaw + }; + results.push(result); + } + } + } + tfjs_esm_exports.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); + } + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = Array.from(await nms.data()); + tfjs_esm_exports.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict18(image28, config3) { + if (!(model19 == null ? void 0 : model19["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; + const skipFrame = skipped15 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { + skipped15++; + return last10; + } + skipped15 = 0; + if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) + return last10; + return new Promise(async (resolve) => { + const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; + const resizeT = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize9, inputSize9], false); + const normT = tfjs_esm_exports.div(resizeT, constants.tf255); + const transposeT = tfjs_esm_exports.transpose(normT, [0, 3, 1, 2]); + let objectT; + if (config3.object.enabled) + objectT = model19.execute(transposeT); + lastTime15 = now(); + const obj = await process4(objectT, outputSize2, config3); + last10 = obj; + tfjs_esm_exports.dispose([resizeT, normT, transposeT, ...objectT]); + resolve(obj); + }); +} + +// src/body/posenetutils.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; +function getBoundingBox(keypoints) { + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score, part, position }) => ({ + score, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })), + annotations: {} + }); + const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + __publicField(this, "priorityQueue"); + __publicField(this, "numberOfElements"); + __publicField(this, "getElementValue"); + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max5 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max5; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t2 = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t2; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min2, max5) { + if (a < min2) + return min2; + if (a > max5) + return max5; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/body/posenet.ts +var model20; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors( + { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, + { x: offsetPoint.x, y: offsetPoint.y } + ); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints; +} +function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence2, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < minConfidence2) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) + queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses, { x, y }, keypointId) { + return poses.some(({ keypoints }) => { + var _a; + const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints) { + const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { + const poses = []; + const queue = buildPartWithScoreQueue(minConfidence2, scores); + while (poses.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses, rootImageCoords, root.part.id)) + continue; + let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints = keypoints.filter((a) => a.score > minConfidence2); + const score = getInstanceScore(poses, keypoints); + const box = getBoundingBox(keypoints); + if (score > minConfidence2) + poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); + } + return poses; +} +async function predict19(input, config3) { + if (!(model20 == null ? void 0 : model20["executor"])) + return []; + const res = tfjs_esm_exports.tidy(() => { + if (!model20.inputs[0].shape) + return []; + const resized = tfjs_esm_exports.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1); + const results = model20.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0])); + results3d[1] = tfjs_esm_exports.sigmoid(results3d[1]); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); + for (const t2 of res) + tfjs_esm_exports.dispose(t2); + const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model20.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + return scaled; +} +async function load19(config3) { + if (!model20 || env.initial) + model20 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model20["modelUrl"]); + return model20; +} + +// src/segmentation/rvm.ts +var model21; +var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; +var t = {}; +var ratio = 0; +function init3(config3) { + tfjs_esm_exports.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + t.r1i = tfjs_esm_exports.tensor(0); + t.r2i = tfjs_esm_exports.tensor(0); + t.r3i = tfjs_esm_exports.tensor(0); + t.r4i = tfjs_esm_exports.tensor(0); + ratio = config3.segmentation.ratio || 0.5; + t.downsample_ratio = tfjs_esm_exports.tensor(ratio); +} +async function load20(config3) { + if (!model21 || env.initial) + model21 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model21["modelUrl"]); + init3(config3); + return model21; +} +var normalize = (r) => tfjs_esm_exports.tidy(() => { + const squeeze14 = tfjs_esm_exports.squeeze(r, [0]); + const mul15 = tfjs_esm_exports.mul(squeeze14, constants.tf255); + const cast8 = tfjs_esm_exports.cast(mul15, "int32"); + return cast8; +}); +function getRGBA(fgr, pha) { + const rgb2 = fgr ? normalize(fgr) : tfjs_esm_exports.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : tfjs_esm_exports.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = tfjs_esm_exports.concat([rgb2, a], -1); + tfjs_esm_exports.dispose([rgb2, a]); + return rgba; +} +function getState(state) { + return tfjs_esm_exports.tidy(() => { + const r = {}; + r.unstack = tfjs_esm_exports.unstack(state, -1); + r.concat = tfjs_esm_exports.concat(r.unstack, 1); + r.split = tfjs_esm_exports.split(r.concat, 4, 1); + r.stack = tfjs_esm_exports.concat(r.split, 2); + r.squeeze = tfjs_esm_exports.squeeze(r.stack, [0]); + r.expand = tfjs_esm_exports.expandDims(r.squeeze, -1); + r.add = tfjs_esm_exports.add(r.expand, 1); + r.mul = tfjs_esm_exports.mul(r.add, 127.5); + r.cast = tfjs_esm_exports.cast(r.mul, "int32"); + r.tile = tfjs_esm_exports.tile(r.cast, [1, 1, 3]); + r.alpha = tfjs_esm_exports.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return tfjs_esm_exports.concat([r.tile, r.alpha], -1); + }); +} +async function predict20(input, config3) { + if (!model21) + model21 = await load20(config3); + if (!(model21 == null ? void 0 : model21["executor"])) + return null; + t.src = tfjs_esm_exports.div(input, 255); + if (ratio !== config3.segmentation.ratio) + init3(config3); + const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + rgba = getRGBA(fgr, pha); + break; + case "alpha": + rgba = getRGBA(null, pha); + break; + case "foreground": + rgba = getRGBA(fgr, null); + break; + case "state": + rgba = getState(r1o); + break; + default: + rgba = tfjs_esm_exports.tensor(0); + } + tfjs_esm_exports.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; + return rgba; +} + +// src/segmentation/selfie.ts +var model22; +async function load21(config3) { + if (!model22 || env.initial) + model22 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model22["modelUrl"]); + return model22; +} +async function predict21(input, config3) { + var _a; + if (!model22) + model22 = await load21(config3); + if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); + t2.res = model22.execute(t2.norm); + t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); + t2.alpha = tfjs_esm_exports.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t2.mul = tfjs_esm_exports.mul(t2.alpha, constants.tf255); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tfjs_esm_exports.squeeze(input); + t2.concat = tfjs_esm_exports.concat([t2.input, t2.mul], -1); + rgba = tfjs_esm_exports.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tfjs_esm_exports.cast(t2.mul, "int32"); + break; + default: + rgba = tfjs_esm_exports.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); + return rgba; +} + +// src/models.ts +function validateModel(instance, model23, name) { + var _a, _b; + if (!model23) + return null; + if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) + return null; + const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; + const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; + const ops = []; + const missing = []; + const url = model23["modelUrl"]; + const executor = model23["executor"]; + if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { + for (const kernel of Object.values(executor.graph.nodes)) { + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); + } + } else { + if (!executor && instance.config.debug) { + log("model not loaded", name); + } + } + for (const op of ops) { + if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); + } + } + if (instance.config.debug && missing.length > 0) + log("model validation failed:", name, missing); + return missing.length > 0 ? { name, missing, ops, url } : null; +} +var Models = class { + constructor(currentInstance) { + __publicField(this, "instance"); + __publicField(this, "models", {}); + this.models = {}; + this.instance = currentInstance; + } + stats() { + let totalSizeFromManifest = 0; + let totalSizeWeights = 0; + let totalSizeLoading = 0; + for (const m of Object.values(modelStats)) { + totalSizeFromManifest += m.sizeFromManifest; + totalSizeWeights += m.sizeLoadedWeights; + totalSizeLoading += m.sizeDesired; + } + const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; + return { + numLoadedModels: Object.values(modelStats).length, + numDefinedModels: Object.keys(this.models).length, + percentageLoaded, + totalSizeFromManifest, + totalSizeWeights, + totalSizeLoading, + modelStats: Object.values(modelStats) + }; + } + reset() { + for (const model23 of Object.keys(this.models)) + this.models[model23] = null; + } + async load(instance) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; + if (env.initial) + this.reset(); + if (instance) + this.instance = instance; + const m = {}; + m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; + m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; + m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; + m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; + m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; + m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; + m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; + m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; + m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; + m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; + m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; + m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; + m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; + m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; + m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; + m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; + m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; + if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) + [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; + m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; + m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; + m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; + m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; + for (const [model23, promise] of Object.entries(m)) { + if (promise == null ? void 0 : promise["then"]) + promise["then"]((val) => this.models[model23] = val); + } + await Promise.all(Object.values(m)); + } + list() { + const models3 = Object.keys(this.models).map((model23) => { + var _a; + return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; + }); + for (const m of models3) { + const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); + if (!stats) + continue; + m.size = modelStats[stats].sizeLoadedWeights; + m.url = modelStats[stats].url; + } + return models3; + } + loaded() { + const list = this.list(); + const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); + return loaded; + } + validate() { + const missing = []; + for (const defined of Object.keys(this.models)) { + const model23 = this.models[defined]; + if (!model23) + continue; + const res = validateModel(this.instance, model23, defined); + if (res) + missing.push(res); + } + return missing; + } +}; + +// src/util/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f; + let id = 0; + const persons = []; + for (const face4 of faces) { + const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture2 of gestures) { + if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) + person2.gestures.push(gesture2); + } + const x = []; + const y = []; + const extractXY = (box) => { + if (box && box.length === 4) { + x.push(box[0], box[0] + box[2]); + y.push(box[1], box[1] + box[3]); + } + }; + extractXY(person2.face.box); + extractXY((_d = person2.body) == null ? void 0 : _d.box); + extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); + extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons.push(person2); + } + return persons; +} + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -269,7 +14030,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,ot=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -837,4 +14599,563 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;async function aA(e){let t=(s,A="application/octet-stream")=>fetch(`data:${A};base64,${s}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(nt);break;case"body":case"full":n=await 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Cn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await fn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Dn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return Q5(this.config,t,n)}async init(){await $e(this,!0),await this.tf.ready(),lt()}async load(t){this.state="load";let n=T(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await $e(this,!1)||h("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(T()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return yn(t,this.config)}async warmup(t){let n=T(),o=await Gn(this,t),s=T();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,P,v,p,b,j,k,N,B,q,W,Z,J,R,D,U,H,a0,o0,C,F;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=v0(this,rt).call(this,t);A&&(h(A,t),this.emit("error"),o(ee(A)));let a=T();await this.load(),s=T(),this.state="image";let i=await y2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(ee("could not convert input to tensor"));return}this.emit("image"),s=T(),this.config.skipAllowed=await J5(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?A5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=T(),c=this.config.face.enabled?await A5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?j5(i.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?d=this.config.body.enabled?Mt(i.tensor,f):[]:(v=this.config.body.modelPath)!=null&&v.includes("efficientpose")?d=this.config.body.enabled?jt(i.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?M5(i.tensor,f):[]),this.performance.body&&delete this.performance.body):(s=T(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?d=this.config.body.enabled?await j5(i.tensor,f):[]:(j=this.config.body.modelPath)!=null&&j.includes("blazepose")?d=this.config.body.enabled?await Mt(i.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await jt(i.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(d=this.config.body.enabled?await M5(i.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((q=(B=this.config.hand.detector)==null?void 0:B.modelPath)!=null&&q.includes("handdetect")?y=this.config.hand.enabled?d5(i.tensor,x):[]:(Z=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&Z.includes("handtrack")&&(y=this.config.hand.enabled?f5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=T(),(R=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&R.includes("handdetect")?y=this.config.hand.enabled?await d5(i.tensor,x):[]:(U=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&U.includes("handtrack")&&(y=this.config.hand.enabled?await f5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((H=this.config.object.modelPath)!=null&&H.includes("nanodet")?l=this.config.object.enabled?k5(i.tensor,this.config):[]:(a0=this.config.object.modelPath)!=null&&a0.includes("centernet")&&(l=this.config.object.enabled?wt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=T(),(o0=this.config.object.modelPath)!=null&&o0.includes("nanodet")?l=this.config.object.enabled?await k5(i.tensor,this.config):[]:(C=this.config.object.modelPath)!=null&&C.includes("centernet")&&(l=this.config.object.enabled?await wt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(s=T(),u=[...G3(c),...H3(d),...Z3(y),...V3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(T()-a):Math.trunc(T()-a);let m=((F=this.process.tensor)==null?void 0:F.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Hn(c,d,y,u,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(v0(this,he)[t.id]||(this.config.debug&&h("video start",t.id),v0(this,he)[t.id]=!0),!t.paused&&v0(this,he)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),v0(this,he)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),v0(this,he)[t.id]=!1)}};Ue=new WeakMap,i2=new WeakMap,l2=new WeakMap,rt=new WeakMap,he=new WeakMap;export{m2 as Env,Vn as Human,Vn as default,Le as defaults,bt as draw,ee as empty,M as env,T5 as match,Bn as models}; +2Q==`; + +// src/warmup.ts +async function warmupBitmap(instance) { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (instance.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "body": + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await instance.detect(bitmap, instance.config); + bitmap.close(); + } + return res; +} +async function warmupCanvas(instance) { + return new Promise((resolve) => { + let src; + switch (instance.config.warmup) { + case "face": + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + src = "data:image/jpeg;base64," + body3; + break; + default: + src = ""; + } + let img; + if (typeof Image !== "undefined") + img = new Image(); + else if (env.Image) + img = new env.Image(); + else { + resolve(void 0); + return; + } + img.onload = async () => { + const canvas3 = canvas(img.naturalWidth, img.naturalHeight); + if (!canvas3) { + log("Warmup: Canvas not found"); + resolve(void 0); + } else { + const ctx = canvas3.getContext("2d"); + if (ctx) + ctx.drawImage(img, 0, 0); + const tensor6 = await instance.image(canvas3, true); + const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; + resolve(res); + } + }; + if (src) + img.src = src; + else + resolve(void 0); + }); +} +async function warmupNode(instance) { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (instance.config.warmup === "face") + img = atob(face3); + else + img = atob(body3); + let res; + if ("node" in tfjs_esm_exports && tfjs_esm_exports.getBackend() === "tensorflow") { + const data = tfjs_esm_exports["node"].decodeJpeg(img); + const expanded = tfjs_esm_exports.expandDims(data, 0); + instance.tf.dispose(data); + res = await instance.detect(expanded, instance.config); + instance.tf.dispose(expanded); + } else { + if (instance.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; +} +async function runInference(instance) { + let res; + if (typeof createImageBitmap === "function") + res = await warmupBitmap(instance); + else if (typeof Image !== "undefined" || env.Canvas !== void 0) + res = await warmupCanvas(instance); + else + res = await warmupNode(instance); + return res; +} +async function runCompile(instance) { + var _a, _b, _c, _d; + if (!tfjs_esm_exports.env().flagRegistry.ENGINE_COMPILE_ONLY) + return; + const backendType = tfjs_esm_exports.getBackend(); + const webGLBackend = tfjs_esm_exports.backend(); + if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { + return; + } + tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", true); + const numTensorsStart = tfjs_esm_exports.engine().state.numTensors; + const compiledModels = []; + for (const [modelName, model23] of Object.entries(instance.models.models)) { + if (!model23) + continue; + const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; + const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; + for (let dim = 0; dim < shape.length; dim++) { + if (shape[dim] === -1) + shape[dim] = dim === 0 ? 1 : 64; + } + const tensor6 = tfjs_esm_exports.zeros(shape, dtype); + try { + const res = model23.execute(tensor6); + compiledModels.push(modelName); + if (Array.isArray(res)) + res.forEach((t2) => tfjs_esm_exports.dispose(t2)); + else + tfjs_esm_exports.dispose(res); + } catch (e) { + if (instance.config.debug) + log("compile fail model:", modelName); + } + tfjs_esm_exports.dispose(tensor6); + } + const kernels = await webGLBackend["checkCompileCompletionAsync"](); + webGLBackend["getUniformLocations"](); + if (instance.config.debug) + log("compile pass:", { models: compiledModels, kernels: kernels.length }); + tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", false); + const numTensorsEnd = tfjs_esm_exports.engine().state.numTensors; + if (numTensorsEnd - numTensorsStart > 0) + log("tensor leak:", numTensorsEnd - numTensorsStart); +} +async function warmup(instance, userConfig) { + await check(instance, false); + const t0 = now(); + instance.state = "warmup"; + if (userConfig) + instance.config = mergeDeep(instance.config, userConfig); + if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { + return empty(); + } + return new Promise(async (resolve) => { + await instance.models.load(); + await runCompile(instance); + const res = await runInference(instance); + const t1 = now(); + if (instance.config.debug) + log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); + instance.emit("warmup"); + resolve(res); + }); +} + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; +var Human = class { + constructor(userConfig) { + __publicField(this, "version"); + __publicField(this, "config"); + __publicField(this, "result"); + __publicField(this, "state"); + __publicField(this, "process"); + __publicField(this, "tf"); + __publicField(this, "env", env); + __publicField(this, "draw", draw_exports); + __publicField(this, "match", match_exports); + __publicField(this, "models"); + __publicField(this, "events"); + __publicField(this, "faceTriangulation"); + __publicField(this, "faceUVMap"); + __publicField(this, "performance"); + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __publicField(this, "analyze", (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }); + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.env.node && !(input instanceof tfjs_esm_exports.Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __publicField(this, "webcam", new WebCam()); + __publicField(this, "emit", (event) => { + var _a; + if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) + this.events.dispatchEvent(new Event(event)); + }); + __privateAdd(this, _loops, {}); + const tfVersion = (version7.tfjs || tfjs_esm_exports.version_core).replace(/-(.*)/, ""); + config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; + config.modelBasePath = env.browser ? "../models/" : "file://models/"; + this.version = version8; + Object.defineProperty(this, "version", { value: version8 }); + this.config = JSON.parse(JSON.stringify(config)); + Object.seal(this.config); + this.config.cacheModels = typeof indexedDB !== "undefined"; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + setModelLoadOptions(this.config); + this.tf = tfjs_esm_exports; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + this.performance = {}; + this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; + this.models = new Models(this); + init2(); + this.result = empty(); + this.process = { tensor: null, canvas: null }; + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + validateModel(this, null, ""); + this.emit("create"); + if (this.config.debug || this.env.browser) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version["tfjs-core"]}`); + const envTemp = JSON.parse(JSON.stringify(this.env)); + delete envTemp.kernels; + delete envTemp.initial; + delete envTemp.perfadd; + if (this.config.debug) + log("environment:", envTemp); + } + reset() { + const currentBackend = this.config.backend; + this.config = JSON.parse(JSON.stringify(config)); + this.config.backend = currentBackend; + reset(); + env.initial = true; + } + validate(userConfig) { + const msgs = validate(config, userConfig || this.config); + if (msgs.length === 0) + this.config = mergeDeep(this.config, userConfig); + return msgs; + } + now() { + return now(); + } + image(input, getTensor = false) { + return process2(input, this.config, getTensor); + } + async segmentation(input, userConfig) { + var _a, _b, _c; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (!this.config.segmentation.enabled) + return null; + const processed = await process2(input, this.config); + if (!processed.tensor) + return null; + let tensor6 = null; + if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) + tensor6 = await predict20(processed.tensor, this.config); + if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) + tensor6 = await predict16(processed.tensor, this.config); + if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) + tensor6 = await predict21(processed.tensor, this.config); + tfjs_esm_exports.dispose(processed.tensor); + return tensor6; + } + compare(firstImageTensor, secondImageTensor) { + return compare(this.config, firstImageTensor, secondImageTensor); + } + async init() { + await check(this, true); + await this.tf.ready(); + reset(); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + const count2 = Object.values(this.models.models).filter((model23) => model23).length; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (this.env.initial) { + if (!await check(this, false)) + log("error: backend check failed"); + await tfjs_esm_exports.ready(); + if (this.env.browser) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("tf flags:", this.tf.ENV.flags); + } + } + await this.models.load(this); + if (this.env.initial && this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + this.env.initial = false; + const loaded = Object.values(this.models.models).filter((model23) => model23).length; + if (loaded !== count2) { + this.models.validate(); + this.emit("load"); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.loadModels || 0)) + this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; + } + next(result = this.result) { + return calc2(result, this.config); + } + async warmup(userConfig) { + const t0 = now(); + const res = await warmup(this, userConfig); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t0); + return res; + } + async profile(input, userConfig) { + const profile = await this.tf.profile(() => this.detect(input, userConfig)); + const kernels = {}; + let total = 0; + for (const kernel of profile.kernels) { + const ms = Number(kernel.kernelTimeMs) || 0; + if (kernels[kernel.name]) + kernels[kernel.name] += ms; + else + kernels[kernel.name] = ms; + total += ms; + } + const kernelArr = []; + Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); + for (const kernel of kernelArr) { + kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; + kernel.time = Math.round(1e3 * kernel.time) / 1e3; + } + kernelArr.sort((a, b) => b.time - a.time); + kernelArr.length = 20; + return kernelArr; + } + async detect(input, userConfig) { + this.state = "detect"; + return new Promise(async (resolve) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; + this.state = "config"; + let timeStamp; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + this.emit("error"); + resolve(empty(error)); + } + const timeStart = now(); + await this.load(); + timeStamp = now(); + this.state = "image"; + const img = await process2(input, this.config); + this.process = img; + this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (!img.tensor) { + if (this.config.debug) + log("could not convert input to tensor"); + this.emit("error"); + resolve(empty("could not convert input to tensor")); + return; + } + this.emit("image"); + timeStamp = now(); + this.config.skipAllowed = await skip(this.config, img.tensor); + this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; + if (!this.performance.totalFrames) + this.performance.totalFrames = 0; + if (!this.performance.cachedFrames) + this.performance.cachedFrames = 0; + this.performance.totalFrames++; + if (this.config.skipAllowed) + this.performance.cachedFrames++; + this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes = []; + let bodyRes = []; + let handRes = []; + let objectRes = []; + this.state = "detect:face"; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; + this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) + faceRes = await faceRes; + this.analyze("Start Body:"); + this.state = "detect:body"; + const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) + bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; + else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; + else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; + else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) + bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; + if (this.performance.body) + delete this.performance.body; + } else { + timeStamp = now(); + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; + else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; + else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; + else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; + this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + this.state = "detect:hand"; + const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) + handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; + else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) + handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + timeStamp = now(); + if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) + handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; + else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) + handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; + this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + this.state = "detect:object"; + if (this.config.async) { + if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) + objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; + else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) + objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + timeStamp = now(); + if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; + else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) + objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; + this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Object:"); + this.state = "detect:await"; + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + this.state = "detect:gesture"; + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; + if (!this.config.async) + this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); + const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: this.process.canvas, + timestamp: Date.now(), + error: null, + width: shape[2], + height: shape[1], + get persons() { + return join2(faceRes, bodyRes, handRes, gestureRes, shape); + } + }; + tfjs_esm_exports.dispose(img.tensor); + this.emit("detect"); + this.state = "idle"; + resolve(this.result); + }); + } + async sleep(ms) { + return new Promise((resolve) => { + setTimeout(resolve, ms); + }); + } + async video(element, run = true, delay = 0) { + if (run) { + if (!__privateGet(this, _loops)[element.id]) { + if (this.config.debug) + log("video start", element.id); + __privateGet(this, _loops)[element.id] = true; + } + if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) + await this.detect(element); + if (delay > 0) + await this.sleep(delay); + if (__privateGet(this, _loops)[element.id]) + requestAnimationFrame(() => this.video(element, run, delay)); + } else { + if (this.config.debug) + log("video stop", element.id); + __privateGet(this, _loops)[element.id] = false; + } + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_sanity = new WeakMap(); +_loops = new WeakMap(); +export { + Env, + Human, + Human as default, + config as defaults, + draw_exports as draw, + empty, + env, + match_exports as match, + models_exports2 as models +}; diff --git a/dist/human.esm.js b/dist/human.esm.js index 75812379..c2933822 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -6,13 +6,6 @@ var __defProp = Object.defineProperty; var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __require = /* @__PURE__ */ ((x) => typeof require !== "undefined" ? require : typeof Proxy !== "undefined" ? new Proxy(x, { - get: (a, b) => (typeof require !== "undefined" ? require : a)[b] -}) : x)(function(x) { - if (typeof require !== "undefined") - return require.apply(this, arguments); - throw new Error('Dynamic require of "' + x + '" is not supported'); -}); var __export = (target, all2) => { for (var name in all2) __defProp(target, name, { get: all2[name], enumerable: true }); @@ -43,623 +36,618 @@ var __privateSet = (obj, member, value, setter) => { // dist/tfjs.esm.js var tfjs_esm_exports = {}; __export(tfjs_esm_exports, { - Abs: () => gs, - Acos: () => sa, - Acosh: () => aa, - AdadeltaOptimizer: () => Ei, - AdagradOptimizer: () => $i, - AdamOptimizer: () => Ai, - AdamaxOptimizer: () => Ri, + Abs: () => ys, + Acos: () => aa, + Acosh: () => ia, + AdadeltaOptimizer: () => yu, + AdagradOptimizer: () => bu, + AdamOptimizer: () => Cu, + AdamaxOptimizer: () => Su, Add: () => eo, - AddN: () => Mo, - All: () => Lo, - Any: () => Bo, - ArgMax: () => Vo, - ArgMin: () => Za, - Asin: () => ia, - Asinh: () => ua, - Atan: () => pa, - Atan2: () => la, - Atanh: () => ca, - AvgPool: () => zo, - AvgPool3D: () => ip, - AvgPool3DGrad: () => Im, - AvgPoolGrad: () => wm, - BackendWasm: () => Pl, - BatchMatMul: () => Wo, - BatchToSpaceND: () => xs, - Bincount: () => Ja, - BroadcastArgs: () => up, - BroadcastTo: () => wne, + AddN: () => Po, + All: () => Mo, + Any: () => Lo, + ArgMax: () => Bo, + ArgMin: () => ei, + Asin: () => ua, + Asinh: () => pa, + Atan: () => ca, + Atan2: () => ma, + Atanh: () => la, + AvgPool: () => Vo, + AvgPool3D: () => dp, + AvgPool3DGrad: () => vm, + AvgPoolGrad: () => mp, + BackendWasm: () => Ll, + BatchMatMul: () => zo, + BatchToSpaceND: () => bs, + Bincount: () => ti, + BroadcastArgs: () => fp, + BroadcastTo: () => ise, Cast: () => co, - Ceil: () => Uo, + Ceil: () => Wo, ClipByValue: () => lo, - Complex: () => ei, - ComplexAbs: () => pp, - Concat: () => ys, - Conv2D: () => Go, - Conv2DBackpropFilter: () => cp, - Conv2DBackpropInput: () => Ho, - Conv3D: () => lp, - Conv3DBackpropFilterV2: () => vm, - Conv3DBackpropInputV2: () => mp, - Cos: () => qo, + Complex: () => ri, + ComplexAbs: () => hp, + Concat: () => Cs, + Conv2D: () => Uo, + Conv2DBackpropFilter: () => oi, + Conv2DBackpropInput: () => Go, + Conv3D: () => gp, + Conv3DBackpropFilterV2: () => km, + Conv3DBackpropInputV2: () => xp, + Cos: () => Ho, Cosh: () => Ko, - CropAndResize: () => Yo, - Cumprod: () => jo, - Cumsum: () => Xo, - DataStorage: () => Do, - DenseBincount: () => ti, - DepthToSpace: () => Qo, - DepthwiseConv2dNative: () => Zo, - DepthwiseConv2dNativeBackpropFilter: () => dp, - DepthwiseConv2dNativeBackpropInput: () => fp, - Diag: () => hp, - Dilation2D: () => gp, - Dilation2DBackpropFilter: () => bb, - Dilation2DBackpropInput: () => yb, - ENV: () => hb, - Einsum: () => ri, - Elu: () => en, - EluGrad: () => km, + CropAndResize: () => Xo, + Cumprod: () => qo, + Cumsum: () => jo, + DataStorage: () => Fo, + DenseBincount: () => ni, + DepthToSpace: () => Yo, + DepthwiseConv2dNative: () => Qo, + DepthwiseConv2dNativeBackpropFilter: () => yp, + DepthwiseConv2dNativeBackpropInput: () => bp, + Diag: () => si, + Dilation2D: () => ai, + Dilation2DBackpropFilter: () => $b, + Dilation2DBackpropInput: () => _b, + ENV: () => kb, + Einsum: () => ii, + Elu: () => Jo, + EluGrad: () => Nm, Environment: () => Uc, - Equal: () => tn, - Erf: () => ma, - Exp: () => rn, - ExpandDims: () => bs, - Expm1: () => da, - FFT: () => oi, - Fill: () => Cs, - FlipLeftRight: () => on, - Floor: () => nn, - FloorDiv: () => sn, + Equal: () => en, + Erf: () => da, + Exp: () => tn, + ExpandDims: () => Ss, + Expm1: () => fa, + FFT: () => ui, + Fill: () => ws, + FlipLeftRight: () => rn, + Floor: () => on, + FloorDiv: () => nn, FromPixels: () => Zi, - FusedBatchNorm: () => an, - FusedConv2D: () => ho, - FusedDepthwiseConv2D: () => go, - GPGPUContext: () => Fu, - GatherNd: () => un, - GatherV2: () => Ss, - GraphModel: () => ll, - Greater: () => pn, - GreaterEqual: () => cn, - IFFT: () => ni, + FusedBatchNorm: () => sn, + FusedConv2D: () => go, + FusedDepthwiseConv2D: () => xo, + GPGPUContext: () => Lu, + GatherNd: () => an, + GatherV2: () => Is, + GraphModel: () => fl, + Greater: () => un, + GreaterEqual: () => pn, + IFFT: () => pi, Identity: () => mo, - Imag: () => si, - IsFinite: () => fa, - IsInf: () => ha, - IsNan: () => ln, + Imag: () => ci, + IsFinite: () => ha, + IsInf: () => ga, + IsNan: () => cn, KernelBackend: () => Zr, - LRN: () => yp, - LRNGrad: () => Nm, - LeakyRelu: () => mn, - Less: () => dn, - LessEqual: () => fn, - LinSpace: () => xp, - Log: () => hn, - Log1p: () => ga, - LogSoftmax: () => Ine, - LogicalAnd: () => gn, - LogicalNot: () => xn, - LogicalOr: () => xa, - LogicalXor: () => GI, - LowerBound: () => vne, - MathBackendCPU: () => Oi, - MathBackendWebGL: () => Bi, + LRN: () => mi, + LRNGrad: () => Tm, + LeakyRelu: () => ln, + Less: () => mn, + LessEqual: () => dn, + LinSpace: () => li, + Log: () => fn, + Log1p: () => xa, + LogSoftmax: () => use, + LogicalAnd: () => hn, + LogicalNot: () => gn, + LogicalOr: () => xn, + LogicalXor: () => XI, + LowerBound: () => pse, + MathBackendCPU: () => Pi, + MathBackendWebGL: () => Vi, Max: () => yn, MaxPool: () => Cn, - MaxPool3D: () => bp, - MaxPool3DGrad: () => _m, - MaxPoolGrad: () => Tm, - MaxPoolWithArgmax: () => Cp, + MaxPool3D: () => Cp, + MaxPool3DGrad: () => $m, + MaxPoolGrad: () => _m, + MaxPoolWithArgmax: () => Sp, Maximum: () => bn, Mean: () => Sn, Min: () => wn, Minimum: () => In, MirrorPad: () => vn, Mod: () => ya, - MomentumOptimizer: () => Fi, - Multinomial: () => Sp, + MomentumOptimizer: () => wu, + Multinomial: () => wp, Multiply: () => kn, - Neg: () => ws, + Neg: () => vs, NonMaxSuppressionV3: () => Tn, NonMaxSuppressionV4: () => ba, NonMaxSuppressionV5: () => _n, NotEqual: () => Nn, - OP_SCOPE_SUFFIX: () => Lb, - OneHot: () => En, - OnesLike: () => Is, + OP_SCOPE_SUFFIX: () => jb, + OneHot: () => $n, + OnesLike: () => ks, Optimizer: () => wr, - OptimizerConstructors: () => ns, - Pack: () => vs, - PadV2: () => $n, - Pool: () => kne, + OptimizerConstructors: () => cl, + Pack: () => Ns, + PadV2: () => En, + Pool: () => cse, Pow: () => An, Prelu: () => Rn, - Prod: () => Fn, - RMSPropOptimizer: () => Di, - RaggedGather: () => wp, - RaggedRange: () => Ip, - RaggedTensorToTensor: () => vp, - Range: () => ks, - Rank: () => _b, - Real: () => ai, - RealDiv: () => Jo, - Reciprocal: () => Dn, - Reduction: () => Et, + Prod: () => Dn, + RMSPropOptimizer: () => Iu, + RaggedGather: () => Ip, + RaggedRange: () => vp, + RaggedTensorToTensor: () => kp, + Range: () => Ts, + Rank: () => Lb, + Real: () => di, + RealDiv: () => Zo, + Reciprocal: () => Fn, + Reduction: () => $t, Relu: () => On, Relu6: () => Ln, - Reshape: () => Ns, + Reshape: () => _s, ResizeBilinear: () => Mn, - ResizeBilinearGrad: () => $m, + ResizeBilinearGrad: () => Am, ResizeNearestNeighbor: () => Pn, ResizeNearestNeighborGrad: () => Em, Reverse: () => Bn, - RotateWithOffset: () => es, - Round: () => Ca, - Rsqrt: () => Vn, - SGDOptimizer: () => qs, - ScatterNd: () => zn, - SearchSorted: () => ii, - Select: () => Ts, - Selu: () => Xi, - Sigmoid: () => Un, - Sign: () => Yi, - Sin: () => Wn, + RotateWithOffset: () => ts, + Round: () => Vn, + Rsqrt: () => zn, + SGDOptimizer: () => Pa, + ScatterNd: () => Wn, + SearchSorted: () => fi, + Select: () => $s, + Selu: () => Ca, + Sigmoid: () => Gn, + Sign: () => wa, + Sin: () => Un, Sinh: () => Sa, - Slice: () => _s, + Slice: () => Es, Softmax: () => qn, - Softplus: () => Qi, - SpaceToBatchND: () => Es, - SparseFillEmptyRows: () => ui, - SparseReshape: () => wa, - SparseSegmentMean: () => pi, - SparseSegmentSum: () => ci, - SparseToDense: () => li, - SplitV: () => $s, - Sqrt: () => Gn, - Square: () => mi, - SquaredDifference: () => Kn, - Step: () => Ds, - StridedSlice: () => jn, - StringNGrams: () => As, - StringSplit: () => di, - StringToHashBucketFast: () => fi, - Sub: () => Xn, - Sum: () => Hn, - Tan: () => Yn, - Tanh: () => Qn, + Softplus: () => Ia, + SpaceToBatchND: () => As, + SparseFillEmptyRows: () => hi, + SparseReshape: () => va, + SparseSegmentMean: () => gi, + SparseSegmentSum: () => xi, + SparseToDense: () => yi, + SplitV: () => Rs, + Sqrt: () => Hn, + Square: () => bi, + SquaredDifference: () => jn, + Step: () => fo, + StridedSlice: () => Xn, + StringNGrams: () => Ds, + StringSplit: () => Ci, + StringToHashBucketFast: () => Si, + Sub: () => Yn, + Sum: () => Kn, + Tan: () => Qn, + Tanh: () => Zn, Tensor: () => it, TensorBuffer: () => st, Tile: () => to, - TopK: () => Zn, - Transform: () => Jn, + TopK: () => Jn, + Transform: () => es, Transpose: () => ro, - Unique: () => kp, - Unpack: () => Rs, - UnsortedSegmentSum: () => Np, - UpperBound: () => Nne, - Variable: () => va, - WebGPUBackend: () => Ui, - ZerosLike: () => Fs, - _FusedMatMul: () => fo, + Unique: () => Np, + Unpack: () => Fs, + UnsortedSegmentSum: () => Tp, + UpperBound: () => lse, + Variable: () => Na, + WebGPUBackend: () => Gi, + ZerosLike: () => Os, + _FusedMatMul: () => ho, abs: () => Yt, - acos: () => fv, - acosh: () => hv, - add: () => xe, - addN: () => gv, - all: () => xv, - any: () => yv, - argMax: () => bv, - argMin: () => Cv, - asin: () => Sv, - asinh: () => wv, - atan: () => Iv, - atan2: () => vv, - atanh: () => kv, - avgPool: () => td, - avgPool3d: () => _v, - backend: () => Oie, + acos: () => G0, + acosh: () => H0, + add: () => be, + addN: () => K0, + all: () => q0, + any: () => j0, + argMax: () => X0, + argMin: () => Y0, + asin: () => Q0, + asinh: () => Z0, + atan: () => J0, + atan2: () => ev, + atanh: () => tv, + avgPool: () => qm, + avgPool3d: () => nv, + backend: () => Pie, backend_util: () => S, - basicLSTMCell: () => Ev, - batchNorm: () => wi, - batchNorm2d: () => Av, - batchNorm3d: () => Rv, - batchNorm4d: () => Fv, - batchToSpaceND: () => rd, - bincount: () => od, - booleanMaskAsync: () => XG, - broadcastArgs: () => Dv, - broadcastTo: () => Ii, - broadcast_util: () => br, - browser: () => Q0, + basicLSTMCell: () => sv, + batchNorm: () => _i, + batchNorm2d: () => iv, + batchNorm3d: () => uv, + batchNorm4d: () => pv, + batchToSpaceND: () => jm, + bincount: () => Xm, + booleanMaskAsync: () => VG, + broadcastArgs: () => cv, + broadcastTo: () => $i, + broadcast_util: () => yr, + browser: () => gN, buffer: () => le, - cast: () => Ke, - ceil: () => Ov, - clipByValue: () => Pv, - clone: () => Br, + cast: () => je, + ceil: () => lv, + clipByValue: () => mv, + clone: () => Lr, complex: () => Tr, - concat: () => gt, - concat1d: () => Mv, - concat2d: () => Lv, - concat3d: () => Bv, - concat4d: () => Vv, - conv1d: () => zv, - conv2d: () => vi, - conv2dTranspose: () => Wv, - conv3d: () => Uv, - conv3dTranspose: () => Hv, - copyRegisteredKernels: () => Dne, - cos: () => qv, - cosh: () => Kv, - cosineWindow: () => il, - cumprod: () => jv, - cumsum: () => Xv, - customGrad: () => Cr, - denseBincount: () => Yv, - deprecationWarn: () => eC, - depthToSpace: () => Qv, + concat: () => xt, + concat1d: () => dv, + concat2d: () => fv, + concat3d: () => hv, + concat4d: () => gv, + conv1d: () => xv, + conv2d: () => Ei, + conv2dTranspose: () => yv, + conv3d: () => bv, + conv3dTranspose: () => Sv, + copyRegisteredKernels: () => bse, + cos: () => wv, + cosh: () => Iv, + cosineWindow: () => al, + cumprod: () => vv, + cumsum: () => kv, + customGrad: () => br, + denseBincount: () => Nv, + deprecationWarn: () => nC, + depthToSpace: () => Tv, depthwiseConv2d: () => Bp, - deregisterOp: () => xK, - device_util: () => yi, - diag: () => Zv, - dilation2d: () => Jv, - disableDeprecationWarnings: () => vie, - dispose: () => Dt, - disposeVariables: () => kie, - div: () => Ge, - divNoNan: () => ek, - dot: () => tk, - dropout: () => aH, - einsum: () => rk, - elu: () => ad, - enableDebugMode: () => Iie, - enableProdMode: () => wie, - enclosingPowerOfTwo: () => xC, - engine: () => cr, + deregisterOp: () => Mq, + device_util: () => ki, + diag: () => _v, + dilation2d: () => $v, + disableDeprecationWarnings: () => kie, + dispose: () => Ot, + disposeVariables: () => Nie, + div: () => He, + divNoNan: () => Av, + dot: () => Rv, + dropout: () => ZG, + einsum: () => Dv, + elu: () => Jm, + enableDebugMode: () => vie, + enableProdMode: () => Iie, + enclosingPowerOfTwo: () => CC, + engine: () => sr, env: () => O, - equal: () => sd, - erf: () => ok, - euclideanNorm: () => ak, - exp: () => Co, - expandDims: () => Fa, - expm1: () => ik, - eye: () => id, + equal: () => Zm, + erf: () => Fv, + euclideanNorm: () => Mv, + exp: () => So, + expandDims: () => Ra, + expm1: () => Lv, + eye: () => ed, fft: () => zp, - fill: () => Ws, + fill: () => Us, findBackend: () => Fie, - findBackendFactory: () => Die, - floor: () => ud, - floorDiv: () => Jm, - forceHalfFloat: () => L$, - fused: () => yC, - gather: () => pd, - gatherND: () => nH, - gather_util: () => Ym, - getBackend: () => Aie, - getGradient: () => Cb, - getKernel: () => qc, - getKernelsForBackend: () => Am, - getThreadsCount: () => Nee, - gpgpu_util: () => yw, - grad: () => l4, - grads: () => m4, - greater: () => cu, - greaterEqual: () => cd, - ifft: () => hu, - imag: () => Si, - image: () => uq, - inTopKAsync: () => uH, - io: () => Ea, - irfft: () => Fd, - isFinite: () => uk, - isInf: () => pk, - isNaN: () => ck, + findBackendFactory: () => Oie, + floor: () => td, + floorDiv: () => Km, + forceHalfFloat: () => GE, + fused: () => SC, + gather: () => rd, + gatherND: () => YG, + gather_util: () => Wd, + getBackend: () => Rie, + getGradient: () => Eb, + getKernel: () => Kc, + getKernelsForBackend: () => Rm, + getThreadsCount: () => Kee, + gpgpu_util: () => $w, + grad: () => HU, + grads: () => KU, + greater: () => uu, + greaterEqual: () => od, + ifft: () => du, + imag: () => Ri, + image: () => eK, + inTopKAsync: () => eH, + io: () => La, + irfft: () => Td, + isFinite: () => Bv, + isInf: () => Vv, + isNaN: () => zv, keep: () => _r, - kernel_impls: () => Lt, - leakyRelu: () => ld, - less: () => lk, + kernel_impls: () => Vt, + leakyRelu: () => nd, + less: () => Wv, lessEqual: () => Vp, - linalg: () => pq, - linspace: () => mk, - loadGraphModel: () => l6, - loadGraphModelSync: () => m6, - localResponseNormalization: () => dk, + linalg: () => tK, + linspace: () => Uv, + loadGraphModel: () => A6, + loadGraphModelSync: () => R6, + localResponseNormalization: () => Gv, log: () => Da, - log1p: () => md, - logSigmoid: () => fk, - logSoftmax: () => hk, - logSumExp: () => hd, - logicalAnd: () => lu, - logicalNot: () => gd, - logicalOr: () => xd, - logicalXor: () => gk, - losses: () => cq, - lowerBound: () => xk, + log1p: () => sd, + logSigmoid: () => Hv, + logSoftmax: () => Kv, + logSumExp: () => ud, + logicalAnd: () => pu, + logicalNot: () => pd, + logicalOr: () => cd, + logicalXor: () => qv, + losses: () => rK, + lowerBound: () => jv, matMul: () => Xe, - math: () => j0, - max: () => Us, - maxPool: () => bd, - maxPool3d: () => yk, - maxPoolWithArgmax: () => bk, - maximum: () => Cd, - mean: () => mu, - memory: () => Nie, - meshgrid: () => Ck, - min: () => sl, - minimum: () => Sd, - mirrorPad: () => Sk, - mod: () => wk, - moments: () => Ik, - movingAverage: () => QG, - mul: () => ae, - multiRNNCell: () => vk, - multinomial: () => kk, - neg: () => yr, - nextFrame: () => CC, - norm: () => pu, - notEqual: () => wd, - oneHot: () => tl, - ones: () => Gs, - onesLike: () => Nk, + math: () => fN, + max: () => Gs, + maxPool: () => md, + maxPool3d: () => Xv, + maxPoolWithArgmax: () => Yv, + maximum: () => dd, + mean: () => cu, + memory: () => Tie, + meshgrid: () => Qv, + min: () => rl, + minimum: () => fd, + mirrorPad: () => Zv, + mod: () => Jv, + moments: () => ek, + movingAverage: () => UG, + mul: () => ne, + multiRNNCell: () => tk, + multinomial: () => rk, + neg: () => Cr, + nextFrame: () => AC, + norm: () => iu, + notEqual: () => hd, + oneHot: () => nl, + ones: () => Hs, + onesLike: () => ok, op: () => N, - outerProduct: () => Tk, - pad: () => Hs, - pad1d: () => _k, - pad2d: () => Ek, - pad3d: () => $k, - pad4d: () => Ak, - pool: () => Rk, - pow: () => Ra, - prelu: () => vd, - print: () => Gm, - prod: () => Fk, - profile: () => Tie, - raggedGather: () => Dk, - raggedRange: () => Ok, - raggedTensorToTensor: () => Pk, - rand: () => Mk, - randomGamma: () => e1, - randomNormal: () => Ed, - randomStandardNormal: () => t1, - randomUniform: () => $d, - range: () => Ni, - ready: () => $ie, - real: () => $a, - reciprocal: () => r1, - registerBackend: () => Ci, - registerGradient: () => Ane, - registerKernel: () => Ia, - registerOp: () => gK, - relu: () => Ti, - relu6: () => Ad, - removeBackend: () => Rie, - reshape: () => z, + outerProduct: () => nk, + pad: () => Ks, + pad1d: () => sk, + pad2d: () => ak, + pad3d: () => ik, + pad4d: () => uk, + pool: () => pk, + pow: () => Aa, + prelu: () => xd, + print: () => Hm, + prod: () => ck, + profile: () => _ie, + raggedGather: () => lk, + raggedRange: () => mk, + raggedTensorToTensor: () => dk, + rand: () => fk, + randomGamma: () => Ok, + randomNormal: () => Id, + randomStandardNormal: () => Pk, + randomUniform: () => vd, + range: () => Di, + ready: () => Aie, + real: () => Fa, + reciprocal: () => Mk, + registerBackend: () => Ti, + registerGradient: () => gse, + registerKernel: () => ka, + registerOp: () => Pq, + relu: () => Fi, + relu6: () => kd, + removeBackend: () => Die, + reshape: () => W, reverse: () => no, - reverse1d: () => o1, - reverse2d: () => n1, - reverse3d: () => s1, - reverse4d: () => a1, + reverse1d: () => Lk, + reverse2d: () => Bk, + reverse3d: () => Vk, + reverse4d: () => zk, rfft: () => Wp, - round: () => Rd, - rsqrt: () => i1, - scalar: () => be, - scatterND: () => JG, - scatter_util: () => rl, - searchSorted: () => al, - selu: () => u1, - separableConv2d: () => p1, - serialization: () => pv, + round: () => Nd, + rsqrt: () => Wk, + scalar: () => Ce, + scatterND: () => HG, + scatter_util: () => sl, + searchSorted: () => ol, + selu: () => Uk, + separableConv2d: () => Gk, + serialization: () => oN, setBackend: () => Eie, - setPlatform: () => Pie, - setThreadsCount: () => kee, - setWasmPath: () => Iee, - setWasmPaths: () => vee, - setWebGLContext: () => RS, - setdiff1dAsync: () => c1, - shared: () => Qp, - sigmoid: () => zs, - sign: () => l1, - signal: () => iq, - sin: () => m1, - sinh: () => d1, - slice: () => He, - slice1d: () => f1, - slice2d: () => h1, - slice3d: () => g1, - slice4d: () => x1, + setPlatform: () => Mie, + setThreadsCount: () => Hee, + setWasmPath: () => Uee, + setWasmPaths: () => Gee, + setWebGLContext: () => US, + setdiff1dAsync: () => Hk, + shared: () => Zp, + sigmoid: () => Ws, + sign: () => Kk, + signal: () => JH, + sin: () => qk, + sinh: () => jk, + slice: () => Ke, + slice1d: () => Xk, + slice2d: () => Yk, + slice3d: () => Qk, + slice4d: () => Zk, slice_util: () => ut, - softmax: () => y1, - softplus: () => fd, - spaceToBatchND: () => Id, - sparse: () => lq, - sparseToDense: () => rH, - spectral: () => aq, + softmax: () => Jk, + softplus: () => id, + spaceToBatchND: () => gd, + sparse: () => oK, + sparseToDense: () => jG, + spectral: () => ZH, split: () => Oa, sqrt: () => $r, square: () => Qt, - squaredDifference: () => Dd, + squaredDifference: () => _d, squeeze: () => Up, stack: () => Sr, - step: () => Od, - stridedSlice: () => b1, - string: () => mq, - sub: () => Ne, + step: () => $d, + stridedSlice: () => e1, + string: () => nK, + sub: () => ke, sum: () => et, - sumOutType: () => ka, - tan: () => C1, - tanh: () => nl, + sumOutType: () => Ta, + tan: () => t1, + tanh: () => tl, tensor: () => nr, - tensor1d: () => mr, - tensor2d: () => _i, - tensor3d: () => Xm, - tensor4d: () => S1, - tensor5d: () => w1, - tensor6d: () => I1, - tensor_util: () => h0, - test_util: () => dv, + tensor1d: () => dr, + tensor2d: () => Oi, + tensor3d: () => Ed, + tensor4d: () => r1, + tensor5d: () => o1, + tensor6d: () => n1, + tensor_util: () => C0, + test_util: () => Fk, tidy: () => Ee, - tile: () => ki, - time: () => _ie, - topk: () => v1, - train: () => hMe, - transpose: () => Mp, - truncatedNormal: () => k1, - unique: () => N1, - unregisterGradient: () => Fne, - unregisterKernel: () => Rne, - unsortedSegmentSum: () => T1, + tile: () => Ai, + time: () => $ie, + topk: () => s1, + train: () => YMe, + transpose: () => Gp, + truncatedNormal: () => a1, + unique: () => i1, + unregisterGradient: () => yse, + unregisterKernel: () => xse, + unsortedSegmentSum: () => u1, unstack: () => so, upcastType: () => dt, - upperBound: () => _1, + upperBound: () => p1, util: () => y, - valueAndGrad: () => d4, - valueAndGrads: () => f4, - variable: () => E1, + valueAndGrad: () => qU, + valueAndGrads: () => jU, + variable: () => c1, variableGrads: () => pC, - version: () => gne, - version_converter: () => f6, - version_core: () => xW, - version_cpu: () => U6, - version_wasm: () => Tee, - version_webgl: () => L8, - webgl: () => L9e, - webgl_util: () => oc, - webgpu_util: () => nI, - where: () => os, - whereAsync: () => Md, - zeros: () => Vr, + version: () => tse, + version_converter: () => F6, + version_core: () => RK, + version_cpu: () => pj, + version_wasm: () => qee, + version_webgl: () => nY, + webgl: () => CJe, + webgl_util: () => nc, + webgpu_util: () => hI, + where: () => ss, + whereAsync: () => Rd, + zeros: () => Br, zerosLike: () => Ut }); -var yV = Object.create; -var ub = Object.defineProperty; -var bV = Object.getOwnPropertyDescriptor; -var CV = Object.getOwnPropertyNames; -var SV = Object.getPrototypeOf; -var wV = Object.prototype.hasOwnProperty; -var bm = ((r) => typeof __require != "undefined" ? __require : typeof Proxy != "undefined" ? new Proxy(r, { get: (e, t6) => (typeof __require != "undefined" ? __require : e)[t6] }) : r)(function(r) { - if (typeof __require != "undefined") - return __require.apply(this, arguments); - throw new Error('Dynamic require of "' + r + '" is not supported'); -}); -var qt = (r, e) => () => (e || r((e = { exports: {} }).exports, e), e.exports); -var Ue = (r, e) => { - for (var t6 in e) - ub(r, t6, { get: e[t6], enumerable: true }); +var PV = Object.create; +var yb = Object.defineProperty; +var MV = Object.getOwnPropertyDescriptor; +var LV = Object.getOwnPropertyNames; +var BV = Object.getPrototypeOf; +var VV = Object.prototype.hasOwnProperty; +var Kt = (r, e) => () => (e || r((e = { exports: {} }).exports, e), e.exports); +var Ge = (r, e) => { + for (var t10 in e) + yb(r, t10, { get: e[t10], enumerable: true }); }; -var IV = (r, e, t6, o) => { +var zV = (r, e, t10, o) => { if (e && typeof e == "object" || typeof e == "function") - for (let n of CV(e)) - !wV.call(r, n) && n !== t6 && ub(r, n, { get: () => e[n], enumerable: !(o = bV(e, n)) || o.enumerable }); + for (let n of LV(e)) + !VV.call(r, n) && n !== t10 && yb(r, n, { get: () => e[n], enumerable: !(o = MV(e, n)) || o.enumerable }); return r; }; -var rp = (r, e, t6) => (t6 = r != null ? yV(SV(r)) : {}, IV(e || !r || !r.__esModule ? ub(t6, "default", { value: r, enumerable: true }) : t6, r)); -var r0 = qt((Pne, t0) => { - 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(r >>>= 0, (n = 0 <= r && r < 256) && (o = KI[r], o) ? o : (t6 = vt(r, (r | 0) < 0 ? -1 : 0, true), n && (KI[r] = t6), t6)) : (r |= 0, (n = -128 <= r && r < 128) && (o = qI[r], o) ? o : (t6 = vt(r, r < 0 ? -1 : 0, false), n && (qI[r] = t6), t6)); + var t10, o, n; + return e ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = ZI[r], o) ? o : (t10 = vt(r, (r | 0) < 0 ? -1 : 0, true), n && (ZI[r] = t10), t10)) : (r |= 0, (n = -128 <= r && r < 128) && (o = QI[r], o) ? o : (t10 = vt(r, r < 0 ? -1 : 0, false), n && (QI[r] = t10), t10)); } It.fromInt = eu; - function yo(r, e) { + function bo(r, e) { if (isNaN(r)) - return e ? Ji : bo; + return e ? Ji : Co; if (e) { if (r < 0) return Ji; - if (r >= QI) - return e0; + if (r >= r0) + return s0; } else { - if (r <= -XI) - return Mr; - if (r + 1 >= XI) - return JI; + if (r <= -e0) + return Pr; + if (r + 1 >= e0) + return n0; } - return r < 0 ? yo(-r, e).neg() : vt(r % Ep | 0, r / Ep | 0, e); + return r < 0 ? bo(-r, e).neg() : vt(r % Ep | 0, r / Ep | 0, e); } - It.fromNumber = yo; - function vt(r, e, t6) { - return new It(r, e, t6); + It.fromNumber = bo; + function vt(r, e, t10) { + return new It(r, e, t10); } It.fromBits = vt; - var Rm = Math.pow; - function Ib(r, e, t6) { + var Dm = Math.pow; + function Db(r, e, t10) { if (r.length === 0) throw Error("empty string"); if (r === "NaN" || r === "Infinity" || r === "+Infinity" || r === "-Infinity") - return bo; - if (typeof e == "number" ? (t6 = e, e = false) : e = !!e, t6 = t6 || 10, t6 < 2 || 36 < t6) + return Co; + if (typeof e == "number" ? (t10 = e, e = false) : e = !!e, t10 = t10 || 10, t10 < 2 || 36 < t10) throw RangeError("radix"); var o; if ((o = r.indexOf("-")) > 0) throw Error("interior hyphen"); if (o === 0) - return Ib(r.substring(1), e, t6).neg(); - for (var n = yo(Rm(t6, 8)), s = bo, a = 0; a < r.length; a += 8) { - var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t6); + return Db(r.substring(1), e, t10).neg(); + for (var n = bo(Dm(t10, 8)), s = Co, a = 0; a < r.length; a += 8) { + var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t10); if (i < 8) { - var u = yo(Rm(t6, i)); - s = s.mul(u).add(yo(p)); + var u = bo(Dm(t10, i)); + s = s.mul(u).add(bo(p)); } else - s = s.mul(n), s = s.add(yo(p)); + s = s.mul(n), s = s.add(bo(p)); } return s.unsigned = e, s; } - It.fromString = Ib; - function ts(r, e) { - return typeof r == "number" ? yo(r, e) : typeof r == "string" ? Ib(r, e) : vt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned); + It.fromString = Db; + function rs(r, e) { + return typeof r == "number" ? bo(r, e) : typeof r == "string" ? Db(r, e) : vt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned); } - It.fromValue = ts; - var jI = 1 << 16, HV = 1 << 24, Ep = jI * jI, QI = Ep * Ep, XI = QI / 2, YI = eu(HV), bo = eu(0); - It.ZERO = bo; + It.fromValue = rs; + var JI = 1 << 16, cz = 1 << 24, Ep = JI * JI, r0 = Ep * Ep, e0 = r0 / 2, t0 = eu(cz), Co = eu(0); + It.ZERO = Co; var Ji = eu(0, true); It.UZERO = Ji; - var _p = eu(1); - It.ONE = _p; - var ZI = eu(1, true); - It.UONE = ZI; - var wb = eu(-1); - It.NEG_ONE = wb; - var JI = vt(-1, 2147483647, false); - It.MAX_VALUE = JI; - var e0 = vt(-1, -1, true); - It.MAX_UNSIGNED_VALUE = e0; - var Mr = vt(0, -2147483648, false); - It.MIN_VALUE = Mr; - var me = It.prototype; - me.toInt = function() { + var $p = eu(1); + It.ONE = $p; + var o0 = eu(1, true); + It.UONE = o0; + var Rb = eu(-1); + It.NEG_ONE = Rb; + var n0 = vt(-1, 2147483647, false); + It.MAX_VALUE = n0; + var s0 = vt(-1, -1, true); + It.MAX_UNSIGNED_VALUE = s0; + var Pr = vt(0, -2147483648, false); + It.MIN_VALUE = Pr; + var de = It.prototype; + de.toInt = function() { return this.unsigned ? this.low >>> 0 : this.low; }; - me.toNumber = function() { + de.toNumber = function() { return this.unsigned ? (this.high >>> 0) * Ep + (this.low >>> 0) : this.high * Ep + (this.low >>> 0); }; - me.toString = function(e) { + de.toString = function(e) { if (e = e || 10, e < 2 || 36 < e) throw RangeError("radix"); if (this.isZero()) return "0"; if (this.isNegative()) - if (this.eq(Mr)) { - var t6 = yo(e), o = this.div(t6), n = o.mul(t6).sub(this); + if (this.eq(Pr)) { + var t10 = bo(e), o = this.div(t10), n = o.mul(t10).sub(this); return o.toString(e) + n.toInt().toString(e); } else return "-" + this.neg().toString(e); - for (var s = yo(Rm(e, 6), this.unsigned), a = this, i = ""; ; ) { + for (var s = bo(Dm(e, 6), this.unsigned), a = this, i = ""; ; ) { var p = a.div(s), u = a.sub(p.mul(s)).toInt() >>> 0, c = u.toString(e); if (a = p, a.isZero()) return c + i; @@ -668,228 +656,228 @@ var r0 = qt((Pne, t0) => { i = "" + c + i; } }; - me.getHighBits = function() { + de.getHighBits = function() { return this.high; }; - me.getHighBitsUnsigned = function() { + de.getHighBitsUnsigned = function() { return this.high >>> 0; }; - me.getLowBits = function() { + de.getLowBits = function() { return this.low; }; - me.getLowBitsUnsigned = function() { + de.getLowBitsUnsigned = function() { return this.low >>> 0; }; - me.getNumBitsAbs = function() { + de.getNumBitsAbs = function() { if (this.isNegative()) - return this.eq(Mr) ? 64 : this.neg().getNumBitsAbs(); - for (var e = this.high != 0 ? this.high : this.low, t6 = 31; t6 > 0 && (e & 1 << t6) == 0; t6--) + return this.eq(Pr) ? 64 : this.neg().getNumBitsAbs(); + for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && (e & 1 << t10) == 0; t10--) ; - return this.high != 0 ? t6 + 33 : t6 + 1; + return this.high != 0 ? t10 + 33 : t10 + 1; }; - me.isZero = function() { + de.isZero = function() { return this.high === 0 && this.low === 0; }; - me.eqz = me.isZero; - me.isNegative = function() { + de.eqz = de.isZero; + de.isNegative = function() { return !this.unsigned && this.high < 0; }; - me.isPositive = function() { + de.isPositive = function() { return this.unsigned || this.high >= 0; }; - me.isOdd = function() { + de.isOdd = function() { return (this.low & 1) === 1; }; - me.isEven = function() { + de.isEven = function() { return (this.low & 1) === 0; }; - me.equals = function(e) { - return Lr(e) || (e = ts(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low; + de.equals = function(e) { + return Mr(e) || (e = rs(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low; }; - me.eq = me.equals; - me.notEquals = function(e) { + de.eq = de.equals; + de.notEquals = function(e) { return !this.eq(e); }; - me.neq = me.notEquals; - me.ne = me.notEquals; - me.lessThan = function(e) { + de.neq = de.notEquals; + de.ne = de.notEquals; + de.lessThan = function(e) { return this.comp(e) < 0; }; - me.lt = me.lessThan; - me.lessThanOrEqual = function(e) { + de.lt = de.lessThan; + de.lessThanOrEqual = function(e) { return this.comp(e) <= 0; }; - me.lte = me.lessThanOrEqual; - me.le = me.lessThanOrEqual; - me.greaterThan = function(e) { + de.lte = de.lessThanOrEqual; + de.le = de.lessThanOrEqual; + de.greaterThan = function(e) { return this.comp(e) > 0; }; - me.gt = me.greaterThan; - me.greaterThanOrEqual = function(e) { + de.gt = de.greaterThan; + de.greaterThanOrEqual = function(e) { return this.comp(e) >= 0; }; - me.gte = me.greaterThanOrEqual; - me.ge = me.greaterThanOrEqual; - me.compare = function(e) { - if (Lr(e) || (e = ts(e)), this.eq(e)) + de.gte = de.greaterThanOrEqual; + de.ge = de.greaterThanOrEqual; + de.compare = function(e) { + if (Mr(e) || (e = rs(e)), this.eq(e)) return 0; - var t6 = this.isNegative(), o = e.isNegative(); - return t6 && !o ? -1 : !t6 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1; + var t10 = this.isNegative(), o = e.isNegative(); + return t10 && !o ? -1 : !t10 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1; }; - me.comp = me.compare; - me.negate = function() { - return !this.unsigned && this.eq(Mr) ? Mr : this.not().add(_p); + de.comp = de.compare; + de.negate = function() { + return !this.unsigned && this.eq(Pr) ? Pr : this.not().add($p); }; - me.neg = me.negate; - me.add = function(e) { - Lr(e) || (e = ts(e)); - var t6 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0; - return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t6 + a, c &= 65535, vt(m << 16 | d, c << 16 | l, this.unsigned); + de.neg = de.negate; + de.add = function(e) { + Mr(e) || (e = rs(e)); + var t10 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0; + return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t10 + a, c &= 65535, vt(m << 16 | d, c << 16 | l, this.unsigned); }; - me.subtract = function(e) { - return Lr(e) || (e = ts(e)), this.add(e.neg()); + de.subtract = function(e) { + return Mr(e) || (e = rs(e)), this.add(e.neg()); }; - me.sub = me.subtract; - me.multiply = function(e) { + de.sub = de.subtract; + de.multiply = function(e) { if (this.isZero()) - return bo; - if (Lr(e) || (e = ts(e)), xo) { - var t6 = xo.mul(this.low, this.high, e.low, e.high); - return vt(t6, xo.get_high(), this.unsigned); + return Co; + if (Mr(e) || (e = rs(e)), yo) { + var t10 = yo.mul(this.low, this.high, e.low, e.high); + return vt(t10, yo.get_high(), this.unsigned); } if (e.isZero()) - return bo; - if (this.eq(Mr)) - return e.isOdd() ? Mr : bo; - if (e.eq(Mr)) - return this.isOdd() ? Mr : bo; + return Co; + if (this.eq(Pr)) + return e.isOdd() ? Pr : Co; + if (e.eq(Pr)) + return this.isOdd() ? Pr : Co; if (this.isNegative()) return e.isNegative() ? this.neg().mul(e.neg()) : this.neg().mul(e).neg(); if (e.isNegative()) return this.mul(e.neg()).neg(); - if (this.lt(YI) && e.lt(YI)) - return yo(this.toNumber() * e.toNumber(), this.unsigned); + if (this.lt(t0) && e.lt(t0)) + return bo(this.toNumber() * e.toNumber(), this.unsigned); var o = this.high >>> 16, n = this.high & 65535, s = this.low >>> 16, a = this.low & 65535, i = e.high >>> 16, p = e.high & 65535, u = e.low >>> 16, c = e.low & 65535, l = 0, m = 0, d = 0, f = 0; return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, vt(d << 16 | f, l << 16 | m, this.unsigned); }; - me.mul = me.multiply; - me.divide = function(e) { - if (Lr(e) || (e = ts(e)), e.isZero()) + de.mul = de.multiply; + de.divide = function(e) { + if (Mr(e) || (e = rs(e)), e.isZero()) throw Error("division by zero"); - if (xo) { + if (yo) { if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1) return this; - var t6 = (this.unsigned ? xo.div_u : xo.div_s)(this.low, this.high, e.low, e.high); - return vt(t6, xo.get_high(), this.unsigned); + var t10 = (this.unsigned ? yo.div_u : yo.div_s)(this.low, this.high, e.low, e.high); + return vt(t10, yo.get_high(), this.unsigned); } if (this.isZero()) - return this.unsigned ? Ji : bo; + return this.unsigned ? Ji : Co; var o, n, s; if (this.unsigned) { if (e.unsigned || (e = e.toUnsigned()), e.gt(this)) return Ji; if (e.gt(this.shru(1))) - return ZI; + return o0; s = Ji; } else { - if (this.eq(Mr)) { - if (e.eq(_p) || e.eq(wb)) - return Mr; - if (e.eq(Mr)) - return _p; + if (this.eq(Pr)) { + if (e.eq($p) || e.eq(Rb)) + return Pr; + if (e.eq(Pr)) + return $p; var a = this.shr(1); - return o = a.div(e).shl(1), o.eq(bo) ? e.isNegative() ? _p : wb : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s); - } else if (e.eq(Mr)) - return this.unsigned ? Ji : bo; + return o = a.div(e).shl(1), o.eq(Co) ? e.isNegative() ? $p : Rb : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s); + } else if (e.eq(Pr)) + return this.unsigned ? Ji : Co; if (this.isNegative()) return e.isNegative() ? this.neg().div(e.neg()) : this.neg().div(e).neg(); if (e.isNegative()) return this.div(e.neg()).neg(); - s = bo; + s = Co; } for (n = this; n.gte(e); ) { o = Math.max(1, Math.floor(n.toNumber() / e.toNumber())); - for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Rm(2, i - 48), u = yo(o), c = u.mul(e); c.isNegative() || c.gt(n); ) - o -= p, u = yo(o, this.unsigned), c = u.mul(e); - u.isZero() && (u = _p), s = s.add(u), n = n.sub(c); + for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Dm(2, i - 48), u = bo(o), c = u.mul(e); c.isNegative() || c.gt(n); ) + o -= p, u = bo(o, this.unsigned), c = u.mul(e); + u.isZero() && (u = $p), s = s.add(u), n = n.sub(c); } return s; }; - me.div = me.divide; - me.modulo = function(e) { - if (Lr(e) || (e = ts(e)), xo) { - var t6 = (this.unsigned ? xo.rem_u : xo.rem_s)(this.low, this.high, e.low, e.high); - return vt(t6, xo.get_high(), this.unsigned); + de.div = de.divide; + de.modulo = function(e) { + if (Mr(e) || (e = rs(e)), yo) { + var t10 = (this.unsigned ? yo.rem_u : yo.rem_s)(this.low, this.high, e.low, e.high); + return vt(t10, yo.get_high(), this.unsigned); } return this.sub(this.div(e).mul(e)); }; - me.mod = me.modulo; - me.rem = me.modulo; - me.not = function() { + de.mod = de.modulo; + de.rem = de.modulo; + de.not = function() { return vt(~this.low, ~this.high, this.unsigned); }; - me.and = function(e) { - return Lr(e) || (e = ts(e)), vt(this.low & e.low, this.high & e.high, this.unsigned); + de.and = function(e) { + return Mr(e) || (e = rs(e)), vt(this.low & e.low, this.high & e.high, this.unsigned); }; - me.or = function(e) { - return Lr(e) || (e = ts(e)), vt(this.low | e.low, this.high | e.high, this.unsigned); + de.or = function(e) { + return Mr(e) || (e = rs(e)), vt(this.low | e.low, this.high | e.high, this.unsigned); }; - me.xor = function(e) { - return Lr(e) || (e = ts(e)), vt(this.low ^ e.low, this.high ^ e.high, this.unsigned); + de.xor = function(e) { + return Mr(e) || (e = rs(e)), vt(this.low ^ e.low, this.high ^ e.high, this.unsigned); }; - me.shiftLeft = function(e) { - return Lr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : vt(0, this.low << e - 32, this.unsigned); + de.shiftLeft = function(e) { + return Mr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : vt(0, this.low << e - 32, this.unsigned); }; - me.shl = me.shiftLeft; - me.shiftRight = function(e) { - return Lr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : vt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned); + de.shl = de.shiftLeft; + de.shiftRight = function(e) { + return Mr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : vt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned); }; - me.shr = me.shiftRight; - me.shiftRightUnsigned = function(e) { - if (Lr(e) && (e = e.toInt()), e &= 63, e === 0) + de.shr = de.shiftRight; + de.shiftRightUnsigned = function(e) { + if (Mr(e) && (e = e.toInt()), e &= 63, e === 0) return this; - var t6 = this.high; + var t10 = this.high; if (e < 32) { var o = this.low; - return vt(o >>> e | t6 << 32 - e, t6 >>> e, this.unsigned); + return vt(o >>> e | t10 << 32 - e, t10 >>> e, this.unsigned); } else - return e === 32 ? vt(t6, 0, this.unsigned) : vt(t6 >>> e - 32, 0, this.unsigned); + return e === 32 ? vt(t10, 0, this.unsigned) : vt(t10 >>> e - 32, 0, this.unsigned); }; - me.shru = me.shiftRightUnsigned; - me.shr_u = me.shiftRightUnsigned; - me.toSigned = function() { + de.shru = de.shiftRightUnsigned; + de.shr_u = de.shiftRightUnsigned; + de.toSigned = function() { return this.unsigned ? vt(this.low, this.high, false) : this; }; - me.toUnsigned = function() { + de.toUnsigned = function() { return this.unsigned ? this : vt(this.low, this.high, true); }; - me.toBytes = function(e) { + de.toBytes = function(e) { return e ? this.toBytesLE() : this.toBytesBE(); }; - me.toBytesLE = function() { - var e = this.high, t6 = this.low; - return [t6 & 255, t6 >>> 8 & 255, t6 >>> 16 & 255, t6 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24]; + de.toBytesLE = function() { + var e = this.high, t10 = this.low; + return [t10 & 255, t10 >>> 8 & 255, t10 >>> 16 & 255, t10 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24]; }; - me.toBytesBE = function() { - var e = this.high, t6 = this.low; - return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t6 >>> 24, t6 >>> 16 & 255, t6 >>> 8 & 255, t6 & 255]; + de.toBytesBE = function() { + var e = this.high, t10 = this.low; + return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t10 >>> 24, t10 >>> 16 & 255, t10 >>> 8 & 255, t10 & 255]; }; - It.fromBytes = function(e, t6, o) { - return o ? It.fromBytesLE(e, t6) : It.fromBytesBE(e, t6); + It.fromBytes = function(e, t10, o) { + return o ? It.fromBytesLE(e, t10) : It.fromBytesBE(e, t10); }; - It.fromBytesLE = function(e, t6) { - return new It(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t6); + It.fromBytesLE = function(e, t10) { + return new It(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t10); }; - It.fromBytesBE = function(e, t6) { - return new It(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t6); + It.fromBytesBE = function(e, t10) { + return new It(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t10); }; }); -var M0 = qt(() => { +var W0 = Kt(() => { }); -var L0 = qt(() => { +var U0 = Kt(() => { }); -var Bk = qt((Lk, cC) => { - (function(r, e, t6) { +var gk = Kt((hk, cC) => { + (function(r, e, t10) { function o(i) { var p = this, u = a(); p.next = function() { @@ -922,13 +910,13 @@ var Bk = qt((Lk, cC) => { }; return p; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.alea = s; - })(Lk, typeof cC == "object" && cC, typeof define == "function" && define); + })(hk, typeof cC == "object" && cC, typeof define == "function" && define); }); -var zk = qt((Vk, lC) => { - (function(r, e, t6) { +var yk = Kt((xk, lC) => { + (function(r, e, t10) { function o(a) { var i = this, p = ""; i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.next = function() { @@ -954,13 +942,13 @@ var zk = qt((Vk, lC) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.xor128 = s; - })(Vk, typeof lC == "object" && lC, typeof define == "function" && define); + })(xk, typeof lC == "object" && lC, typeof define == "function" && define); }); -var Uk = qt((Wk, mC) => { - (function(r, e, t6) { +var Ck = Kt((bk, mC) => { + (function(r, e, t10) { function o(a) { var i = this, p = ""; i.next = function() { @@ -986,13 +974,13 @@ var Uk = qt((Wk, mC) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.xorwow = s; - })(Wk, typeof mC == "object" && mC, typeof define == "function" && define); + })(bk, typeof mC == "object" && mC, typeof define == "function" && define); }); -var Hk = qt((Gk, dC) => { - (function(r, e, t6) { +var wk = Kt((Sk, dC) => { + (function(r, e, t10) { function o(a) { var i = this; i.next = function() { @@ -1032,13 +1020,13 @@ var Hk = qt((Gk, dC) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.xorshift7 = s; - })(Gk, typeof dC == "object" && dC, typeof define == "function" && define); + })(Sk, typeof dC == "object" && dC, typeof define == "function" && define); }); -var Kk = qt((qk, fC) => { - (function(r, e, t6) { +var vk = Kt((Ik, fC) => { + (function(r, e, t10) { function o(a) { var i = this; i.next = function() { @@ -1072,13 +1060,13 @@ var Kk = qt((qk, fC) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.xor4096 = s; - })(qk, typeof fC == "object" && fC, typeof define == "function" && define); + })(Ik, typeof fC == "object" && fC, typeof define == "function" && define); }); -var Xk = qt((jk, hC) => { - (function(r, e, t6) { +var Nk = Kt((kk, hC) => { + (function(r, e, t10) { function o(a) { var i = this, p = ""; i.next = function() { @@ -1104,20 +1092,20 @@ var Xk = qt((jk, hC) => { return n(p, {}); }), c; } - e && e.exports ? e.exports = s : t6 && t6.amd ? t6(function() { + e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() { return s; }) : this.tychei = s; - })(jk, typeof hC == "object" && hC, typeof define == "function" && define); + })(kk, typeof hC == "object" && hC, typeof define == "function" && define); }); -var Yk = qt(() => { +var Tk = Kt(() => { }); -var Zk = qt((Qk, kd) => { - (function(r, e, t6) { - var o = 256, n = 6, s = 52, a = "random", i = t6.pow(o, n), p = t6.pow(2, s), u = p * 2, c = o - 1, l; +var $k = Kt((_k, yd) => { + (function(r, e, t10) { + var o = 256, n = 6, s = 52, a = "random", i = t10.pow(o, n), p = t10.pow(2, s), u = p * 2, c = o - 1, l; function m(C, w, k) { var _ = []; w = w == true ? { entropy: true } : w || {}; - var $ = g(h(w.entropy ? [C, b(e)] : C == null ? x() : C, 3), _), A = new d(_), R = function() { + var E = g(h(w.entropy ? [C, b(e)] : C == null ? x() : C, 3), _), A = new d(_), R = function() { for (var D = A.g(n), P = i, M = 0; D < p; ) D = (D + M) * o, P *= o, M = A.g(1); for (; D >= u; ) @@ -1131,37 +1119,37 @@ var Zk = qt((Qk, kd) => { }, R.double = R, g(b(A.S), e), (w.pass || k || function(D, P, M, L) { return L && (L.S && f(L, A), D.state = function() { return f(A, {}); - }), M ? (t6[a] = D, P) : D; - })(R, $, "global" in w ? w.global : this == t6, w.state); + }), M ? (t10[a] = D, P) : D; + })(R, E, "global" in w ? w.global : this == t10, w.state); } function d(C) { - var w, k = C.length, _ = this, $ = 0, A = _.i = _.j = 0, R = _.S = []; - for (k || (C = [k++]); $ < o; ) - R[$] = $++; - for ($ = 0; $ < o; $++) - R[$] = R[A = c & A + C[$ % k] + (w = R[$])], R[A] = w; + var w, k = C.length, _ = this, E = 0, A = _.i = _.j = 0, R = _.S = []; + for (k || (C = [k++]); E < o; ) + R[E] = E++; + for (E = 0; E < o; E++) + R[E] = R[A = c & A + C[E % k] + (w = R[E])], R[A] = w; (_.g = function(D) { - for (var P, M = 0, L = _.i, W = _.j, V = _.S; D--; ) - P = V[L = c & L + 1], M = M * o + V[c & (V[L] = V[W = c & W + P]) + (V[W] = P)]; - return _.i = L, _.j = W, M; + for (var P, M = 0, L = _.i, V = _.j, z = _.S; D--; ) + P = z[L = c & L + 1], M = M * o + z[c & (z[L] = z[V = c & V + P]) + (z[V] = P)]; + return _.i = L, _.j = V, M; })(o); } function f(C, w) { return w.i = C.i, w.j = C.j, w.S = C.S.slice(), w; } function h(C, w) { - var k = [], _ = typeof C, $; + var k = [], _ = typeof C, E; if (w && _ == "object") - for ($ in C) + for (E in C) try { - k.push(h(C[$], w - 1)); + k.push(h(C[E], w - 1)); } catch (A) { } return k.length ? k : _ == "string" ? C : C + "\0"; } function g(C, w) { - for (var k = C + "", _, $ = 0; $ < k.length; ) - w[c & $] = c & (_ ^= w[c & $] * 19) + k.charCodeAt($++); + for (var k = C + "", _, E = 0; E < k.length; ) + w[c & E] = c & (_ ^= w[c & E] * 19) + k.charCodeAt(E++); return b(w); } function x() { @@ -1176,63 +1164,63 @@ var Zk = qt((Qk, kd) => { function b(C) { return String.fromCharCode.apply(0, C); } - if (g(t6.random(), e), typeof kd == "object" && kd.exports) { - kd.exports = m; + if (g(t10.random(), e), typeof yd == "object" && yd.exports) { + yd.exports = m; try { - l = Yk(); + l = Tk(); } catch (C) { } } else typeof define == "function" && define.amd ? define(function() { return m; - }) : t6["seed" + a] = m; - })(typeof self != "undefined" ? self : Qk, [], Math); + }) : t10["seed" + a] = m; + })(typeof self != "undefined" ? self : _k, [], Math); }); -var gC = qt((_we, Jk) => { - var eG = Bk(), tG = zk(), rG = Uk(), oG = Hk(), nG = Kk(), sG = Xk(), du = Zk(); - du.alea = eG; - du.xor128 = tG; - du.xorwow = rG; - du.xorshift7 = oG; - du.xor4096 = nG; - du.tychei = sG; - Jk.exports = du; +var gC = Kt((QSe, Ek) => { + var P4 = gk(), M4 = yk(), L4 = Ck(), B4 = wk(), V4 = vk(), z4 = Nk(), lu = $k(); + lu.alea = P4; + lu.xor128 = M4; + lu.xorwow = L4; + lu.xorshift7 = B4; + lu.xor4096 = V4; + lu.tychei = z4; + Ek.exports = lu; }); -var Rl = qt(() => { +var oI = Kt(() => { }); -var qw = qt(() => { +var nI = Kt(() => { }); -var l3 = qt(() => { +var x3 = Kt(() => { }); -var m3 = qt(() => { +var y3 = Kt(() => { }); -var d3 = qt(() => { +var b3 = Kt(() => { }); -var f3 = qt((wg, jw) => { - var Kw = (() => { +var C3 = Kt((wg, aI) => { + var sI = (() => { var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; return typeof __filename != "undefined" && (r = r || __filename), function(e) { e = e || {}; - function t6() { - return J.buffer != Oe && Nt(J.buffer), mt; + function t10() { + return te.buffer != ze && Nt(te.buffer), ct; } function o() { - return J.buffer != Oe && Nt(J.buffer), at; + return te.buffer != ze && Nt(te.buffer), at; } function n() { - return J.buffer != Oe && Nt(J.buffer), ft; + return te.buffer != ze && Nt(te.buffer), ft; } function s() { - return J.buffer != Oe && Nt(J.buffer), Fr; + return te.buffer != ze && Nt(te.buffer), Rr; } function a() { - return J.buffer != Oe && Nt(J.buffer), Ot; + return te.buffer != ze && Nt(te.buffer), Pt; } function i() { - return J.buffer != Oe && Nt(J.buffer), Kr; + return te.buffer != ze && Nt(te.buffer), qr; } function p() { - return J.buffer != Oe && Nt(J.buffer), er; + return te.buffer != ze && Nt(te.buffer), er; } var u = typeof e != "undefined" ? e : {}, c, l; u.ready = new Promise(function(F, B) { @@ -1246,869 +1234,849 @@ var f3 = qt((wg, jw) => { function _(F) { return u.locateFile ? u.locateFile(F, k) : k + F; } - var $, A, R, D; + var E, A, R, D; function P(F) { - if (F instanceof Hi) + if (F instanceof qi) return; - q("exiting due to exception: " + F); + K("exiting due to exception: " + F); } if (C) { - b ? k = Rl().dirname(k) + "/" : k = __dirname + "/"; - var M, L; - typeof bm == "function" && (M = qw(), L = Rl()), $ = (B, ne) => (B = L.normalize(B), M.readFileSync(B, ne ? void 0 : "utf8")), R = (B) => { - var ne = $(B, true); - return ne.buffer || (ne = new Uint8Array(ne)), ne; - }, A = (B, ne, fe) => { - B = L.normalize(B), M.readFile(B, function(Te, Ze) { - Te ? fe(Te) : ne(Ze.buffer); + var M = oI(), L = nI(); + b ? k = L.dirname(k) + "/" : k = __dirname + "/", E = (B, ue) => (B = ep(B) ? new URL(B) : L.normalize(B), M.readFileSync(B, ue ? void 0 : "utf8")), R = (B) => { + var ue = E(B, true); + return ue.buffer || (ue = new Uint8Array(ue)), ue; + }, A = (B, ue, _e) => { + B = ep(B) ? new URL(B) : L.normalize(B), M.readFile(B, function(Me, Pe) { + Me ? _e(Me) : ue(Pe.buffer); }); }, process.argv.length > 1 && (h = process.argv[1].replace(/\\/g, "/")), f = process.argv.slice(2), process.on("uncaughtException", function(B) { - if (!(B instanceof Hi)) + if (!(B instanceof qi)) throw B; }), process.on("unhandledRejection", function(B) { throw B; - }), g = (B, ne) => { - if (Fo()) - throw process.exitCode = B, ne; - P(ne), process.exit(B); + }), g = (B, ue) => { + if (Do()) + throw process.exitCode = B, ue; + P(ue), process.exit(B); }, u.inspect = function() { return "[Emscripten Module object]"; }; let F; try { - F = l3(); + F = x3(); } catch (B) { throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), B; } global.Worker = F.Worker; } else - (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || ($ = (F) => { + (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => { var B = new XMLHttpRequest(); return B.open("GET", F, false), B.send(null), B.responseText; }, b && (R = (F) => { var B = new XMLHttpRequest(); return B.open("GET", F, false), B.responseType = "arraybuffer", B.send(null), new Uint8Array(B.response); - }), A = (F, B, ne) => { - var fe = new XMLHttpRequest(); - fe.open("GET", F, true), fe.responseType = "arraybuffer", fe.onload = () => { - if (fe.status == 200 || fe.status == 0 && fe.response) { - B(fe.response); + }), A = (F, B, ue) => { + var _e = new XMLHttpRequest(); + _e.open("GET", F, true), _e.responseType = "arraybuffer", _e.onload = () => { + if (_e.status == 200 || _e.status == 0 && _e.response) { + B(_e.response); return; } - ne(); - }, fe.onerror = ne, fe.send(null); + ue(); + }, _e.onerror = ue, _e.send(null); }), D = (F) => document.title = F); - C && typeof performance == "undefined" && (global.performance = m3().performance); - var W = console.log.bind(console), V = console.warn.bind(console); - C && (W = (F) => M.writeSync(1, F + ` -`), V = (F) => M.writeSync(2, F + ` + C && typeof performance == "undefined" && (global.performance = y3().performance); + var V = console.log.bind(console), z = console.warn.bind(console); + C && (V = (F) => M.writeSync(1, F + ` +`), z = (F) => M.writeSync(2, F + ` `)); - var U = u.print || W, q = u.printErr || V; + var U = u.print || V, K = u.printErr || z; Object.assign(u, d), d = null, u.arguments && (f = u.arguments), u.thisProgram && (h = u.thisProgram), u.quit && (g = u.quit); - var H = 4, j = Atomics.load, X = Atomics.store, Z = Atomics.compareExchange, ee; - u.wasmBinary && (ee = u.wasmBinary); - var Y = u.noExitRuntime || true; - typeof WebAssembly != "object" && Xu("no native wasm support detected"); - var J, ie, pe = false, he; - function we(F, B) { - F || Xu(B); + var H = 4, q = Atomics.load, Z = Atomics.store, ee = Atomics.compareExchange, oe; + u.wasmBinary && (oe = u.wasmBinary); + var J = u.noExitRuntime || true; + typeof WebAssembly != "object" && Ki("no native wasm support detected"); + var te, ie, ce = false, ye; + function Ne(F, B) { + F || Ki(B); } - var ve = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; - function $e(F, B, ne) { - for (var fe = B + ne, Te = B; F[Te] && !(Te >= fe); ) - ++Te; - if (Te - B > 16 && F.buffer && ve) - return ve.decode(F.buffer instanceof SharedArrayBuffer ? F.slice(B, Te) : F.subarray(B, Te)); - for (var Ze = ""; B < Te; ) { - var Ae = F[B++]; - if (!(Ae & 128)) { - Ze += String.fromCharCode(Ae); + var Ie = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function Ae(F, B, ue) { + for (var _e = B + ue, Me = B; F[Me] && !(Me >= _e); ) + ++Me; + if (Me - B > 16 && F.buffer && Ie) + return Ie.decode(F.buffer instanceof SharedArrayBuffer ? F.slice(B, Me) : F.subarray(B, Me)); + for (var Pe = ""; B < Me; ) { + var fe = F[B++]; + if (!(fe & 128)) { + Pe += String.fromCharCode(fe); continue; } - var Pe = F[B++] & 63; - if ((Ae & 224) == 192) { - Ze += String.fromCharCode((Ae & 31) << 6 | Pe); + var ve = F[B++] & 63; + if ((fe & 224) == 192) { + Pe += String.fromCharCode((fe & 31) << 6 | ve); continue; } - var zt = F[B++] & 63; - if ((Ae & 240) == 224 ? Ae = (Ae & 15) << 12 | Pe << 6 | zt : Ae = (Ae & 7) << 18 | Pe << 12 | zt << 6 | F[B++] & 63, Ae < 65536) - Ze += String.fromCharCode(Ae); + var Ft = F[B++] & 63; + if ((fe & 240) == 224 ? fe = (fe & 15) << 12 | ve << 6 | Ft : fe = (fe & 7) << 18 | ve << 12 | Ft << 6 | F[B++] & 63, fe < 65536) + Pe += String.fromCharCode(fe); else { - var Qr = Ae - 65536; - Ze += String.fromCharCode(55296 | Qr >> 10, 56320 | Qr & 1023); + var Qr = fe - 65536; + Pe += String.fromCharCode(55296 | Qr >> 10, 56320 | Qr & 1023); } } - return Ze; + return Pe; } - function Le(F, B) { - return F ? $e(o(), F, B) : ""; + function Re(F, B) { + return F ? Ae(o(), F, B) : ""; } - function nt(F, B, ne, fe) { - if (!(fe > 0)) + function ot(F, B, ue, _e) { + if (!(_e > 0)) return 0; - for (var Te = ne, Ze = ne + fe - 1, Ae = 0; Ae < F.length; ++Ae) { - var Pe = F.charCodeAt(Ae); - if (Pe >= 55296 && Pe <= 57343) { - var zt = F.charCodeAt(++Ae); - Pe = 65536 + ((Pe & 1023) << 10) | zt & 1023; + for (var Me = ue, Pe = ue + _e - 1, fe = 0; fe < F.length; ++fe) { + var ve = F.charCodeAt(fe); + if (ve >= 55296 && ve <= 57343) { + var Ft = F.charCodeAt(++fe); + ve = 65536 + ((ve & 1023) << 10) | Ft & 1023; } - if (Pe <= 127) { - if (ne >= Ze) + if (ve <= 127) { + if (ue >= Pe) break; - B[ne++] = Pe; - } else if (Pe <= 2047) { - if (ne + 1 >= Ze) + B[ue++] = ve; + } else if (ve <= 2047) { + if (ue + 1 >= Pe) break; - B[ne++] = 192 | Pe >> 6, B[ne++] = 128 | Pe & 63; - } else if (Pe <= 65535) { - if (ne + 2 >= Ze) + B[ue++] = 192 | ve >> 6, B[ue++] = 128 | ve & 63; + } else if (ve <= 65535) { + if (ue + 2 >= Pe) break; - B[ne++] = 224 | Pe >> 12, B[ne++] = 128 | Pe >> 6 & 63, B[ne++] = 128 | Pe & 63; + B[ue++] = 224 | ve >> 12, B[ue++] = 128 | ve >> 6 & 63, B[ue++] = 128 | ve & 63; } else { - if (ne + 3 >= Ze) + if (ue + 3 >= Pe) break; - B[ne++] = 240 | Pe >> 18, B[ne++] = 128 | Pe >> 12 & 63, B[ne++] = 128 | Pe >> 6 & 63, B[ne++] = 128 | Pe & 63; + B[ue++] = 240 | ve >> 18, B[ue++] = 128 | ve >> 12 & 63, B[ue++] = 128 | ve >> 6 & 63, B[ue++] = 128 | ve & 63; } } - return B[ne] = 0, ne - Te; + return B[ue] = 0, ue - Me; } - function pt(F, B, ne) { - return nt(F, o(), B, ne); + function pt(F, B, ue) { + return ot(F, o(), B, ue); } - var Oe, mt, at, ft, wt, Fr, Ot, Kr, er; - w && (Oe = u.buffer); + var ze, ct, at, ft, ht, Rr, Pt, qr, er; + w && (ze = u.buffer); function Nt(F) { - Oe = F, u.HEAP8 = mt = new Int8Array(F), u.HEAP16 = ft = new Int16Array(F), u.HEAP32 = Fr = new Int32Array(F), u.HEAPU8 = at = new Uint8Array(F), u.HEAPU16 = wt = new Uint16Array(F), u.HEAPU32 = Ot = new Uint32Array(F), u.HEAPF32 = Kr = new Float32Array(F), u.HEAPF64 = er = new Float64Array(F); + ze = F, u.HEAP8 = ct = new Int8Array(F), u.HEAP16 = ft = new Int16Array(F), u.HEAP32 = Rr = new Int32Array(F), u.HEAPU8 = at = new Uint8Array(F), u.HEAPU16 = ht = new Uint16Array(F), u.HEAPU32 = Pt = new Uint32Array(F), u.HEAPF32 = qr = new Float32Array(F), u.HEAPF64 = er = new Float64Array(F); } var tr = u.INITIAL_MEMORY || 16777216; if (w) - J = u.wasmMemory, Oe = u.buffer; + te = u.wasmMemory, ze = u.buffer; else if (u.wasmMemory) - J = u.wasmMemory; - else if (J = new WebAssembly.Memory({ initial: tr / 65536, maximum: 32768, shared: true }), !(J.buffer instanceof SharedArrayBuffer)) - throw q("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), C && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory"); - J && (Oe = J.buffer), tr = Oe.byteLength, Nt(Oe); - var rr, jr = [], Xr = [], pr = [], Js = false; - function Fo() { - return Y; + te = u.wasmMemory; + else if (te = new WebAssembly.Memory({ initial: tr / 65536, maximum: 32768, shared: true }), !(te.buffer instanceof SharedArrayBuffer)) + throw K("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), C && K("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"), Error("bad memory"); + te && (ze = te.buffer), tr = ze.byteLength, Nt(ze); + var rr, jr = [], Xr = [], cr = [], ea = false; + function Do() { + return J; } - function Ka() { + function hs() { if (u.preRun) for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; ) - Ac(u.preRun.shift()); + Dc(u.preRun.shift()); Pc(jr); } - function Kt() { - Js = true, !w && Pc(Xr); + function qt() { + ea = true, !w && Pc(Xr); } - function ea() { + function ta() { if (!w) { if (u.postRun) for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; ) - SI(u.postRun.shift()); - Pc(pr); + AI(u.postRun.shift()); + Pc(cr); } } - function Ac(F) { + function Dc(F) { jr.unshift(F); } - function Rc(F) { + function Fc(F) { Xr.unshift(F); } - function SI(F) { - pr.unshift(F); + function AI(F) { + cr.unshift(F); } - var ja = 0, ju = null, ta = null; - function wI(F) { - ja++, u.monitorRunDependencies && u.monitorRunDependencies(ja); + var Ya = 0, Ju = null, ra = null; + function Rx(F) { + Ya++, u.monitorRunDependencies && u.monitorRunDependencies(Ya); } - function II(F) { - if (ja--, u.monitorRunDependencies && u.monitorRunDependencies(ja), ja == 0 && (ju !== null && (clearInterval(ju), ju = null), ta)) { - var B = ta; - ta = null, B(); + function Yl(F) { + if (Ya--, u.monitorRunDependencies && u.monitorRunDependencies(Ya), Ya == 0 && (Ju !== null && (clearInterval(Ju), Ju = null), ra)) { + var B = ra; + ra = null, B(); } } - function Xu(F) { - w ? postMessage({ cmd: "onAbort", arg: F }) : u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", q(F), pe = true, he = 1, F += ". Build with -sASSERTIONS for more info."; + function Ki(F) { + u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", K(F), ce = true, ye = 1, F += ". Build with -sASSERTIONS for more info."; var B = new WebAssembly.RuntimeError(F); throw l(B), B; } - var Ix = "data:application/octet-stream;base64,"; - function jl(F) { - return F.startsWith(Ix); + var Dx = "data:application/octet-stream;base64,"; + function Ql(F) { + return F.startsWith(Dx); } - function Fc(F) { + function ep(F) { return F.startsWith("file://"); } - var fr; - fr = "tfjs-backend-wasm-threaded-simd.wasm", jl(fr) || (fr = _(fr)); - function Xl(F) { + var lr; + lr = "tfjs-backend-wasm-threaded-simd.wasm", Ql(lr) || (lr = _(lr)); + function Zl(F) { try { - if (F == fr && ee) - return new Uint8Array(ee); + if (F == lr && oe) + return new Uint8Array(oe); if (R) return R(F); throw "both async and sync fetching of the wasm failed"; } catch (B) { - Xu(B); + Ki(B); } } - function vx() { - if (!ee && (x || b)) { - if (typeof fetch == "function" && !Fc(fr)) - return fetch(fr, { credentials: "same-origin" }).then(function(F) { + function Fx() { + if (!oe && (x || b)) { + if (typeof fetch == "function" && !ep(lr)) + return fetch(lr, { credentials: "same-origin" }).then(function(F) { if (!F.ok) - throw "failed to load wasm binary file at '" + fr + "'"; + throw "failed to load wasm binary file at '" + lr + "'"; return F.arrayBuffer(); }).catch(function() { - return Xl(fr); + return Zl(lr); }); if (A) return new Promise(function(F, B) { - A(fr, function(ne) { - F(new Uint8Array(ne)); + A(lr, function(ue) { + F(new Uint8Array(ue)); }, B); }); } return Promise.resolve().then(function() { - return Xl(fr); + return Zl(lr); }); } - function kx() { - var F = { env: im, wasi_snapshot_preview1: im }; - function B(Ae, Pe) { - var zt = Ae.exports; - if (u.asm = zt, Ox(u.asm._emscripten_tls_init), rr = u.asm.__indirect_function_table, Rc(u.asm.__wasm_call_ctors), ie = Pe, !w) { - var Qr = De.unusedWorkers.length; - De.unusedWorkers.forEach(function(oa) { - De.loadWasmModuleToWorker(oa, function() { - --Qr || II("wasm-instantiate"); + function Ox() { + var F = { env: lm, wasi_snapshot_preview1: lm }; + function B(fe, ve) { + var Ft = fe.exports; + if (u.asm = Ft, Gx(u.asm._emscripten_tls_init), rr = u.asm.__indirect_function_table, Fc(u.asm.__wasm_call_ctors), ie = ve, !w) { + var Qr = Oe.unusedWorkers.length; + Oe.unusedWorkers.forEach(function(na) { + Oe.loadWasmModuleToWorker(na, function() { + --Qr || Yl("wasm-instantiate"); }); }); } } - w || wI("wasm-instantiate"); - function ne(Ae) { - B(Ae.instance, Ae.module); + w || Rx("wasm-instantiate"); + function ue(fe) { + B(fe.instance, fe.module); } - function fe(Ae) { - return vx().then(function(Pe) { - return WebAssembly.instantiate(Pe, F); - }).then(function(Pe) { - return Pe; - }).then(Ae, function(Pe) { - q("failed to asynchronously prepare wasm: " + Pe), Xu(Pe); + function _e(fe) { + return Fx().then(function(ve) { + return WebAssembly.instantiate(ve, F); + }).then(function(ve) { + return ve; + }).then(fe, function(ve) { + K("failed to asynchronously prepare wasm: " + ve), Ki(ve); }); } - function Te() { - return !ee && typeof WebAssembly.instantiateStreaming == "function" && !jl(fr) && !Fc(fr) && !C && typeof fetch == "function" ? fetch(fr, { credentials: "same-origin" }).then(function(Ae) { - var Pe = WebAssembly.instantiateStreaming(Ae, F); - return Pe.then(ne, function(zt) { - return q("wasm streaming compile failed: " + zt), q("falling back to ArrayBuffer instantiation"), fe(ne); + function Me() { + return !oe && typeof WebAssembly.instantiateStreaming == "function" && !Ql(lr) && !ep(lr) && !C && typeof fetch == "function" ? fetch(lr, { credentials: "same-origin" }).then(function(fe) { + var ve = WebAssembly.instantiateStreaming(fe, F); + return ve.then(ue, function(Ft) { + return K("wasm streaming compile failed: " + Ft), K("falling back to ArrayBuffer instantiation"), _e(ue); }); - }) : fe(ne); + }) : _e(ue); } if (u.instantiateWasm) try { - var Ze = u.instantiateWasm(F, B); - return Ze; - } catch (Ae) { - q("Module.instantiateWasm callback failed with error: " + Ae), l(Ae); + var Pe = u.instantiateWasm(F, B); + return Pe; + } catch (fe) { + K("Module.instantiateWasm callback failed with error: " + fe), l(fe); } - return Te().catch(l), {}; + return Me().catch(l), {}; } - var Nx, vI, Tx = {}; - function Hi(F) { + var RI, DI, Jl = {}; + function qi(F) { this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F; } - function _x(F) { - var B = De.pthreads[F]; - delete De.pthreads[F], B.terminate(), sb(F), De.runningWorkers.splice(De.runningWorkers.indexOf(B), 1), B.pthread_ptr = 0; + function Px(F) { + var B = Oe.pthreads[F]; + delete Oe.pthreads[F], B.terminate(), hb(F), Oe.runningWorkers.splice(Oe.runningWorkers.indexOf(B), 1), B.pthread_ptr = 0; } - function Ex(F) { - var B = De.pthreads[F]; + function Mx(F) { + var B = Oe.pthreads[F]; B.postMessage({ cmd: "cancel" }); } - function Dc(F) { - var B = De.pthreads[F]; - we(B), De.returnWorkerToPool(B); + function Oc(F) { + var B = Oe.pthreads[F]; + Ne(B), Oe.returnWorkerToPool(B); } - function Yl(F) { - var B = De.getNewWorker(); + function Lx(F) { + var B = Oe.getNewWorker(); if (!B) return 6; - De.runningWorkers.push(B), De.pthreads[F.pthread_ptr] = B, B.pthread_ptr = F.pthread_ptr; - var ne = { cmd: "run", start_routine: F.startRoutine, arg: F.arg, pthread_ptr: F.pthread_ptr }; + Oe.runningWorkers.push(B), Oe.pthreads[F.pthread_ptr] = B, B.pthread_ptr = F.pthread_ptr; + var ue = { cmd: "run", start_routine: F.startRoutine, arg: F.arg, pthread_ptr: F.pthread_ptr }; return B.runPthread = () => { - ne.time = performance.now(), B.postMessage(ne, F.transferList); - }, B.loaded && (B.runPthread(), delete B.runPthread), 0; + C && B.ref(), B.postMessage(ue, F.transferList), delete B.runPthread; + }, B.loaded && B.runPthread(), 0; } - var Ql = { varargs: void 0, get: function() { - Ql.varargs += 4; - var F = s()[Ql.varargs - 4 >> 2]; + var em = { varargs: void 0, get: function() { + em.varargs += 4; + var F = s()[em.varargs - 4 >> 2]; return F; }, getStr: function(F) { - var B = Le(F); + var B = Re(F); return B; } }; - function Oc(F) { + function tm(F) { if (w) - return Xa(1, 1, F); - he = F, Fo() || (De.terminateAllThreads(), u.onExit && u.onExit(F), pe = true), g(F, new Hi(F)); + return Qa(1, 1, F); + ye = F, Do() || (Oe.terminateAllThreads(), u.onExit && u.onExit(F), ce = true), g(F, new qi(F)); } - function kI(F, B) { - if (he = F, !B && w) - throw Jl(F), "unwind"; - Oc(F); + function Bx(F, B) { + if (ye = F, !B && w) + throw om(F), "unwind"; + tm(F); } - var Zl = kI; - function $x(F) { - if (F instanceof Hi || F == "unwind") - return he; + var rm = Bx; + function Vx(F) { + if (F instanceof qi || F == "unwind") + return ye; g(1, F); } - var De = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() { - w ? De.initWorker() : De.initMainThread(); + var Oe = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() { + w ? Oe.initWorker() : Oe.initMainThread(); }, initMainThread: function() { for (var F = 8; F--; ) - De.allocateUnusedWorker(); + Oe.allocateUnusedWorker(); }, initWorker: function() { - Y = false; + J = false; }, setExitStatus: function(F) { - he = F; + ye = F; }, terminateAllThreads: function() { - for (var F of Object.values(De.pthreads)) - De.returnWorkerToPool(F); - for (var F of De.unusedWorkers) + for (var F of Object.values(Oe.pthreads)) + Oe.returnWorkerToPool(F); + for (var F of Oe.unusedWorkers) F.terminate(); - De.unusedWorkers = []; + Oe.unusedWorkers = []; }, returnWorkerToPool: function(F) { var B = F.pthread_ptr; - delete De.pthreads[B], De.unusedWorkers.push(F), De.runningWorkers.splice(De.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, sb(B); + delete Oe.pthreads[B], Oe.unusedWorkers.push(F), Oe.runningWorkers.splice(Oe.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), hb(B); }, receiveObjectTransfer: function(F) { }, threadInitTLS: function() { - De.tlsInitFunctions.forEach((F) => F()); + Oe.tlsInitFunctions.forEach((F) => F()); }, loadWasmModuleToWorker: function(F, B) { - F.onmessage = (ne) => { - var fe = ne.data, Te = fe.cmd; - if (F.pthread_ptr && (De.currentProxiedOperationCallerThread = F.pthread_ptr), fe.targetThread && fe.targetThread != dm()) { - var Ze = De.pthreads[fe.targetThread]; - Ze ? Ze.postMessage(fe, fe.transferList) : q('Internal error! Worker sent a message "' + Te + '" to target pthread ' + fe.targetThread + ", but that thread no longer exists!"), De.currentProxiedOperationCallerThread = void 0; + F.onmessage = (Pe) => { + var fe = Pe.data, ve = fe.cmd; + if (F.pthread_ptr && (Oe.currentProxiedOperationCallerThread = F.pthread_ptr), fe.targetThread && fe.targetThread != xm()) { + var Ft = Oe.pthreads[fe.targetThread]; + Ft ? Ft.postMessage(fe, fe.transferList) : K('Internal error! Worker sent a message "' + ve + '" to target pthread ' + fe.targetThread + ", but that thread no longer exists!"), Oe.currentProxiedOperationCallerThread = void 0; return; } - Te === "processProxyingQueue" ? Mc(fe.queue) : Te === "spawnThread" ? Yl(fe) : Te === "cleanupThread" ? Dc(fe.thread) : Te === "killThread" ? _x(fe.thread) : Te === "cancelThread" ? Ex(fe.thread) : Te === "loaded" ? (F.loaded = true, B && B(F), F.runPthread && (F.runPthread(), delete F.runPthread)) : Te === "print" ? U("Thread " + fe.threadId + ": " + fe.text) : Te === "printErr" ? q("Thread " + fe.threadId + ": " + fe.text) : Te === "alert" ? alert("Thread " + fe.threadId + ": " + fe.text) : fe.target === "setimmediate" ? F.postMessage(fe) : Te === "onAbort" ? u.onAbort && u.onAbort(fe.arg) : Te && q("worker sent an unknown command " + Te), De.currentProxiedOperationCallerThread = void 0; - }, F.onerror = (ne) => { + ve === "processProxyingQueue" ? Mc(fe.queue) : ve === "spawnThread" ? Lx(fe) : ve === "cleanupThread" ? Oc(fe.thread) : ve === "killThread" ? Px(fe.thread) : ve === "cancelThread" ? Mx(fe.thread) : ve === "loaded" ? (F.loaded = true, C && F.unref(), B && B(F), F.runPthread && F.runPthread()) : ve === "print" ? U("Thread " + fe.threadId + ": " + fe.text) : ve === "printErr" ? K("Thread " + fe.threadId + ": " + fe.text) : ve === "alert" ? alert("Thread " + fe.threadId + ": " + fe.text) : fe.target === "setimmediate" ? F.postMessage(fe) : ve === "callHandler" ? u[fe.handler](...fe.args) : ve && K("worker sent an unknown command " + ve), Oe.currentProxiedOperationCallerThread = void 0; + }, F.onerror = (Pe) => { var fe = "worker sent an error!"; - throw q(fe + " " + ne.filename + ":" + ne.lineno + ": " + ne.message), ne; - }, C && (F.on("message", function(ne) { - F.onmessage({ data: ne }); - }), F.on("error", function(ne) { - F.onerror(ne); + throw K(fe + " " + Pe.filename + ":" + Pe.lineno + ": " + Pe.message), Pe; + }, C && (F.on("message", function(Pe) { + F.onmessage({ data: Pe }); + }), F.on("error", function(Pe) { + F.onerror(Pe); }), F.on("detachedExit", function() { - })), F.postMessage({ cmd: "load", urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: J, wasmModule: ie }); + })); + var ue = [], _e = ["onExit", "onAbort", "print", "printErr"]; + for (var Me of _e) + u.hasOwnProperty(Me) && ue.push(Me); + F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: te, wasmModule: ie }); }, allocateUnusedWorker: function() { - var F = _("tfjs-backend-wasm-threaded-simd.worker.js"); - De.unusedWorkers.push(new Worker(F)); + var F, B = _("tfjs-backend-wasm-threaded-simd.worker.js"); + F = new Worker(B), Oe.unusedWorkers.push(F); }, getNewWorker: function() { - return De.unusedWorkers.length == 0 && (De.allocateUnusedWorker(), De.loadWasmModuleToWorker(De.unusedWorkers[0])), De.unusedWorkers.pop(); + return Oe.unusedWorkers.length == 0 && (Oe.allocateUnusedWorker(), Oe.loadWasmModuleToWorker(Oe.unusedWorkers[0])), Oe.unusedWorkers.pop(); } }; - u.PThread = De; + u.PThread = Oe; function Pc(F) { for (; F.length > 0; ) F.shift()(u); } - function Ax(F) { - var B = ab(), ne = F(); - return fm(B), ne; - } - function NI(F) { - return F; - } - function TI(F) { - var B = /\b_Z[\w\d_]+/g; - return F.replace(B, function(ne) { - var fe = ne; - return ne === fe ? ne : fe + " [" + ne + "]"; - }); - } - function Rx() { - var F = dm(), B = s()[F + 44 >> 2], ne = s()[F + 48 >> 2], fe = B - ne; - DI(B, fe), fm(B); - } - u.establishStackSpace = Rx; - function Jl(F) { - if (w) - return Xa(2, 0, F); - try { - Zl(F); - } catch (B) { - $x(B); - } - } - var Yu = []; - function Fx(F) { - var B = Yu[F]; - return B || (F >= Yu.length && (Yu.length = F + 1), Yu[F] = B = rr.get(F)), B; - } - function Dx(F, B) { - var ne = Fx(F)(B); - Fo() ? De.setExitStatus(ne) : FI(ne); - } - u.invokeEntryPoint = Dx; - function _I() { - var F = new Error(); - if (!F.stack) { - try { - throw new Error(); - } catch (B) { - F = B; - } - if (!F.stack) - return "(no stack trace available)"; - } - return F.stack.toString(); - } - function Ox(F) { - De.tlsInitFunctions.push(F); - } - function Px(F, B) { - t6().set(F, B); - } - function Mx(F) { - $I(F, !b, 1, !x), De.threadInitTLS(); - } - function Lx(F) { - w ? postMessage({ cmd: "cleanupThread", thread: F }) : Dc(F); - } - function em(F, B, ne, fe) { - return w ? Xa(3, 1, F, B, ne, fe) : tm(F, B, ne, fe); - } - function tm(F, B, ne, fe) { - if (typeof SharedArrayBuffer == "undefined") - return q("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6; - var Te = [], Ze = 0; - if (w && (Te.length === 0 || Ze)) - return em(F, B, ne, fe); - if (Ze) - return Ze; - var Ae = { startRoutine: ne, pthread_ptr: F, arg: fe, transferList: Te }; - return w ? (Ae.cmd = "spawnThread", postMessage(Ae, Te), 0) : Yl(Ae); - } - function Bx() { - return 2097152; - } - var Vx = true; function zx() { - return Vx; + var F = xm(), B = s()[F + 52 >> 2], ue = s()[F + 56 >> 2], _e = B - ue; + BI(B, _e), ym(B); + } + u.establishStackSpace = zx; + function om(F) { + if (w) + return Qa(2, 0, F); + try { + rm(F); + } catch (B) { + Vx(B); + } + } + var tp = []; + function Wx(F) { + var B = tp[F]; + return B || (F >= tp.length && (tp.length = F + 1), tp[F] = B = rr.get(F)), B; + } + function Ux(F, B) { + var ue = Wx(F)(B); + Do() ? Oe.setExitStatus(ue) : LI(ue); + } + u.invokeEntryPoint = Ux; + function Gx(F) { + Oe.tlsInitFunctions.push(F); + } + function Hx(F) { + OI(F, !b, 1, !x), Oe.threadInitTLS(); + } + function Kx(F) { + w ? postMessage({ cmd: "cleanupThread", thread: F }) : Oc(F); + } + function nm(F, B, ue, _e) { + return w ? Qa(3, 1, F, B, ue, _e) : sm(F, B, ue, _e); + } + function sm(F, B, ue, _e) { + if (typeof SharedArrayBuffer == "undefined") + return K("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6; + var Me = [], Pe = 0; + if (w && (Me.length === 0 || Pe)) + return nm(F, B, ue, _e); + if (Pe) + return Pe; + var fe = { startRoutine: ue, pthread_ptr: F, arg: _e, transferList: Me }; + return w ? (fe.cmd = "spawnThread", postMessage(fe, Me), 0) : Lx(fe); + } + function qx() { + return 65536; + } + var jx = true; + function Xx() { + return jx; } function Mc(F) { - Atomics.store(s(), F >> 2, 1), dm() && RI(F), Atomics.compareExchange(s(), F >> 2, 1, 0); + Atomics.store(s(), F >> 2, 1), xm() && MI(F), Atomics.compareExchange(s(), F >> 2, 1, 0); } u.executeNotifiedProxyingQueue = Mc; - function Wx(F, B, ne, fe) { + function Yx(F, B, ue, _e) { if (F == B) - setTimeout(() => Mc(fe)); + setTimeout(() => Mc(_e)); else if (w) - postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: fe }); + postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: _e }); else { - var Te = De.pthreads[F]; - if (!Te) + var Me = Oe.pthreads[F]; + if (!Me) return; - Te.postMessage({ cmd: "processProxyingQueue", queue: fe }); + Me.postMessage({ cmd: "processProxyingQueue", queue: _e }); } return 1; } - function Ux(F, B, ne) { + function Qx(F, B, ue) { return -1; } - function Gx() { - Xu(""); + function Zx() { + Ki(""); } - function qi(F) { - qi.shown || (qi.shown = {}), qi.shown[F] || (qi.shown[F] = 1, C && (F = "warning: " + F), q(F)); + function ji(F) { + ji.shown || (ji.shown = {}), ji.shown[F] || (ji.shown[F] = 1, C && (F = "warning: " + F), K(F)); } - function Hx() { - C || b || qi("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); + function Jx() { + C || b || ji("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); } - function qx() { + function ey() { return Date.now(); } - function rm() { + function am() { return 2147483648; } - function Kx() { - return rm(); + function ty() { + return am(); } - var Qu; - C ? Qu = () => { + var Lc; + C ? Lc = () => { var F = process.hrtime(); return F[0] * 1e3 + F[1] / 1e6; - } : w ? Qu = () => performance.now() - u.__performance_now_clock_drift : Qu = () => performance.now(); - function jx(F, B, ne) { - o().copyWithin(F, B, B + ne); + } : Lc = () => performance.timeOrigin + performance.now(); + function ry(F, B, ue) { + o().copyWithin(F, B, B + ue); } - function Xx() { - return C ? d3().cpus().length : navigator.hardwareConcurrency; + function oy() { + return C ? b3().cpus().length : navigator.hardwareConcurrency; } - function Xa(F, B) { - var ne = arguments.length - 2, fe = arguments; - return Ax(() => { - for (var Te = ne, Ze = hm(Te * 8), Ae = Ze >> 3, Pe = 0; Pe < ne; Pe++) { - var zt = fe[2 + Pe]; - p()[Ae + Pe] = zt; + function ny(F) { + var B = gb(), ue = F(); + return ym(B), ue; + } + function Qa(F, B) { + var ue = arguments.length - 2, _e = arguments; + return ny(() => { + for (var Me = ue, Pe = bm(Me * 8), fe = Pe >> 3, ve = 0; ve < ue; ve++) { + var Ft = _e[2 + ve]; + p()[fe + ve] = Ft; } - return AI(F, Te, Ze, B); + return PI(F, Me, Pe, B); }); } - var Lc = []; - function Yx(F, B, ne) { - Lc.length = B; - for (var fe = ne >> 3, Te = 0; Te < B; Te++) - Lc[Te] = p()[fe + Te]; - var Ze = F < 0, Ae = Ze ? Tx[-F - 1] : ny[F]; - return Ae.apply(null, Lc); + var Bc = []; + function sy(F, B, ue) { + Bc.length = B; + for (var _e = ue >> 3, Me = 0; Me < B; Me++) + Bc[Me] = p()[_e + Me]; + var Pe = F < 0, fe = Pe ? Jl[-F - 1] : fy[F]; + return fe.apply(null, Bc); } - function Qx(F) { + function ay(F) { try { - return J.grow(F - Oe.byteLength + 65535 >>> 16), Nt(J.buffer), 1; + return te.grow(F - ze.byteLength + 65535 >>> 16), Nt(te.buffer), 1; } catch (B) { } } - function Zx(F) { + function iy(F) { var B = o().length; if (F = F >>> 0, F <= B) return false; - var ne = rm(); - if (F > ne) + var ue = am(); + if (F > ue) return false; - let fe = (zt, Qr) => zt + (Qr - zt % Qr) % Qr; - for (var Te = 1; Te <= 4; Te *= 2) { - var Ze = B * (1 + 0.2 / Te); - Ze = Math.min(Ze, F + 100663296); - var Ae = Math.min(ne, fe(Math.max(F, Ze), 65536)), Pe = Qx(Ae); - if (Pe) + let _e = (Ft, Qr) => Ft + (Qr - Ft % Qr) % Qr; + for (var Me = 1; Me <= 4; Me *= 2) { + var Pe = B * (1 + 0.2 / Me); + Pe = Math.min(Pe, F + 100663296); + var fe = Math.min(ue, _e(Math.max(F, Pe), 65536)), ve = ay(fe); + if (ve) return true; } return false; } - function Jx() { + function uy() { throw "unwind"; } - function om(F) { - return w ? Xa(4, 1, F) : 52; + function im(F) { + return w ? Qa(4, 1, F) : 52; } - function nm(F, B, ne, fe, Te) { - return w ? Xa(5, 1, F, B, ne, fe, Te) : 70; + function um(F, B, ue, _e, Me) { + return w ? Qa(5, 1, F, B, ue, _e, Me) : 70; } - var ey = [null, [], []]; - function ty(F, B) { - var ne = ey[F]; - B === 0 || B === 10 ? ((F === 1 ? U : q)($e(ne, 0)), ne.length = 0) : ne.push(B); + var py = [null, [], []]; + function cy(F, B) { + var ue = py[F]; + B === 0 || B === 10 ? ((F === 1 ? U : K)(Ae(ue, 0)), ue.length = 0) : ue.push(B); } - function sm(F, B, ne, fe) { + function pm(F, B, ue, _e) { if (w) - return Xa(6, 1, F, B, ne, fe); - for (var Te = 0, Ze = 0; Ze < ne; Ze++) { - var Ae = a()[B >> 2], Pe = a()[B + 4 >> 2]; + return Qa(6, 1, F, B, ue, _e); + for (var Me = 0, Pe = 0; Pe < ue; Pe++) { + var fe = a()[B >> 2], ve = a()[B + 4 >> 2]; B += 8; - for (var zt = 0; zt < Pe; zt++) - ty(F, o()[Ae + zt]); - Te += Pe; + for (var Ft = 0; Ft < ve; Ft++) + cy(F, o()[fe + Ft]); + Me += ve; } - return a()[fe >> 2] = Te, 0; + return a()[_e >> 2] = Me, 0; } - function am(F) { + function cm(F) { var B = u["_" + F]; return B; } - function ry(F, B, ne, fe, Te) { - var Ze = { string: (Dr) => { - var tp = 0; - if (Dr != null && Dr !== 0) { - var MI = (Dr.length << 2) + 1; - tp = hm(MI), pt(Dr, tp, MI); - } - return tp; - }, array: (Dr) => { - var tp = hm(Dr.length); - return Px(Dr, tp), tp; - } }; - function Ae(Dr) { - return B === "string" ? Le(Dr) : B === "boolean" ? Boolean(Dr) : Dr; - } - var Pe = am(F), zt = [], Qr = 0; - if (fe) - for (var oa = 0; oa < fe.length; oa++) { - var PI = Ze[ne[oa]]; - PI ? (Qr === 0 && (Qr = ab()), zt[oa] = PI(fe[oa])) : zt[oa] = fe[oa]; - } - var ib = Pe.apply(null, zt); - function xV(Dr) { - return Qr !== 0 && fm(Qr), Ae(Dr); - } - return ib = xV(ib), ib; + function ly(F, B) { + t10().set(F, B); } - function oy(F, B, ne, fe) { - ne = ne || []; - var Te = ne.every((Ae) => Ae === "number" || Ae === "boolean"), Ze = B !== "string"; - return Ze && Te && !fe ? am(F) : function() { - return ry(F, B, ne, arguments, fe); + function my(F, B, ue, _e, Me) { + var Pe = { string: (Dr) => { + var sp = 0; + if (Dr != null && Dr !== 0) { + var WI = (Dr.length << 2) + 1; + sp = bm(WI), pt(Dr, sp, WI); + } + return sp; + }, array: (Dr) => { + var sp = bm(Dr.length); + return ly(Dr, sp), sp; + } }; + function fe(Dr) { + return B === "string" ? Re(Dr) : B === "boolean" ? Boolean(Dr) : Dr; + } + var ve = cm(F), Ft = [], Qr = 0; + if (_e) + for (var na = 0; na < _e.length; na++) { + var zI = Pe[ue[na]]; + zI ? (Qr === 0 && (Qr = gb()), Ft[na] = zI(_e[na])) : Ft[na] = _e[na]; + } + var xb = ve.apply(null, Ft); + function OV(Dr) { + return Qr !== 0 && ym(Qr), fe(Dr); + } + return xb = OV(xb), xb; + } + function dy(F, B, ue, _e) { + ue = ue || []; + var Me = ue.every((fe) => fe === "number" || fe === "boolean"), Pe = B !== "string"; + return Pe && Me && !_e ? cm(F) : function() { + return my(F, B, ue, arguments, _e); }; } - De.init(); - var ny = [null, Oc, Jl, em, om, nm, sm], im = { __emscripten_init_main_thread_js: Mx, __emscripten_thread_cleanup: Lx, __pthread_create_js: tm, _emscripten_default_pthread_stack_size: Bx, _emscripten_get_now_is_monotonic: zx, _emscripten_notify_task_queue: Wx, _emscripten_set_offscreencanvas_size: Ux, abort: Gx, emscripten_check_blocking_allowed: Hx, emscripten_date_now: qx, emscripten_get_heap_max: Kx, emscripten_get_now: Qu, emscripten_memcpy_big: jx, emscripten_num_logical_cores: Xx, emscripten_receive_on_main_thread_js: Yx, emscripten_resize_heap: Zx, emscripten_unwind_to_js_event_loop: Jx, exit: Zl, fd_close: om, fd_seek: nm, fd_write: sm, memory: J || u.wasmMemory }, EI = kx(), sy = u.___wasm_call_ctors = function() { - return (sy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); - }, ay = u._init = function() { - return (ay = u._init = u.asm.init).apply(null, arguments); - }, iy = u._init_with_threads_count = function() { - return (iy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); - }, uy = u._get_threads_count = function() { - return (uy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); - }, py = u._register_tensor = function() { - return (py = u._register_tensor = u.asm.register_tensor).apply(null, arguments); - }, cy = u._dispose_data = function() { - return (cy = u._dispose_data = u.asm.dispose_data).apply(null, arguments); - }, ly = u._dispose = function() { - return (ly = u._dispose = u.asm.dispose).apply(null, arguments); - }, my = u._Abs = function() { - return (my = u._Abs = u.asm.Abs).apply(null, arguments); - }, dy = u._Add = function() { - return (dy = u._Add = u.asm.Add).apply(null, arguments); - }, fy = u._AddN = function() { - return (fy = u._AddN = u.asm.AddN).apply(null, arguments); - }, hy = u._All = function() { - return (hy = u._All = u.asm.All).apply(null, arguments); - }, gy = u._Any = function() { - return (gy = u._Any = u.asm.Any).apply(null, arguments); - }, xy = u._ArgMax = function() { - return (xy = u._ArgMax = u.asm.ArgMax).apply(null, arguments); - }, yy = u._AvgPool = function() { - return (yy = u._AvgPool = u.asm.AvgPool).apply(null, arguments); - }, by = u._BatchMatMul = function() { - return (by = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); - }, Cy = u._Ceil = function() { - return (Cy = u._Ceil = u.asm.Ceil).apply(null, arguments); - }, Sy = u._ClipByValue = function() { - return (Sy = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); - }, wy = u._Conv2D = function() { - return (wy = u._Conv2D = u.asm.Conv2D).apply(null, arguments); - }, Iy = u._Conv2DBackpropInput = function() { - return (Iy = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); - }, vy = u._Cos = function() { - return (vy = u._Cos = u.asm.Cos).apply(null, arguments); - }, ky = u._Cosh = function() { - return (ky = u._Cosh = u.asm.Cosh).apply(null, arguments); - }, Ny = u._CropAndResize = function() { - return (Ny = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); - }, Ty = u._Cumprod = function() { - return (Ty = u._Cumprod = u.asm.Cumprod).apply(null, arguments); - }, _y = u._Cumsum = function() { - return (_y = u._Cumsum = u.asm.Cumsum).apply(null, arguments); - }, Ey = u._DepthToSpace = function() { - return (Ey = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); - }, $y = u._DepthwiseConv2dNative = function() { - return ($y = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Ay = u._Elu = function() { - return (Ay = u._Elu = u.asm.Elu).apply(null, arguments); - }, Ry = u._Equal = function() { - return (Ry = u._Equal = u.asm.Equal).apply(null, arguments); - }, Fy = u._Exp = function() { - return (Fy = u._Exp = u.asm.Exp).apply(null, arguments); - }, Dy = u._FlipLeftRight = function() { - return (Dy = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); - }, Oy = u._Floor = function() { - return (Oy = u._Floor = u.asm.Floor).apply(null, arguments); - }, Py = u._FloorDiv = function() { - return (Py = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); - }, My = u._FusedBatchNorm = function() { - return (My = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); - }, Ly = u._FusedConv2D = function() { - return (Ly = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); - }, By = u._FusedDepthwiseConv2D = function() { - return (By = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Vy = u._Gather = function() { - return (Vy = u._Gather = u.asm.Gather).apply(null, arguments); - }, zy = u._GatherNd = function() { - return (zy = u._GatherNd = u.asm.GatherNd).apply(null, arguments); - }, Wy = u._Greater = function() { - return (Wy = u._Greater = u.asm.Greater).apply(null, arguments); - }, Uy = u._GreaterEqual = function() { - return (Uy = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); - }, Gy = u._IsNan = function() { - return (Gy = u._IsNan = u.asm.IsNan).apply(null, arguments); - }, Hy = u._LeakyRelu = function() { - return (Hy = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); - }, qy = u._Less = function() { - return (qy = u._Less = u.asm.Less).apply(null, arguments); - }, Ky = u._LessEqual = function() { - return (Ky = u._LessEqual = u.asm.LessEqual).apply(null, arguments); - }, jy = u._Log = function() { - return (jy = u._Log = u.asm.Log).apply(null, arguments); - }, Xy = u._LogicalAnd = function() { - return (Xy = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); - }, Yy = u._LogicalNot = function() { - return (Yy = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); - }, Qy = u._LogicalOr = function() { - return (Qy = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); - }, Zy = u._LogicalXor = function() { - return (Zy = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); - }, Jy = u._Max = function() { - return (Jy = u._Max = u.asm.Max).apply(null, arguments); - }, eb = u._MaxPool = function() { - return (eb = u._MaxPool = u.asm.MaxPool).apply(null, arguments); - }, um = u._Maximum = function() { - return (um = u._Maximum = u.asm.Maximum).apply(null, arguments); - }, pm = u._Mean = function() { - return (pm = u._Mean = u.asm.Mean).apply(null, arguments); - }, Bc = u._Min = function() { - return (Bc = u._Min = u.asm.Min).apply(null, arguments); - }, tb = u._Minimum = function() { - return (tb = u._Minimum = u.asm.Minimum).apply(null, arguments); - }, rb = u._MirrorPad = function() { - return (rb = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); - }, Zu = u._Multiply = function() { - return (Zu = u._Multiply = u.asm.Multiply).apply(null, arguments); - }, cm = u._Neg = function() { - return (cm = u._Neg = u.asm.Neg).apply(null, arguments); - }, Ju = u._NonMaxSuppressionV3 = function() { - return (Ju = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); - }, ep = u._NonMaxSuppressionV4 = function() { - return (ep = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); - }, ob = u._NonMaxSuppressionV5 = function() { - return (ob = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments); + Oe.init(); + var fy = [null, tm, om, nm, im, um, pm], lm = { __emscripten_init_main_thread_js: Hx, __emscripten_thread_cleanup: Kx, __pthread_create_js: sm, _emscripten_default_pthread_stack_size: qx, _emscripten_get_now_is_monotonic: Xx, _emscripten_notify_task_queue: Yx, _emscripten_set_offscreencanvas_size: Qx, abort: Zx, emscripten_check_blocking_allowed: Jx, emscripten_date_now: ey, emscripten_get_heap_max: ty, emscripten_get_now: Lc, emscripten_memcpy_big: ry, emscripten_num_logical_cores: oy, emscripten_receive_on_main_thread_js: sy, emscripten_resize_heap: iy, emscripten_unwind_to_js_event_loop: uy, exit: rm, fd_close: im, fd_seek: um, fd_write: pm, memory: te || u.wasmMemory }, FI = Ox(), hy = u.___wasm_call_ctors = function() { + return (hy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); + }, gy = u._init = function() { + return (gy = u._init = u.asm.init).apply(null, arguments); + }, xy = u._init_with_threads_count = function() { + return (xy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); + }, yy = u._get_threads_count = function() { + return (yy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); + }, by = u._register_tensor = function() { + return (by = u._register_tensor = u.asm.register_tensor).apply(null, arguments); + }, Cy = u._dispose_data = function() { + return (Cy = u._dispose_data = u.asm.dispose_data).apply(null, arguments); + }, Sy = u._dispose = function() { + return (Sy = u._dispose = u.asm.dispose).apply(null, arguments); + }, wy = u._Abs = function() { + return (wy = u._Abs = u.asm.Abs).apply(null, arguments); + }, Iy = u._Add = function() { + return (Iy = u._Add = u.asm.Add).apply(null, arguments); + }, vy = u._AddN = function() { + return (vy = u._AddN = u.asm.AddN).apply(null, arguments); + }, ky = u._All = function() { + return (ky = u._All = u.asm.All).apply(null, arguments); + }, Ny = u._Any = function() { + return (Ny = u._Any = u.asm.Any).apply(null, arguments); + }, Ty = u._ArgMax = function() { + return (Ty = u._ArgMax = u.asm.ArgMax).apply(null, arguments); + }, _y = u._AvgPool = function() { + return (_y = u._AvgPool = u.asm.AvgPool).apply(null, arguments); + }, $y = u._BatchMatMul = function() { + return ($y = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); + }, Ey = u._Ceil = function() { + return (Ey = u._Ceil = u.asm.Ceil).apply(null, arguments); + }, Ay = u._ClipByValue = function() { + return (Ay = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); + }, Ry = u._Conv2D = function() { + return (Ry = u._Conv2D = u.asm.Conv2D).apply(null, arguments); + }, Dy = u._Conv2DBackpropInput = function() { + return (Dy = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); + }, Fy = u._Cos = function() { + return (Fy = u._Cos = u.asm.Cos).apply(null, arguments); + }, Oy = u._Cosh = function() { + return (Oy = u._Cosh = u.asm.Cosh).apply(null, arguments); + }, Py = u._CropAndResize = function() { + return (Py = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); + }, My = u._Cumprod = function() { + return (My = u._Cumprod = u.asm.Cumprod).apply(null, arguments); + }, Ly = u._Cumsum = function() { + return (Ly = u._Cumsum = u.asm.Cumsum).apply(null, arguments); + }, By = u._DepthToSpace = function() { + return (By = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); + }, Vy = u._DepthwiseConv2dNative = function() { + return (Vy = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); + }, zy = u._Elu = function() { + return (zy = u._Elu = u.asm.Elu).apply(null, arguments); + }, Wy = u._Equal = function() { + return (Wy = u._Equal = u.asm.Equal).apply(null, arguments); + }, Uy = u._Exp = function() { + return (Uy = u._Exp = u.asm.Exp).apply(null, arguments); + }, Gy = u._FlipLeftRight = function() { + return (Gy = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); + }, Hy = u._Floor = function() { + return (Hy = u._Floor = u.asm.Floor).apply(null, arguments); + }, Ky = u._FloorDiv = function() { + return (Ky = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); + }, qy = u._FusedBatchNorm = function() { + return (qy = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); + }, jy = u._FusedConv2D = function() { + return (jy = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); + }, Xy = u._FusedDepthwiseConv2D = function() { + return (Xy = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, Yy = u._Gather = function() { + return (Yy = u._Gather = u.asm.Gather).apply(null, arguments); + }, Qy = u._GatherNd = function() { + return (Qy = u._GatherNd = u.asm.GatherNd).apply(null, arguments); + }, Zy = u._Greater = function() { + return (Zy = u._Greater = u.asm.Greater).apply(null, arguments); + }, Jy = u._GreaterEqual = function() { + return (Jy = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); + }, eb = u._IsNan = function() { + return (eb = u._IsNan = u.asm.IsNan).apply(null, arguments); + }, tb = u._LeakyRelu = function() { + return (tb = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); + }, rb = u._Less = function() { + return (rb = u._Less = u.asm.Less).apply(null, arguments); + }, ob = u._LessEqual = function() { + return (ob = u._LessEqual = u.asm.LessEqual).apply(null, arguments); + }, nb = u._Log = function() { + return (nb = u._Log = u.asm.Log).apply(null, arguments); + }, sb = u._LogicalAnd = function() { + return (sb = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); + }, ab = u._LogicalNot = function() { + return (ab = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); + }, ib = u._LogicalOr = function() { + return (ib = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); + }, ub = u._LogicalXor = function() { + return (ub = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); + }, pb = u._Max = function() { + return (pb = u._Max = u.asm.Max).apply(null, arguments); + }, cb = u._MaxPool = function() { + return (cb = u._MaxPool = u.asm.MaxPool).apply(null, arguments); + }, mm = u._Maximum = function() { + return (mm = u._Maximum = u.asm.Maximum).apply(null, arguments); + }, dm = u._Mean = function() { + return (dm = u._Mean = u.asm.Mean).apply(null, arguments); + }, Vc = u._Min = function() { + return (Vc = u._Min = u.asm.Min).apply(null, arguments); + }, lb = u._Minimum = function() { + return (lb = u._Minimum = u.asm.Minimum).apply(null, arguments); + }, mb = u._MirrorPad = function() { + return (mb = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); + }, rp = u._Multiply = function() { + return (rp = u._Multiply = u.asm.Multiply).apply(null, arguments); + }, fm = u._Neg = function() { + return (fm = u._Neg = u.asm.Neg).apply(null, arguments); + }, op = u._NonMaxSuppressionV3 = function() { + return (op = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); + }, np = u._NonMaxSuppressionV4 = function() { + return (np = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); + }, db = u._NonMaxSuppressionV5 = function() { + return (db = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments); }, G = u._NotEqual = function() { return (G = u._NotEqual = u.asm.NotEqual).apply(null, arguments); - }, oe = u._OneHot = function() { - return (oe = u._OneHot = u.asm.OneHot).apply(null, arguments); - }, ke = u._PadV2 = function() { - return (ke = u._PadV2 = u.asm.PadV2).apply(null, arguments); - }, je = u._Pow = function() { - return (je = u._Pow = u.asm.Pow).apply(null, arguments); + }, se = u._OneHot = function() { + return (se = u._OneHot = u.asm.OneHot).apply(null, arguments); + }, Te = u._PadV2 = function() { + return (Te = u._PadV2 = u.asm.PadV2).apply(null, arguments); + }, nt = u._Pow = function() { + return (nt = u._Pow = u.asm.Pow).apply(null, arguments); }, Tt = u._Prelu = function() { return (Tt = u._Prelu = u.asm.Prelu).apply(null, arguments); }, _t = u._Prod = function() { return (_t = u._Prod = u.asm.Prod).apply(null, arguments); }, qe = u._RealDiv = function() { return (qe = u._RealDiv = u.asm.RealDiv).apply(null, arguments); - }, We = u._Reciprocal = function() { - return (We = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); - }, Vt = u._Relu = function() { - return (Vt = u._Relu = u.asm.Relu).apply(null, arguments); + }, Ue = u._Reciprocal = function() { + return (Ue = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); + }, Wt = u._Relu = function() { + return (Wt = u._Relu = u.asm.Relu).apply(null, arguments); }, Yr = u._Relu6 = function() { return (Yr = u._Relu6 = u.asm.Relu6).apply(null, arguments); - }, ra = u._ResizeBilinear = function() { - return (ra = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments); - }, lm = u._ResizeNearestNeighbor = function() { - return (lm = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments); - }, Vc = u._Reverse = function() { - return (Vc = u._Reverse = u.asm.Reverse).apply(null, arguments); - }, nb = u._RotateWithOffset = function() { - return (nb = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments); + }, oa = u._ResizeBilinear = function() { + return (oa = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments); + }, hm = u._ResizeNearestNeighbor = function() { + return (hm = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments); + }, zc = u._Reverse = function() { + return (zc = u._Reverse = u.asm.Reverse).apply(null, arguments); + }, fb = u._RotateWithOffset = function() { + return (fb = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments); }, hr = u._Round = function() { return (hr = u._Round = u.asm.Round).apply(null, arguments); - }, Ya = u._Rsqrt = function() { - return (Ya = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); - }, mm = u._ScatterNd = function() { - return (mm = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); - }, BB = u._SelectV2 = function() { - return (BB = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); - }, VB = u._Sigmoid = function() { - return (VB = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); - }, zB = u._Sin = function() { - return (zB = u._Sin = u.asm.Sin).apply(null, arguments); - }, WB = u._Softmax = function() { - return (WB = u._Softmax = u.asm.Softmax).apply(null, arguments); - }, UB = u._SparseFillEmptyRows = function() { - return (UB = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); - }, GB = u._SparseReshape = function() { - return (GB = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); - }, HB = u._SparseSegmentReduction = function() { - return (HB = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); - }, qB = u._Sqrt = function() { - return (qB = u._Sqrt = u.asm.Sqrt).apply(null, arguments); - }, KB = u._Square = function() { - return (KB = u._Square = u.asm.Square).apply(null, arguments); - }, jB = u._SquaredDifference = function() { - return (jB = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); - }, XB = u._Step = function() { - return (XB = u._Step = u.asm.Step).apply(null, arguments); - }, YB = u._StridedSlice = function() { - return (YB = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); - }, QB = u._Sub = function() { - return (QB = u._Sub = u.asm.Sub).apply(null, arguments); - }, ZB = u._Sum = function() { - return (ZB = u._Sum = u.asm.Sum).apply(null, arguments); - }, JB = u._Tan = function() { - return (JB = u._Tan = u.asm.Tan).apply(null, arguments); - }, eV = u._Tanh = function() { - return (eV = u._Tanh = u.asm.Tanh).apply(null, arguments); - }, tV = u._Tile = function() { - return (tV = u._Tile = u.asm.Tile).apply(null, arguments); - }, rV = u._TopK = function() { - return (rV = u._TopK = u.asm.TopK).apply(null, arguments); - }, oV = u._Transform = function() { - return (oV = u._Transform = u.asm.Transform).apply(null, arguments); - }, nV = u._Transpose = function() { - return (nV = u._Transpose = u.asm.Transpose).apply(null, arguments); - }, sV = u.__FusedMatMul = function() { - return (sV = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); - }, aV = u._malloc = function() { - return (aV = u._malloc = u.asm.malloc).apply(null, arguments); - }, iV = u._free = function() { - return (iV = u._free = u.asm.free).apply(null, arguments); - }, uV = u.__emscripten_tls_init = function() { - return (uV = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); - }, dm = u._pthread_self = function() { - return (dm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); - }, pV = u.___errno_location = function() { - return (pV = u.___errno_location = u.asm.__errno_location).apply(null, arguments); - }, $I = u.__emscripten_thread_init = function() { - return ($I = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); - }, cV = u.__emscripten_thread_crashed = function() { - return (cV = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); - }, lV = u._emscripten_main_thread_process_queued_calls = function() { - return (lV = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); - }, mV = u._emscripten_main_browser_thread_id = function() { - return (mV = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); - }, AI = u._emscripten_run_in_main_runtime_thread_js = function() { - return (AI = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); - }, dV = u._emscripten_dispatch_to_thread_ = function() { - return (dV = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); - }, RI = u.__emscripten_proxy_execute_task_queue = function() { - return (RI = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); - }, sb = u.__emscripten_thread_free_data = function() { - return (sb = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); - }, FI = u.__emscripten_thread_exit = function() { - return (FI = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); - }, DI = u._emscripten_stack_set_limits = function() { - return (DI = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); - }, ab = u.stackSave = function() { - return (ab = u.stackSave = u.asm.stackSave).apply(null, arguments); - }, fm = u.stackRestore = function() { - return (fm = u.stackRestore = u.asm.stackRestore).apply(null, arguments); - }, hm = u.stackAlloc = function() { - return (hm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); - }, fV = u.dynCall_iijjiiii = function() { - return (fV = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); - }, hV = u.dynCall_jiji = function() { - return (hV = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); + }, Za = u._Rsqrt = function() { + return (Za = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); + }, gm = u._ScatterNd = function() { + return (gm = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); + }, oV = u._SelectV2 = function() { + return (oV = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); + }, nV = u._Sigmoid = function() { + return (nV = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); + }, sV = u._Sin = function() { + return (sV = u._Sin = u.asm.Sin).apply(null, arguments); + }, aV = u._Softmax = function() { + return (aV = u._Softmax = u.asm.Softmax).apply(null, arguments); + }, iV = u._SparseFillEmptyRows = function() { + return (iV = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); + }, uV = u._SparseReshape = function() { + return (uV = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); + }, pV = u._SparseSegmentReduction = function() { + return (pV = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); + }, cV = u._Sqrt = function() { + return (cV = u._Sqrt = u.asm.Sqrt).apply(null, arguments); + }, lV = u._Square = function() { + return (lV = u._Square = u.asm.Square).apply(null, arguments); + }, mV = u._SquaredDifference = function() { + return (mV = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); + }, dV = u._Step = function() { + return (dV = u._Step = u.asm.Step).apply(null, arguments); + }, fV = u._StridedSlice = function() { + return (fV = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); + }, hV = u._Sub = function() { + return (hV = u._Sub = u.asm.Sub).apply(null, arguments); + }, gV = u._Sum = function() { + return (gV = u._Sum = u.asm.Sum).apply(null, arguments); + }, xV = u._Tan = function() { + return (xV = u._Tan = u.asm.Tan).apply(null, arguments); + }, yV = u._Tanh = function() { + return (yV = u._Tanh = u.asm.Tanh).apply(null, arguments); + }, bV = u._Tile = function() { + return (bV = u._Tile = u.asm.Tile).apply(null, arguments); + }, CV = u._TopK = function() { + return (CV = u._TopK = u.asm.TopK).apply(null, arguments); + }, SV = u._Transform = function() { + return (SV = u._Transform = u.asm.Transform).apply(null, arguments); + }, wV = u._Transpose = function() { + return (wV = u._Transpose = u.asm.Transpose).apply(null, arguments); + }, IV = u.__FusedMatMul = function() { + return (IV = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); + }, vV = u._malloc = function() { + return (vV = u._malloc = u.asm.malloc).apply(null, arguments); + }, kV = u._free = function() { + return (kV = u._free = u.asm.free).apply(null, arguments); + }, NV = u.__emscripten_tls_init = function() { + return (NV = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); + }, xm = u._pthread_self = function() { + return (xm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); + }, TV = u.___errno_location = function() { + return (TV = u.___errno_location = u.asm.__errno_location).apply(null, arguments); + }, OI = u.__emscripten_thread_init = function() { + return (OI = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); + }, _V = u.__emscripten_thread_crashed = function() { + return (_V = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); + }, $V = u._emscripten_main_thread_process_queued_calls = function() { + return ($V = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); + }, EV = u._emscripten_main_browser_thread_id = function() { + return (EV = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); + }, PI = u._emscripten_run_in_main_runtime_thread_js = function() { + return (PI = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); + }, AV = u._emscripten_dispatch_to_thread_ = function() { + return (AV = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); + }, MI = u.__emscripten_proxy_execute_task_queue = function() { + return (MI = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); + }, hb = u.__emscripten_thread_free_data = function() { + return (hb = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); + }, LI = u.__emscripten_thread_exit = function() { + return (LI = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); + }, BI = u._emscripten_stack_set_limits = function() { + return (BI = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); + }, gb = u.stackSave = function() { + return (gb = u.stackSave = u.asm.stackSave).apply(null, arguments); + }, ym = u.stackRestore = function() { + return (ym = u.stackRestore = u.asm.stackRestore).apply(null, arguments); + }, bm = u.stackAlloc = function() { + return (bm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); + }, RV = u.dynCall_iijjiiii = function() { + return (RV = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); + }, DV = u.dynCall_jiji = function() { + return (DV = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); }; - u.keepRuntimeAlive = Fo, u.wasmMemory = J, u.cwrap = oy, u.ExitStatus = Hi, u.PThread = De; - var gm; - ta = function F() { - gm || OI(), gm || (ta = F); + u.keepRuntimeAlive = Do, u.wasmMemory = te, u.cwrap = dy, u.ExitStatus = qi, u.PThread = Oe; + var Cm; + ra = function F() { + Cm || VI(), Cm || (ra = F); }; - function OI(F) { - if (F = F || f, ja > 0) + function VI(F) { + if (F = F || f, Ya > 0) return; if (w) { - c(u), Kt(), postMessage({ cmd: "loaded" }); + c(u), qt(), startWorker(u); return; } - if (Ka(), ja > 0) + if (hs(), Ya > 0) return; function B() { - gm || (gm = true, u.calledRun = true, !pe && (Kt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), ea())); + Cm || (Cm = true, u.calledRun = true, !ce && (qt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), ta())); } u.setStatus ? (u.setStatus("Running..."), setTimeout(function() { setTimeout(function() { @@ -2119,26 +2087,26 @@ var f3 = qt((wg, jw) => { if (u.preInit) for (typeof u.preInit == "function" && (u.preInit = [u.preInit]); u.preInit.length > 0; ) u.preInit.pop()(); - OI(); - var xm; - m && (xm = { uncaughtException: process.listeners("uncaughtException").filter(function(F) { + VI(); + var Sm; + m && (Sm = { uncaughtException: process.listeners("uncaughtException").filter(function(F) { return !m.uncaughtException.indexOf(F) > -1; }), unhandledRejection: process.listeners("unhandledRejection").filter(function(F) { return !m.unhandledRejection.indexOf(F) > -1; }) }); - var ym; + var wm; if (typeof WasmBackendModule != "undefined") - ym = WasmBackendModule; + wm = WasmBackendModule; else if (typeof e != "undefined") - ym = e; + wm = e; else throw new Error("Could not find wasm module in post.js"); - if (xm) { - var gV = ym._dispose; - ym._dispose = function() { - gV(), xm.uncaughtException.forEach(function(F) { + if (Sm) { + var FV = wm._dispose; + wm._dispose = function() { + FV(), Sm.uncaughtException.forEach(function(F) { process.removeListener("uncaughtException", F); - }), xm.unhandledRejection.forEach(function(F) { + }), Sm.unhandledRejection.forEach(function(F) { process.removeListener("unhandledRejection", F); }); }; @@ -2146,105 +2114,104 @@ var f3 = qt((wg, jw) => { return e.ready; }; })(); - typeof wg == "object" && typeof jw == "object" ? jw.exports = Kw : typeof define == "function" && define.amd ? define([], function() { - return Kw; - }) : typeof wg == "object" && (wg.WasmBackendModuleThreadedSimd = Kw); + typeof wg == "object" && typeof aI == "object" ? aI.exports = sI : typeof define == "function" && define.amd ? define([], function() { + return sI; + }) : typeof wg == "object" && (wg.WasmBackendModuleThreadedSimd = sI); }); -var g3 = qt((kkt, h3) => { - h3.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" -");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; +var w3 = Kt((a1t, S3) => { + S3.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" +");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; }); -var x3 = qt((Ig, Yw) => { - var Xw = (() => { +var I3 = Kt((Ig, uI) => { + var iI = (() => { var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; return typeof __filename != "undefined" && (r = r || __filename), function(e) { e = e || {}; - var t6 = typeof e != "undefined" ? e : {}, o, n; - t6.ready = new Promise(function(G, oe) { - o = G, n = oe; + var t10 = typeof e != "undefined" ? e : {}, o, n; + t10.ready = new Promise(function(G, se) { + o = G, n = se; }); var s; typeof process != "undefined" && process.listeners && (s = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }); - var a = Object.assign({}, t6), i = [], p = "./this.program", u = (G, oe) => { - throw oe; + var a = Object.assign({}, t10), i = [], p = "./this.program", u = (G, se) => { + throw se; }, c = typeof window == "object", l = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", d = ""; function f(G) { - return t6.locateFile ? t6.locateFile(G, d) : d + G; + return t10.locateFile ? t10.locateFile(G, d) : d + G; } var h, g, x, b; function C(G) { - if (G instanceof ju) + if (G instanceof Ju) return; - $("exiting due to exception: " + G); + E("exiting due to exception: " + G); } if (m) { - l ? d = Rl().dirname(d) + "/" : d = __dirname + "/"; - var w, k; - typeof bm == "function" && (w = qw(), k = Rl()), h = (G, oe) => (G = k.normalize(G), w.readFileSync(G, oe ? void 0 : "utf8")), x = (G) => { - var oe = h(G, true); - return oe.buffer || (oe = new Uint8Array(oe)), oe; - }, g = (G, oe, ke) => { - G = k.normalize(G), w.readFile(G, function(je, Tt) { - je ? ke(je) : oe(Tt.buffer); + var w = oI(), k = nI(); + l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, se) => (G = hs(G) ? new URL(G) : k.normalize(G), w.readFileSync(G, se ? void 0 : "utf8")), x = (G) => { + var se = h(G, true); + return se.buffer || (se = new Uint8Array(se)), se; + }, g = (G, se, Te) => { + G = hs(G) ? new URL(G) : k.normalize(G), w.readFile(G, function(nt, Tt) { + nt ? Te(nt) : se(Tt.buffer); }); }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(G) { - if (!(G instanceof ju)) + if (!(G instanceof Ju)) throw G; }), process.on("unhandledRejection", function(G) { throw G; - }), u = (G, oe) => { + }), u = (G, se) => { if (at()) - throw process.exitCode = G, oe; - C(oe), process.exit(G); - }, t6.inspect = function() { + throw process.exitCode = G, se; + C(se), process.exit(G); + }, t10.inspect = function() { return "[Emscripten Module object]"; }; } else (c || l) && (l ? d = self.location.href : typeof document != "undefined" && document.currentScript && (d = document.currentScript.src), r && (d = r), d.indexOf("blob:") !== 0 ? d = d.substr(0, d.replace(/[?#].*/, "").lastIndexOf("/") + 1) : d = "", h = (G) => { - var oe = new XMLHttpRequest(); - return oe.open("GET", G, false), oe.send(null), oe.responseText; + var se = new XMLHttpRequest(); + return se.open("GET", G, false), se.send(null), se.responseText; }, l && (x = (G) => { - var oe = new XMLHttpRequest(); - return oe.open("GET", G, false), oe.responseType = "arraybuffer", oe.send(null), new Uint8Array(oe.response); - }), g = (G, oe, ke) => { - var je = new XMLHttpRequest(); - je.open("GET", G, true), je.responseType = "arraybuffer", je.onload = () => { - if (je.status == 200 || je.status == 0 && je.response) { - oe(je.response); + var se = new XMLHttpRequest(); + return se.open("GET", G, false), se.responseType = "arraybuffer", se.send(null), new Uint8Array(se.response); + }), g = (G, se, Te) => { + var nt = new XMLHttpRequest(); + nt.open("GET", G, true), nt.responseType = "arraybuffer", nt.onload = () => { + if (nt.status == 200 || nt.status == 0 && nt.response) { + se(nt.response); return; } - ke(); - }, je.onerror = ke, je.send(null); + Te(); + }, nt.onerror = Te, nt.send(null); }, b = (G) => document.title = G); - var _ = t6.print || console.log.bind(console), $ = t6.printErr || console.warn.bind(console); - Object.assign(t6, a), a = null, t6.arguments && (i = t6.arguments), t6.thisProgram && (p = t6.thisProgram), t6.quit && (u = t6.quit); + var _ = t10.print || console.log.bind(console), E = t10.printErr || console.warn.bind(console); + Object.assign(t10, a), a = null, t10.arguments && (i = t10.arguments), t10.thisProgram && (p = t10.thisProgram), t10.quit && (u = t10.quit); var A = 4, R; - t6.wasmBinary && (R = t6.wasmBinary); - var D = t6.noExitRuntime || true; - typeof WebAssembly != "object" && pr("no native wasm support detected"); + t10.wasmBinary && (R = t10.wasmBinary); + var D = t10.noExitRuntime || true; + typeof WebAssembly != "object" && cr("no native wasm support detected"); var P, M = false, L; - function W(G, oe) { - G || pr(oe); + function V(G, se) { + G || cr(se); } - var V = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; - function U(G, oe, ke) { - for (var je = oe + ke, Tt = oe; G[Tt] && !(Tt >= je); ) + var z = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function U(G, se, Te) { + for (var nt = se + Te, Tt = se; G[Tt] && !(Tt >= nt); ) ++Tt; - if (Tt - oe > 16 && G.buffer && V) - return V.decode(G.subarray(oe, Tt)); - for (var _t = ""; oe < Tt; ) { - var qe = G[oe++]; + if (Tt - se > 16 && G.buffer && z) + return z.decode(G.subarray(se, Tt)); + for (var _t = ""; se < Tt; ) { + var qe = G[se++]; if (!(qe & 128)) { _t += String.fromCharCode(qe); continue; } - var We = G[oe++] & 63; + var Ue = G[se++] & 63; if ((qe & 224) == 192) { - _t += String.fromCharCode((qe & 31) << 6 | We); + _t += String.fromCharCode((qe & 31) << 6 | Ue); continue; } - var Vt = G[oe++] & 63; - if ((qe & 240) == 224 ? qe = (qe & 15) << 12 | We << 6 | Vt : qe = (qe & 7) << 18 | We << 12 | Vt << 6 | G[oe++] & 63, qe < 65536) + var Wt = G[se++] & 63; + if ((qe & 240) == 224 ? qe = (qe & 15) << 12 | Ue << 6 | Wt : qe = (qe & 7) << 18 | Ue << 12 | Wt << 6 | G[se++] & 63, qe < 65536) _t += String.fromCharCode(qe); else { var Yr = qe - 65536; @@ -2253,553 +2220,530 @@ var x3 = qt((Ig, Yw) => { } return _t; } - function q(G, oe) { - return G ? U(ee, G, oe) : ""; + function K(G, se) { + return G ? U(oe, G, se) : ""; } - function H(G, oe, ke, je) { - if (!(je > 0)) + function H(G, se, Te, nt) { + if (!(nt > 0)) return 0; - for (var Tt = ke, _t = ke + je - 1, qe = 0; qe < G.length; ++qe) { - var We = G.charCodeAt(qe); - if (We >= 55296 && We <= 57343) { - var Vt = G.charCodeAt(++qe); - We = 65536 + ((We & 1023) << 10) | Vt & 1023; + for (var Tt = Te, _t = Te + nt - 1, qe = 0; qe < G.length; ++qe) { + var Ue = G.charCodeAt(qe); + if (Ue >= 55296 && Ue <= 57343) { + var Wt = G.charCodeAt(++qe); + Ue = 65536 + ((Ue & 1023) << 10) | Wt & 1023; } - if (We <= 127) { - if (ke >= _t) + if (Ue <= 127) { + if (Te >= _t) break; - oe[ke++] = We; - } else if (We <= 2047) { - if (ke + 1 >= _t) + se[Te++] = Ue; + } else if (Ue <= 2047) { + if (Te + 1 >= _t) break; - oe[ke++] = 192 | We >> 6, oe[ke++] = 128 | We & 63; - } else if (We <= 65535) { - if (ke + 2 >= _t) + se[Te++] = 192 | Ue >> 6, se[Te++] = 128 | Ue & 63; + } else if (Ue <= 65535) { + if (Te + 2 >= _t) break; - oe[ke++] = 224 | We >> 12, oe[ke++] = 128 | We >> 6 & 63, oe[ke++] = 128 | We & 63; + se[Te++] = 224 | Ue >> 12, se[Te++] = 128 | Ue >> 6 & 63, se[Te++] = 128 | Ue & 63; } else { - if (ke + 3 >= _t) + if (Te + 3 >= _t) break; - oe[ke++] = 240 | We >> 18, oe[ke++] = 128 | We >> 12 & 63, oe[ke++] = 128 | We >> 6 & 63, oe[ke++] = 128 | We & 63; + se[Te++] = 240 | Ue >> 18, se[Te++] = 128 | Ue >> 12 & 63, se[Te++] = 128 | Ue >> 6 & 63, se[Te++] = 128 | Ue & 63; } } - return oe[ke] = 0, ke - Tt; + return se[Te] = 0, Te - Tt; } - function j(G, oe, ke) { - return H(G, ee, oe, ke); + function q(G, se, Te) { + return H(G, oe, se, Te); } - var X, Z, ee, Y, J, ie, pe, he, we; - function ve(G) { - X = G, t6.HEAP8 = Z = new Int8Array(G), t6.HEAP16 = Y = new Int16Array(G), t6.HEAP32 = ie = new Int32Array(G), t6.HEAPU8 = ee = new Uint8Array(G), t6.HEAPU16 = J = new Uint16Array(G), t6.HEAPU32 = pe = new Uint32Array(G), t6.HEAPF32 = he = new Float32Array(G), t6.HEAPF64 = we = new Float64Array(G); + var Z, ee, oe, J, te, ie, ce, ye, Ne; + function Ie(G) { + Z = G, t10.HEAP8 = ee = new Int8Array(G), t10.HEAP16 = J = new Int16Array(G), t10.HEAP32 = ie = new Int32Array(G), t10.HEAPU8 = oe = new Uint8Array(G), t10.HEAPU16 = te = new Uint16Array(G), t10.HEAPU32 = ce = new Uint32Array(G), t10.HEAPF32 = ye = new Float32Array(G), t10.HEAPF64 = Ne = new Float64Array(G); } - var $e = t6.INITIAL_MEMORY || 16777216, Le, nt = [], pt = [], Oe = [], mt = false; + var Ae = t10.INITIAL_MEMORY || 16777216, Re, ot = [], pt = [], ze = [], ct = false; function at() { return D; } function ft() { - if (t6.preRun) - for (typeof t6.preRun == "function" && (t6.preRun = [t6.preRun]); t6.preRun.length; ) - Ot(t6.preRun.shift()); - ta(nt); + if (t10.preRun) + for (typeof t10.preRun == "function" && (t10.preRun = [t10.preRun]); t10.preRun.length; ) + Pt(t10.preRun.shift()); + ra(ot); } - function wt() { - mt = true, ta(pt); + function ht() { + ct = true, ra(pt); } - function Fr() { - if (t6.postRun) - for (typeof t6.postRun == "function" && (t6.postRun = [t6.postRun]); t6.postRun.length; ) - er(t6.postRun.shift()); - ta(Oe); + function Rr() { + if (t10.postRun) + for (typeof t10.postRun == "function" && (t10.postRun = [t10.postRun]); t10.postRun.length; ) + er(t10.postRun.shift()); + ra(ze); } - function Ot(G) { - nt.unshift(G); + function Pt(G) { + ot.unshift(G); } - function Kr(G) { + function qr(G) { pt.unshift(G); } function er(G) { - Oe.unshift(G); + ze.unshift(G); } var Nt = 0, tr = null, rr = null; function jr(G) { - Nt++, t6.monitorRunDependencies && t6.monitorRunDependencies(Nt); + Nt++, t10.monitorRunDependencies && t10.monitorRunDependencies(Nt); } function Xr(G) { - if (Nt--, t6.monitorRunDependencies && t6.monitorRunDependencies(Nt), Nt == 0 && (tr !== null && (clearInterval(tr), tr = null), rr)) { - var oe = rr; - rr = null, oe(); + if (Nt--, t10.monitorRunDependencies && t10.monitorRunDependencies(Nt), Nt == 0 && (tr !== null && (clearInterval(tr), tr = null), rr)) { + var se = rr; + rr = null, se(); } } - function pr(G) { - t6.onAbort && t6.onAbort(G), G = "Aborted(" + G + ")", $(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info."; - var oe = new WebAssembly.RuntimeError(G); - throw n(oe), oe; + function cr(G) { + t10.onAbort && t10.onAbort(G), G = "Aborted(" + G + ")", E(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info."; + var se = new WebAssembly.RuntimeError(G); + throw n(se), se; } - var Js = "data:application/octet-stream;base64,"; - function Fo(G) { - return G.startsWith(Js); + var ea = "data:application/octet-stream;base64,"; + function Do(G) { + return G.startsWith(ea); } - function Ka(G) { + function hs(G) { return G.startsWith("file://"); } - var Kt; - Kt = "tfjs-backend-wasm.wasm", Fo(Kt) || (Kt = f(Kt)); - function ea(G) { + var qt; + qt = "tfjs-backend-wasm.wasm", Do(qt) || (qt = f(qt)); + function ta(G) { try { - if (G == Kt && R) + if (G == qt && R) return new Uint8Array(R); if (x) return x(G); throw "both async and sync fetching of the wasm failed"; - } catch (oe) { - pr(oe); + } catch (se) { + cr(se); } } - function Ac() { + function Dc() { if (!R && (c || l)) { - if (typeof fetch == "function" && !Ka(Kt)) - return fetch(Kt, { credentials: "same-origin" }).then(function(G) { + if (typeof fetch == "function" && !hs(qt)) + return fetch(qt, { credentials: "same-origin" }).then(function(G) { if (!G.ok) - throw "failed to load wasm binary file at '" + Kt + "'"; + throw "failed to load wasm binary file at '" + qt + "'"; return G.arrayBuffer(); }).catch(function() { - return ea(Kt); + return ta(qt); }); if (g) - return new Promise(function(G, oe) { - g(Kt, function(ke) { - G(new Uint8Array(ke)); - }, oe); + return new Promise(function(G, se) { + g(qt, function(Te) { + G(new Uint8Array(Te)); + }, se); }); } return Promise.resolve().then(function() { - return ea(Kt); + return ta(qt); }); } - function Rc() { + function Fc() { var G = { env: Oc, wasi_snapshot_preview1: Oc }; - function oe(qe, We) { - var Vt = qe.exports; - t6.asm = Vt, P = t6.asm.memory, ve(P.buffer), Le = t6.asm.__indirect_function_table, Kr(t6.asm.__wasm_call_ctors), Xr("wasm-instantiate"); + function se(qe, Ue) { + var Wt = qe.exports; + t10.asm = Wt, P = t10.asm.memory, Ie(P.buffer), Re = t10.asm.__indirect_function_table, qr(t10.asm.__wasm_call_ctors), Xr("wasm-instantiate"); } jr("wasm-instantiate"); - function ke(qe) { - oe(qe.instance); + function Te(qe) { + se(qe.instance); } - function je(qe) { - return Ac().then(function(We) { - return WebAssembly.instantiate(We, G); - }).then(function(We) { - return We; - }).then(qe, function(We) { - $("failed to asynchronously prepare wasm: " + We), pr(We); + function nt(qe) { + return Dc().then(function(Ue) { + return WebAssembly.instantiate(Ue, G); + }).then(function(Ue) { + return Ue; + }).then(qe, function(Ue) { + E("failed to asynchronously prepare wasm: " + Ue), cr(Ue); }); } function Tt() { - return !R && typeof WebAssembly.instantiateStreaming == "function" && !Fo(Kt) && !Ka(Kt) && !m && typeof fetch == "function" ? fetch(Kt, { credentials: "same-origin" }).then(function(qe) { - var We = WebAssembly.instantiateStreaming(qe, G); - return We.then(ke, function(Vt) { - return $("wasm streaming compile failed: " + Vt), $("falling back to ArrayBuffer instantiation"), je(ke); + return !R && typeof WebAssembly.instantiateStreaming == "function" && !Do(qt) && !hs(qt) && !m && typeof fetch == "function" ? fetch(qt, { credentials: "same-origin" }).then(function(qe) { + var Ue = WebAssembly.instantiateStreaming(qe, G); + return Ue.then(Te, function(Wt) { + return E("wasm streaming compile failed: " + Wt), E("falling back to ArrayBuffer instantiation"), nt(Te); }); - }) : je(ke); + }) : nt(Te); } - if (t6.instantiateWasm) + if (t10.instantiateWasm) try { - var _t = t6.instantiateWasm(G, oe); + var _t = t10.instantiateWasm(G, se); return _t; } catch (qe) { - $("Module.instantiateWasm callback failed with error: " + qe), n(qe); + E("Module.instantiateWasm callback failed with error: " + qe), n(qe); } return Tt().catch(n), {}; } - var SI, ja; - function ju(G) { + var AI, Ya; + function Ju(G) { this.name = "ExitStatus", this.message = "Program terminated with exit(" + G + ")", this.status = G; } - function ta(G) { + function ra(G) { for (; G.length > 0; ) - G.shift()(t6); + G.shift()(t10); } - function wI(G) { - return G; + function Rx() { + cr(""); } - function II(G) { - var oe = /\b_Z[\w\d_]+/g; - return G.replace(oe, function(ke) { - var je = ke; - return ke === je ? ke : je + " [" + ke + "]"; - }); - } - function Xu() { - var G = new Error(); - if (!G.stack) { - try { - throw new Error(); - } catch (oe) { - G = oe; - } - if (!G.stack) - return "(no stack trace available)"; - } - return G.stack.toString(); - } - function Ix(G, oe) { - Z.set(G, oe); - } - function jl() { - pr(""); - } - function Fc() { + function Yl() { return 2147483648; } - function fr() { - return Fc(); + function Ki() { + return Yl(); } - function Xl(G, oe, ke) { - ee.copyWithin(G, oe, oe + ke); + function Dx(G, se, Te) { + oe.copyWithin(G, se, se + Te); } - function vx(G) { + function Ql(G) { try { - return P.grow(G - X.byteLength + 65535 >>> 16), ve(P.buffer), 1; - } catch (oe) { + return P.grow(G - Z.byteLength + 65535 >>> 16), Ie(P.buffer), 1; + } catch (se) { } } - function kx(G) { - var oe = ee.length; + function ep(G) { + var se = oe.length; G = G >>> 0; - var ke = Fc(); - if (G > ke) + var Te = Yl(); + if (G > Te) return false; - let je = (Vt, Yr) => Vt + (Yr - Vt % Yr) % Yr; + let nt = (Wt, Yr) => Wt + (Yr - Wt % Yr) % Yr; for (var Tt = 1; Tt <= 4; Tt *= 2) { - var _t = oe * (1 + 0.2 / Tt); + var _t = se * (1 + 0.2 / Tt); _t = Math.min(_t, G + 100663296); - var qe = Math.min(ke, je(Math.max(G, _t), 65536)), We = vx(qe); - if (We) + var qe = Math.min(Te, nt(Math.max(G, _t), 65536)), Ue = Ql(qe); + if (Ue) return true; } return false; } - var Nx = { varargs: void 0, get: function() { - Nx.varargs += 4; - var G = ie[Nx.varargs - 4 >> 2]; + var lr = { varargs: void 0, get: function() { + lr.varargs += 4; + var G = ie[lr.varargs - 4 >> 2]; return G; }, getStr: function(G) { - var oe = q(G); - return oe; + var se = K(G); + return se; } }; - function vI(G) { + function Zl(G) { return 52; } - function Tx(G, oe, ke, je, Tt) { + function Fx(G, se, Te, nt, Tt) { return 70; } - var Hi = [null, [], []]; - function _x(G, oe) { - var ke = Hi[G]; - oe === 0 || oe === 10 ? ((G === 1 ? _ : $)(U(ke, 0)), ke.length = 0) : ke.push(oe); + var Ox = [null, [], []]; + function RI(G, se) { + var Te = Ox[G]; + se === 0 || se === 10 ? ((G === 1 ? _ : E)(U(Te, 0)), Te.length = 0) : Te.push(se); } - function Ex(G, oe, ke, je) { - for (var Tt = 0, _t = 0; _t < ke; _t++) { - var qe = pe[oe >> 2], We = pe[oe + 4 >> 2]; - oe += 8; - for (var Vt = 0; Vt < We; Vt++) - _x(G, ee[qe + Vt]); - Tt += We; + function DI(G, se, Te, nt) { + for (var Tt = 0, _t = 0; _t < Te; _t++) { + var qe = ce[se >> 2], Ue = ce[se + 4 >> 2]; + se += 8; + for (var Wt = 0; Wt < Ue; Wt++) + RI(G, oe[qe + Wt]); + Tt += Ue; } - return pe[je >> 2] = Tt, 0; + return ce[nt >> 2] = Tt, 0; } - function Dc(G) { - var oe = t6["_" + G]; - return oe; + function Jl(G) { + var se = t10["_" + G]; + return se; } - function Yl(G, oe, ke, je, Tt) { + function qi(G, se) { + ee.set(G, se); + } + function Px(G, se, Te, nt, Tt) { var _t = { string: (hr) => { - var Ya = 0; + var Za = 0; if (hr != null && hr !== 0) { - var mm = (hr.length << 2) + 1; - Ya = Bc(mm), j(hr, Ya, mm); + var gm = (hr.length << 2) + 1; + Za = Vc(gm), q(hr, Za, gm); } - return Ya; + return Za; }, array: (hr) => { - var Ya = Bc(hr.length); - return Ix(hr, Ya), Ya; + var Za = Vc(hr.length); + return qi(hr, Za), Za; } }; function qe(hr) { - return oe === "string" ? q(hr) : oe === "boolean" ? Boolean(hr) : hr; + return se === "string" ? K(hr) : se === "boolean" ? Boolean(hr) : hr; } - var We = Dc(G), Vt = [], Yr = 0; - if (je) - for (var ra = 0; ra < je.length; ra++) { - var lm = _t[ke[ra]]; - lm ? (Yr === 0 && (Yr = um()), Vt[ra] = lm(je[ra])) : Vt[ra] = je[ra]; + var Ue = Jl(G), Wt = [], Yr = 0; + if (nt) + for (var oa = 0; oa < nt.length; oa++) { + var hm = _t[Te[oa]]; + hm ? (Yr === 0 && (Yr = mm()), Wt[oa] = hm(nt[oa])) : Wt[oa] = nt[oa]; } - var Vc = We.apply(null, Vt); - function nb(hr) { - return Yr !== 0 && pm(Yr), qe(hr); + var zc = Ue.apply(null, Wt); + function fb(hr) { + return Yr !== 0 && dm(Yr), qe(hr); } - return Vc = nb(Vc), Vc; + return zc = fb(zc), zc; } - function Ql(G, oe, ke, je) { - ke = ke || []; - var Tt = ke.every((qe) => qe === "number" || qe === "boolean"), _t = oe !== "string"; - return _t && Tt && !je ? Dc(G) : function() { - return Yl(G, oe, ke, arguments, je); + function Mx(G, se, Te, nt) { + Te = Te || []; + var Tt = Te.every((qe) => qe === "number" || qe === "boolean"), _t = se !== "string"; + return _t && Tt && !nt ? Jl(G) : function() { + return Px(G, se, Te, arguments, nt); }; } - var Oc = { abort: jl, emscripten_get_heap_max: fr, emscripten_memcpy_big: Xl, emscripten_resize_heap: kx, fd_close: vI, fd_seek: Tx, fd_write: Ex }, kI = Rc(), Zl = t6.___wasm_call_ctors = function() { - return (Zl = t6.___wasm_call_ctors = t6.asm.__wasm_call_ctors).apply(null, arguments); - }, $x = t6._init = function() { - return ($x = t6._init = t6.asm.init).apply(null, arguments); - }, De = t6._init_with_threads_count = function() { - return (De = t6._init_with_threads_count = t6.asm.init_with_threads_count).apply(null, arguments); - }, Pc = t6._get_threads_count = function() { - return (Pc = t6._get_threads_count = t6.asm.get_threads_count).apply(null, arguments); - }, Ax = t6._register_tensor = function() { - return (Ax = t6._register_tensor = t6.asm.register_tensor).apply(null, arguments); - }, NI = t6._dispose_data = function() { - return (NI = t6._dispose_data = t6.asm.dispose_data).apply(null, arguments); - }, TI = t6._dispose = function() { - return (TI = t6._dispose = t6.asm.dispose).apply(null, arguments); - }, Rx = t6._Abs = function() { - return (Rx = t6._Abs = t6.asm.Abs).apply(null, arguments); - }, Jl = t6._Add = function() { - return (Jl = t6._Add = t6.asm.Add).apply(null, arguments); - }, Yu = t6._AddN = function() { - return (Yu = t6._AddN = t6.asm.AddN).apply(null, arguments); - }, Fx = t6._All = function() { - return (Fx = t6._All = t6.asm.All).apply(null, arguments); - }, Dx = t6._Any = function() { - return (Dx = t6._Any = t6.asm.Any).apply(null, arguments); - }, _I = t6._ArgMax = function() { - return (_I = t6._ArgMax = t6.asm.ArgMax).apply(null, arguments); - }, Ox = t6._AvgPool = function() { - return (Ox = t6._AvgPool = t6.asm.AvgPool).apply(null, arguments); - }, Px = t6._BatchMatMul = function() { - return (Px = t6._BatchMatMul = t6.asm.BatchMatMul).apply(null, arguments); - }, Mx = t6._Ceil = function() { - return (Mx = t6._Ceil = t6.asm.Ceil).apply(null, arguments); - }, Lx = t6._ClipByValue = function() { - return (Lx = t6._ClipByValue = t6.asm.ClipByValue).apply(null, arguments); - }, em = t6._Conv2D = function() { - return (em = t6._Conv2D = t6.asm.Conv2D).apply(null, arguments); - }, tm = t6._Conv2DBackpropInput = function() { - return (tm = t6._Conv2DBackpropInput = t6.asm.Conv2DBackpropInput).apply(null, arguments); - }, Bx = t6._Cos = function() { - return (Bx = t6._Cos = t6.asm.Cos).apply(null, arguments); - }, Vx = t6._Cosh = function() { - return (Vx = t6._Cosh = t6.asm.Cosh).apply(null, arguments); - }, zx = t6._CropAndResize = function() { - return (zx = t6._CropAndResize = t6.asm.CropAndResize).apply(null, arguments); - }, Mc = t6._Cumprod = function() { - return (Mc = t6._Cumprod = t6.asm.Cumprod).apply(null, arguments); - }, Wx = t6._Cumsum = function() { - return (Wx = t6._Cumsum = t6.asm.Cumsum).apply(null, arguments); - }, Ux = t6._DepthToSpace = function() { - return (Ux = t6._DepthToSpace = t6.asm.DepthToSpace).apply(null, arguments); - }, Gx = t6._DepthwiseConv2dNative = function() { - return (Gx = t6._DepthwiseConv2dNative = t6.asm.DepthwiseConv2dNative).apply(null, arguments); - }, qi = t6._Elu = function() { - return (qi = t6._Elu = t6.asm.Elu).apply(null, arguments); - }, Hx = t6._Equal = function() { - return (Hx = t6._Equal = t6.asm.Equal).apply(null, arguments); - }, qx = t6._Exp = function() { - return (qx = t6._Exp = t6.asm.Exp).apply(null, arguments); - }, rm = t6._FlipLeftRight = function() { - return (rm = t6._FlipLeftRight = t6.asm.FlipLeftRight).apply(null, arguments); - }, Kx = t6._Floor = function() { - return (Kx = t6._Floor = t6.asm.Floor).apply(null, arguments); - }, Qu = t6._FloorDiv = function() { - return (Qu = t6._FloorDiv = t6.asm.FloorDiv).apply(null, arguments); - }, jx = t6._FusedBatchNorm = function() { - return (jx = t6._FusedBatchNorm = t6.asm.FusedBatchNorm).apply(null, arguments); - }, Xx = t6._FusedConv2D = function() { - return (Xx = t6._FusedConv2D = t6.asm.FusedConv2D).apply(null, arguments); - }, Xa = t6._FusedDepthwiseConv2D = function() { - return (Xa = t6._FusedDepthwiseConv2D = t6.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Lc = t6._Gather = function() { - return (Lc = t6._Gather = t6.asm.Gather).apply(null, arguments); - }, Yx = t6._GatherNd = function() { - return (Yx = t6._GatherNd = t6.asm.GatherNd).apply(null, arguments); - }, Qx = t6._Greater = function() { - return (Qx = t6._Greater = t6.asm.Greater).apply(null, arguments); - }, Zx = t6._GreaterEqual = function() { - return (Zx = t6._GreaterEqual = t6.asm.GreaterEqual).apply(null, arguments); - }, Jx = t6._IsNan = function() { - return (Jx = t6._IsNan = t6.asm.IsNan).apply(null, arguments); - }, om = t6._LeakyRelu = function() { - return (om = t6._LeakyRelu = t6.asm.LeakyRelu).apply(null, arguments); - }, nm = t6._Less = function() { - return (nm = t6._Less = t6.asm.Less).apply(null, arguments); - }, ey = t6._LessEqual = function() { - return (ey = t6._LessEqual = t6.asm.LessEqual).apply(null, arguments); - }, ty = t6._Log = function() { - return (ty = t6._Log = t6.asm.Log).apply(null, arguments); - }, sm = t6._LogicalAnd = function() { - return (sm = t6._LogicalAnd = t6.asm.LogicalAnd).apply(null, arguments); - }, am = t6._LogicalNot = function() { - return (am = t6._LogicalNot = t6.asm.LogicalNot).apply(null, arguments); - }, ry = t6._LogicalOr = function() { - return (ry = t6._LogicalOr = t6.asm.LogicalOr).apply(null, arguments); - }, oy = t6._LogicalXor = function() { - return (oy = t6._LogicalXor = t6.asm.LogicalXor).apply(null, arguments); - }, ny = t6._Max = function() { - return (ny = t6._Max = t6.asm.Max).apply(null, arguments); - }, im = t6._MaxPool = function() { - return (im = t6._MaxPool = t6.asm.MaxPool).apply(null, arguments); - }, EI = t6._Maximum = function() { - return (EI = t6._Maximum = t6.asm.Maximum).apply(null, arguments); - }, sy = t6._Mean = function() { - return (sy = t6._Mean = t6.asm.Mean).apply(null, arguments); - }, ay = t6._Min = function() { - return (ay = t6._Min = t6.asm.Min).apply(null, arguments); - }, iy = t6._Minimum = function() { - return (iy = t6._Minimum = t6.asm.Minimum).apply(null, arguments); - }, uy = t6._MirrorPad = function() { - return (uy = t6._MirrorPad = t6.asm.MirrorPad).apply(null, arguments); - }, py = t6._Multiply = function() { - return (py = t6._Multiply = t6.asm.Multiply).apply(null, arguments); - }, cy = t6._Neg = function() { - return (cy = t6._Neg = t6.asm.Neg).apply(null, arguments); - }, ly = t6._NonMaxSuppressionV3 = function() { - return (ly = t6._NonMaxSuppressionV3 = t6.asm.NonMaxSuppressionV3).apply(null, arguments); - }, my = t6._NonMaxSuppressionV4 = function() { - return (my = t6._NonMaxSuppressionV4 = t6.asm.NonMaxSuppressionV4).apply(null, arguments); - }, dy = t6._NonMaxSuppressionV5 = function() { - return (dy = t6._NonMaxSuppressionV5 = t6.asm.NonMaxSuppressionV5).apply(null, arguments); - }, fy = t6._NotEqual = function() { - return (fy = t6._NotEqual = t6.asm.NotEqual).apply(null, arguments); - }, hy = t6._OneHot = function() { - return (hy = t6._OneHot = t6.asm.OneHot).apply(null, arguments); - }, gy = t6._PadV2 = function() { - return (gy = t6._PadV2 = t6.asm.PadV2).apply(null, arguments); - }, xy = t6._Pow = function() { - return (xy = t6._Pow = t6.asm.Pow).apply(null, arguments); - }, yy = t6._Prelu = function() { - return (yy = t6._Prelu = t6.asm.Prelu).apply(null, arguments); - }, by = t6._Prod = function() { - return (by = t6._Prod = t6.asm.Prod).apply(null, arguments); - }, Cy = t6._RealDiv = function() { - return (Cy = t6._RealDiv = t6.asm.RealDiv).apply(null, arguments); - }, Sy = t6._Reciprocal = function() { - return (Sy = t6._Reciprocal = t6.asm.Reciprocal).apply(null, arguments); - }, wy = t6._Relu = function() { - return (wy = t6._Relu = t6.asm.Relu).apply(null, arguments); - }, Iy = t6._Relu6 = function() { - return (Iy = t6._Relu6 = t6.asm.Relu6).apply(null, arguments); - }, vy = t6._ResizeBilinear = function() { - return (vy = t6._ResizeBilinear = t6.asm.ResizeBilinear).apply(null, arguments); - }, ky = t6._ResizeNearestNeighbor = function() { - return (ky = t6._ResizeNearestNeighbor = t6.asm.ResizeNearestNeighbor).apply(null, arguments); - }, Ny = t6._Reverse = function() { - return (Ny = t6._Reverse = t6.asm.Reverse).apply(null, arguments); - }, Ty = t6._RotateWithOffset = function() { - return (Ty = t6._RotateWithOffset = t6.asm.RotateWithOffset).apply(null, arguments); - }, _y = t6._Round = function() { - return (_y = t6._Round = t6.asm.Round).apply(null, arguments); - }, Ey = t6._Rsqrt = function() { - return (Ey = t6._Rsqrt = t6.asm.Rsqrt).apply(null, arguments); - }, $y = t6._ScatterNd = function() { - return ($y = t6._ScatterNd = t6.asm.ScatterNd).apply(null, arguments); - }, Ay = t6._SelectV2 = function() { - return (Ay = t6._SelectV2 = t6.asm.SelectV2).apply(null, arguments); - }, Ry = t6._Sigmoid = function() { - return (Ry = t6._Sigmoid = t6.asm.Sigmoid).apply(null, arguments); - }, Fy = t6._Sin = function() { - return (Fy = t6._Sin = t6.asm.Sin).apply(null, arguments); - }, Dy = t6._Softmax = function() { - return (Dy = t6._Softmax = t6.asm.Softmax).apply(null, arguments); - }, Oy = t6._SparseFillEmptyRows = function() { - return (Oy = t6._SparseFillEmptyRows = t6.asm.SparseFillEmptyRows).apply(null, arguments); - }, Py = t6._SparseReshape = function() { - return (Py = t6._SparseReshape = t6.asm.SparseReshape).apply(null, arguments); - }, My = t6._SparseSegmentReduction = function() { - return (My = t6._SparseSegmentReduction = t6.asm.SparseSegmentReduction).apply(null, arguments); - }, Ly = t6._Sqrt = function() { - return (Ly = t6._Sqrt = t6.asm.Sqrt).apply(null, arguments); - }, By = t6._Square = function() { - return (By = t6._Square = t6.asm.Square).apply(null, arguments); - }, Vy = t6._SquaredDifference = function() { - return (Vy = t6._SquaredDifference = t6.asm.SquaredDifference).apply(null, arguments); - }, zy = t6._Step = function() { - return (zy = t6._Step = t6.asm.Step).apply(null, arguments); - }, Wy = t6._StridedSlice = function() { - return (Wy = t6._StridedSlice = t6.asm.StridedSlice).apply(null, arguments); - }, Uy = t6._Sub = function() { - return (Uy = t6._Sub = t6.asm.Sub).apply(null, arguments); - }, Gy = t6._Sum = function() { - return (Gy = t6._Sum = t6.asm.Sum).apply(null, arguments); - }, Hy = t6._Tan = function() { - return (Hy = t6._Tan = t6.asm.Tan).apply(null, arguments); - }, qy = t6._Tanh = function() { - return (qy = t6._Tanh = t6.asm.Tanh).apply(null, arguments); - }, Ky = t6._Tile = function() { - return (Ky = t6._Tile = t6.asm.Tile).apply(null, arguments); - }, jy = t6._TopK = function() { - return (jy = t6._TopK = t6.asm.TopK).apply(null, arguments); - }, Xy = t6._Transform = function() { - return (Xy = t6._Transform = t6.asm.Transform).apply(null, arguments); - }, Yy = t6._Transpose = function() { - return (Yy = t6._Transpose = t6.asm.Transpose).apply(null, arguments); - }, Qy = t6.__FusedMatMul = function() { - return (Qy = t6.__FusedMatMul = t6.asm._FusedMatMul).apply(null, arguments); - }, Zy = t6._malloc = function() { - return (Zy = t6._malloc = t6.asm.malloc).apply(null, arguments); - }, Jy = t6._free = function() { - return (Jy = t6._free = t6.asm.free).apply(null, arguments); - }, eb = t6.___errno_location = function() { - return (eb = t6.___errno_location = t6.asm.__errno_location).apply(null, arguments); - }, um = t6.stackSave = function() { - return (um = t6.stackSave = t6.asm.stackSave).apply(null, arguments); - }, pm = t6.stackRestore = function() { - return (pm = t6.stackRestore = t6.asm.stackRestore).apply(null, arguments); - }, Bc = t6.stackAlloc = function() { - return (Bc = t6.stackAlloc = t6.asm.stackAlloc).apply(null, arguments); - }, tb = t6.dynCall_iijjiiii = function() { - return (tb = t6.dynCall_iijjiiii = t6.asm.dynCall_iijjiiii).apply(null, arguments); - }, rb = t6.dynCall_jiji = function() { - return (rb = t6.dynCall_jiji = t6.asm.dynCall_jiji).apply(null, arguments); + var Oc = { abort: Rx, emscripten_get_heap_max: Ki, emscripten_memcpy_big: Dx, emscripten_resize_heap: ep, fd_close: Zl, fd_seek: Fx, fd_write: DI }, Lx = Fc(), em = t10.___wasm_call_ctors = function() { + return (em = t10.___wasm_call_ctors = t10.asm.__wasm_call_ctors).apply(null, arguments); + }, tm = t10._init = function() { + return (tm = t10._init = t10.asm.init).apply(null, arguments); + }, Bx = t10._init_with_threads_count = function() { + return (Bx = t10._init_with_threads_count = t10.asm.init_with_threads_count).apply(null, arguments); + }, rm = t10._get_threads_count = function() { + return (rm = t10._get_threads_count = t10.asm.get_threads_count).apply(null, arguments); + }, Vx = t10._register_tensor = function() { + return (Vx = t10._register_tensor = t10.asm.register_tensor).apply(null, arguments); + }, Oe = t10._dispose_data = function() { + return (Oe = t10._dispose_data = t10.asm.dispose_data).apply(null, arguments); + }, Pc = t10._dispose = function() { + return (Pc = t10._dispose = t10.asm.dispose).apply(null, arguments); + }, zx = t10._Abs = function() { + return (zx = t10._Abs = t10.asm.Abs).apply(null, arguments); + }, om = t10._Add = function() { + return (om = t10._Add = t10.asm.Add).apply(null, arguments); + }, tp = t10._AddN = function() { + return (tp = t10._AddN = t10.asm.AddN).apply(null, arguments); + }, Wx = t10._All = function() { + return (Wx = t10._All = t10.asm.All).apply(null, arguments); + }, Ux = t10._Any = function() { + return (Ux = t10._Any = t10.asm.Any).apply(null, arguments); + }, Gx = t10._ArgMax = function() { + return (Gx = t10._ArgMax = t10.asm.ArgMax).apply(null, arguments); + }, Hx = t10._AvgPool = function() { + return (Hx = t10._AvgPool = t10.asm.AvgPool).apply(null, arguments); + }, Kx = t10._BatchMatMul = function() { + return (Kx = t10._BatchMatMul = t10.asm.BatchMatMul).apply(null, arguments); + }, nm = t10._Ceil = function() { + return (nm = t10._Ceil = t10.asm.Ceil).apply(null, arguments); + }, sm = t10._ClipByValue = function() { + return (sm = t10._ClipByValue = t10.asm.ClipByValue).apply(null, arguments); + }, qx = t10._Conv2D = function() { + return (qx = t10._Conv2D = t10.asm.Conv2D).apply(null, arguments); + }, jx = t10._Conv2DBackpropInput = function() { + return (jx = t10._Conv2DBackpropInput = t10.asm.Conv2DBackpropInput).apply(null, arguments); + }, Xx = t10._Cos = function() { + return (Xx = t10._Cos = t10.asm.Cos).apply(null, arguments); + }, Mc = t10._Cosh = function() { + return (Mc = t10._Cosh = t10.asm.Cosh).apply(null, arguments); + }, Yx = t10._CropAndResize = function() { + return (Yx = t10._CropAndResize = t10.asm.CropAndResize).apply(null, arguments); + }, Qx = t10._Cumprod = function() { + return (Qx = t10._Cumprod = t10.asm.Cumprod).apply(null, arguments); + }, Zx = t10._Cumsum = function() { + return (Zx = t10._Cumsum = t10.asm.Cumsum).apply(null, arguments); + }, ji = t10._DepthToSpace = function() { + return (ji = t10._DepthToSpace = t10.asm.DepthToSpace).apply(null, arguments); + }, Jx = t10._DepthwiseConv2dNative = function() { + return (Jx = t10._DepthwiseConv2dNative = t10.asm.DepthwiseConv2dNative).apply(null, arguments); + }, ey = t10._Elu = function() { + return (ey = t10._Elu = t10.asm.Elu).apply(null, arguments); + }, am = t10._Equal = function() { + return (am = t10._Equal = t10.asm.Equal).apply(null, arguments); + }, ty = t10._Exp = function() { + return (ty = t10._Exp = t10.asm.Exp).apply(null, arguments); + }, Lc = t10._FlipLeftRight = function() { + return (Lc = t10._FlipLeftRight = t10.asm.FlipLeftRight).apply(null, arguments); + }, ry = t10._Floor = function() { + return (ry = t10._Floor = t10.asm.Floor).apply(null, arguments); + }, oy = t10._FloorDiv = function() { + return (oy = t10._FloorDiv = t10.asm.FloorDiv).apply(null, arguments); + }, ny = t10._FusedBatchNorm = function() { + return (ny = t10._FusedBatchNorm = t10.asm.FusedBatchNorm).apply(null, arguments); + }, Qa = t10._FusedConv2D = function() { + return (Qa = t10._FusedConv2D = t10.asm.FusedConv2D).apply(null, arguments); + }, Bc = t10._FusedDepthwiseConv2D = function() { + return (Bc = t10._FusedDepthwiseConv2D = t10.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, sy = t10._Gather = function() { + return (sy = t10._Gather = t10.asm.Gather).apply(null, arguments); + }, ay = t10._GatherNd = function() { + return (ay = t10._GatherNd = t10.asm.GatherNd).apply(null, arguments); + }, iy = t10._Greater = function() { + return (iy = t10._Greater = t10.asm.Greater).apply(null, arguments); + }, uy = t10._GreaterEqual = function() { + return (uy = t10._GreaterEqual = t10.asm.GreaterEqual).apply(null, arguments); + }, im = t10._IsNan = function() { + return (im = t10._IsNan = t10.asm.IsNan).apply(null, arguments); + }, um = t10._LeakyRelu = function() { + return (um = t10._LeakyRelu = t10.asm.LeakyRelu).apply(null, arguments); + }, py = t10._Less = function() { + return (py = t10._Less = t10.asm.Less).apply(null, arguments); + }, cy = t10._LessEqual = function() { + return (cy = t10._LessEqual = t10.asm.LessEqual).apply(null, arguments); + }, pm = t10._Log = function() { + return (pm = t10._Log = t10.asm.Log).apply(null, arguments); + }, cm = t10._LogicalAnd = function() { + return (cm = t10._LogicalAnd = t10.asm.LogicalAnd).apply(null, arguments); + }, ly = t10._LogicalNot = function() { + return (ly = t10._LogicalNot = t10.asm.LogicalNot).apply(null, arguments); + }, my = t10._LogicalOr = function() { + return (my = t10._LogicalOr = t10.asm.LogicalOr).apply(null, arguments); + }, dy = t10._LogicalXor = function() { + return (dy = t10._LogicalXor = t10.asm.LogicalXor).apply(null, arguments); + }, fy = t10._Max = function() { + return (fy = t10._Max = t10.asm.Max).apply(null, arguments); + }, lm = t10._MaxPool = function() { + return (lm = t10._MaxPool = t10.asm.MaxPool).apply(null, arguments); + }, FI = t10._Maximum = function() { + return (FI = t10._Maximum = t10.asm.Maximum).apply(null, arguments); + }, hy = t10._Mean = function() { + return (hy = t10._Mean = t10.asm.Mean).apply(null, arguments); + }, gy = t10._Min = function() { + return (gy = t10._Min = t10.asm.Min).apply(null, arguments); + }, xy = t10._Minimum = function() { + return (xy = t10._Minimum = t10.asm.Minimum).apply(null, arguments); + }, yy = t10._MirrorPad = function() { + return (yy = t10._MirrorPad = t10.asm.MirrorPad).apply(null, arguments); + }, by = t10._Multiply = function() { + return (by = t10._Multiply = t10.asm.Multiply).apply(null, arguments); + }, Cy = t10._Neg = function() { + return (Cy = t10._Neg = t10.asm.Neg).apply(null, arguments); + }, Sy = t10._NonMaxSuppressionV3 = function() { + return (Sy = t10._NonMaxSuppressionV3 = t10.asm.NonMaxSuppressionV3).apply(null, arguments); + }, wy = t10._NonMaxSuppressionV4 = function() { + return (wy = t10._NonMaxSuppressionV4 = t10.asm.NonMaxSuppressionV4).apply(null, arguments); + }, Iy = t10._NonMaxSuppressionV5 = function() { + return (Iy = t10._NonMaxSuppressionV5 = t10.asm.NonMaxSuppressionV5).apply(null, arguments); + }, vy = t10._NotEqual = function() { + return (vy = t10._NotEqual = t10.asm.NotEqual).apply(null, arguments); + }, ky = t10._OneHot = function() { + return (ky = t10._OneHot = t10.asm.OneHot).apply(null, arguments); + }, Ny = t10._PadV2 = function() { + return (Ny = t10._PadV2 = t10.asm.PadV2).apply(null, arguments); + }, Ty = t10._Pow = function() { + return (Ty = t10._Pow = t10.asm.Pow).apply(null, arguments); + }, _y = t10._Prelu = function() { + return (_y = t10._Prelu = t10.asm.Prelu).apply(null, arguments); + }, $y = t10._Prod = function() { + return ($y = t10._Prod = t10.asm.Prod).apply(null, arguments); + }, Ey = t10._RealDiv = function() { + return (Ey = t10._RealDiv = t10.asm.RealDiv).apply(null, arguments); + }, Ay = t10._Reciprocal = function() { + return (Ay = t10._Reciprocal = t10.asm.Reciprocal).apply(null, arguments); + }, Ry = t10._Relu = function() { + return (Ry = t10._Relu = t10.asm.Relu).apply(null, arguments); + }, Dy = t10._Relu6 = function() { + return (Dy = t10._Relu6 = t10.asm.Relu6).apply(null, arguments); + }, Fy = t10._ResizeBilinear = function() { + return (Fy = t10._ResizeBilinear = t10.asm.ResizeBilinear).apply(null, arguments); + }, Oy = t10._ResizeNearestNeighbor = function() { + return (Oy = t10._ResizeNearestNeighbor = t10.asm.ResizeNearestNeighbor).apply(null, arguments); + }, Py = t10._Reverse = function() { + return (Py = t10._Reverse = t10.asm.Reverse).apply(null, arguments); + }, My = t10._RotateWithOffset = function() { + return (My = t10._RotateWithOffset = t10.asm.RotateWithOffset).apply(null, arguments); + }, Ly = t10._Round = function() { + return (Ly = t10._Round = t10.asm.Round).apply(null, arguments); + }, By = t10._Rsqrt = function() { + return (By = t10._Rsqrt = t10.asm.Rsqrt).apply(null, arguments); + }, Vy = t10._ScatterNd = function() { + return (Vy = t10._ScatterNd = t10.asm.ScatterNd).apply(null, arguments); + }, zy = t10._SelectV2 = function() { + return (zy = t10._SelectV2 = t10.asm.SelectV2).apply(null, arguments); + }, Wy = t10._Sigmoid = function() { + return (Wy = t10._Sigmoid = t10.asm.Sigmoid).apply(null, arguments); + }, Uy = t10._Sin = function() { + return (Uy = t10._Sin = t10.asm.Sin).apply(null, arguments); + }, Gy = t10._Softmax = function() { + return (Gy = t10._Softmax = t10.asm.Softmax).apply(null, arguments); + }, Hy = t10._SparseFillEmptyRows = function() { + return (Hy = t10._SparseFillEmptyRows = t10.asm.SparseFillEmptyRows).apply(null, arguments); + }, Ky = t10._SparseReshape = function() { + return (Ky = t10._SparseReshape = t10.asm.SparseReshape).apply(null, arguments); + }, qy = t10._SparseSegmentReduction = function() { + return (qy = t10._SparseSegmentReduction = t10.asm.SparseSegmentReduction).apply(null, arguments); + }, jy = t10._Sqrt = function() { + return (jy = t10._Sqrt = t10.asm.Sqrt).apply(null, arguments); + }, Xy = t10._Square = function() { + return (Xy = t10._Square = t10.asm.Square).apply(null, arguments); + }, Yy = t10._SquaredDifference = function() { + return (Yy = t10._SquaredDifference = t10.asm.SquaredDifference).apply(null, arguments); + }, Qy = t10._Step = function() { + return (Qy = t10._Step = t10.asm.Step).apply(null, arguments); + }, Zy = t10._StridedSlice = function() { + return (Zy = t10._StridedSlice = t10.asm.StridedSlice).apply(null, arguments); + }, Jy = t10._Sub = function() { + return (Jy = t10._Sub = t10.asm.Sub).apply(null, arguments); + }, eb = t10._Sum = function() { + return (eb = t10._Sum = t10.asm.Sum).apply(null, arguments); + }, tb = t10._Tan = function() { + return (tb = t10._Tan = t10.asm.Tan).apply(null, arguments); + }, rb = t10._Tanh = function() { + return (rb = t10._Tanh = t10.asm.Tanh).apply(null, arguments); + }, ob = t10._Tile = function() { + return (ob = t10._Tile = t10.asm.Tile).apply(null, arguments); + }, nb = t10._TopK = function() { + return (nb = t10._TopK = t10.asm.TopK).apply(null, arguments); + }, sb = t10._Transform = function() { + return (sb = t10._Transform = t10.asm.Transform).apply(null, arguments); + }, ab = t10._Transpose = function() { + return (ab = t10._Transpose = t10.asm.Transpose).apply(null, arguments); + }, ib = t10.__FusedMatMul = function() { + return (ib = t10.__FusedMatMul = t10.asm._FusedMatMul).apply(null, arguments); + }, ub = t10._malloc = function() { + return (ub = t10._malloc = t10.asm.malloc).apply(null, arguments); + }, pb = t10._free = function() { + return (pb = t10._free = t10.asm.free).apply(null, arguments); + }, cb = t10.___errno_location = function() { + return (cb = t10.___errno_location = t10.asm.__errno_location).apply(null, arguments); + }, mm = t10.stackSave = function() { + return (mm = t10.stackSave = t10.asm.stackSave).apply(null, arguments); + }, dm = t10.stackRestore = function() { + return (dm = t10.stackRestore = t10.asm.stackRestore).apply(null, arguments); + }, Vc = t10.stackAlloc = function() { + return (Vc = t10.stackAlloc = t10.asm.stackAlloc).apply(null, arguments); + }, lb = t10.dynCall_iijjiiii = function() { + return (lb = t10.dynCall_iijjiiii = t10.asm.dynCall_iijjiiii).apply(null, arguments); + }, mb = t10.dynCall_jiji = function() { + return (mb = t10.dynCall_jiji = t10.asm.dynCall_jiji).apply(null, arguments); }; - t6.cwrap = Ql; - var Zu; + t10.cwrap = Mx; + var rp; rr = function G() { - Zu || cm(), Zu || (rr = G); + rp || fm(), rp || (rr = G); }; - function cm(G) { + function fm(G) { if (G = G || i, Nt > 0 || (ft(), Nt > 0)) return; - function oe() { - Zu || (Zu = true, t6.calledRun = true, !M && (wt(), o(t6), t6.onRuntimeInitialized && t6.onRuntimeInitialized(), Fr())); + function se() { + rp || (rp = true, t10.calledRun = true, !M && (ht(), o(t10), t10.onRuntimeInitialized && t10.onRuntimeInitialized(), Rr())); } - t6.setStatus ? (t6.setStatus("Running..."), setTimeout(function() { + t10.setStatus ? (t10.setStatus("Running..."), setTimeout(function() { setTimeout(function() { - t6.setStatus(""); - }, 1), oe(); - }, 1)) : oe(); + t10.setStatus(""); + }, 1), se(); + }, 1)) : se(); } - if (t6.preInit) - for (typeof t6.preInit == "function" && (t6.preInit = [t6.preInit]); t6.preInit.length > 0; ) - t6.preInit.pop()(); - cm(); - var Ju; - s && (Ju = { uncaughtException: process.listeners("uncaughtException").filter(function(G) { + if (t10.preInit) + for (typeof t10.preInit == "function" && (t10.preInit = [t10.preInit]); t10.preInit.length > 0; ) + t10.preInit.pop()(); + fm(); + var op; + s && (op = { uncaughtException: process.listeners("uncaughtException").filter(function(G) { return !s.uncaughtException.indexOf(G) > -1; }), unhandledRejection: process.listeners("unhandledRejection").filter(function(G) { return !s.unhandledRejection.indexOf(G) > -1; }) }); - var ep; + var np; if (typeof e != "undefined") - ep = e; + np = e; else if (typeof WasmBackendModuleThreadedSimd != "undefined") - ep = WasmBackendModuleThreadedSimd; + np = WasmBackendModuleThreadedSimd; else throw new Error("Could not find wasm module in post.js"); - if (Ju) { - var ob = ep._dispose; - ep._dispose = function() { - ob(), Ju.uncaughtException.forEach(function(G) { + if (op) { + var db = np._dispose; + np._dispose = function() { + db(), op.uncaughtException.forEach(function(G) { process.removeListener("uncaughtException", G); - }), Ju.unhandledRejection.forEach(function(G) { + }), op.unhandledRejection.forEach(function(G) { process.removeListener("unhandledRejection", G); }); }; @@ -2807,19 +2751,19 @@ var x3 = qt((Ig, Yw) => { return e.ready; }; })(); - typeof Ig == "object" && typeof Yw == "object" ? Yw.exports = Xw : typeof define == "function" && define.amd ? define([], function() { - return Xw; - }) : typeof Ig == "object" && (Ig.WasmBackendModule = Xw); + typeof Ig == "object" && typeof uI == "object" ? uI.exports = iI : typeof define == "function" && define.amd ? define([], function() { + return iI; + }) : typeof Ig == "object" && (Ig.WasmBackendModule = iI); }); -var Do = class { - constructor(e, t6) { - this.backend = e, this.dataMover = t6, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; +var Fo = class { + constructor(e, t10) { + this.backend = e, this.dataMover = t10, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; } get(e) { return this.data.has(e) || this.dataMover.moveData(this.backend, e), this.data.get(e); } - set(e, t6) { - this.dataIdsCount++, this.data.set(e, t6); + set(e, t10) { + this.dataIdsCount++, this.data.set(e, t10); } has(e) { return this.data.has(e); @@ -2833,140 +2777,132 @@ var Do = class { }; var Zr = class { refCount(e) { - return Or("refCount"); + return Fr("refCount"); } incRef(e) { - return Or("incRef"); + return Fr("incRef"); } timerAvailable() { return true; } time(e) { - return Or("time"); + return Fr("time"); } read(e) { - return Or("read"); + return Fr("read"); } readSync(e) { - return Or("readSync"); + return Fr("readSync"); } - readToGPU(e, t6) { - return Or("readToGPU"); + readToGPU(e, t10) { + return Fr("readToGPU"); } numDataIds() { - return Or("numDataIds"); + return Fr("numDataIds"); } - disposeData(e, t6) { - return Or("disposeData"); + disposeData(e, t10) { + return Fr("disposeData"); } - write(e, t6, o) { - return Or("write"); + write(e, t10, o) { + return Fr("write"); } - move(e, t6, o, n, s) { - return Or("move"); + move(e, t10, o, n, s) { + return Fr("move"); } - createTensorFromTexture(e, t6, o) { - return Or("createTensorFromTexture"); + createTensorFromGPUData(e, t10, o) { + return Fr("createTensorFromGPUData"); } memory() { - return Or("memory"); + return Fr("memory"); } floatPrecision() { - return Or("floatPrecision"); + return Fr("floatPrecision"); } epsilon() { return this.floatPrecision() === 32 ? 1e-7 : 1e-4; } dispose() { - return Or("dispose"); + return Fr("dispose"); } }; -function Or(r) { +function Fr(r) { throw new Error(`'${r}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); } -function LI(r) { - let e = r.length, t6 = 0; +function UI(r) { + let e = r.length, t10 = 0; for (; e > 0; ) - t6 = Math.random() * e | 0, e--, Cm(r, e, t6); + t10 = Math.random() * e | 0, e--, Im(r, e, t10); } -function vV(r, e) { +function WV(r, e) { if (r.length !== e.length) throw new Error(`Array sizes must match to be shuffled together First array length was ${r.length}Second array length was ${e.length}`); - let t6 = r.length, o = 0; - for (; t6 > 0; ) - o = Math.random() * t6 | 0, t6--, Cm(r, t6, o), Cm(e, t6, o); + let t10 = r.length, o = 0; + for (; t10 > 0; ) + o = Math.random() * t10 | 0, t10--, Im(r, t10, o), Im(e, t10, o); } -function op(r, e, t6) { - return Math.max(r, Math.min(e, t6)); +function ip(r, e, t10) { + return Math.max(r, Math.min(e, t10)); } -function kV(r) { +function UV(r) { return r % 2 === 0 ? r : r + 1; } -function Cm(r, e, t6) { +function Im(r, e, t10) { let o = r[e]; - r[e] = r[t6], r[t6] = o; + r[e] = r[t10], r[t10] = o; } -function NV(r) { +function GV(r) { let e = 0; - for (let t6 = 0; t6 < r.length; t6++) - e += r[t6]; + for (let t10 = 0; t10 < r.length; t10++) + e += r[t10]; return e; } -function TV(r, e) { - let t6 = Math.random(); - return e * t6 + (1 - t6) * r; +function HV(r, e) { + let t10 = Math.random(); + return e * t10 + (1 - t10) * r; } -function _V(r, e) { - let t6 = 0; +function KV(r, e) { + let t10 = 0; for (let o = 0; o < r.length; o++) { let n = Number(r[o]) - Number(e[o]); - t6 += n * n; + t10 += n * n; } - return t6; + return t10; } -function E(r, e) { +function $(r, e) { if (!r) throw new Error(typeof e == "string" ? e : e()); } -function ht(r, e, t6 = "") { - E(Pr(r, e), () => t6 + ` Shapes ${r} and ${e} must match`); +function gt(r, e, t10 = "") { + $(Or(r, e), () => t10 + ` Shapes ${r} and ${e} must match`); } function Jr(r) { - E(r != null, () => "The input to the tensor constructor must be a non-null value."); + $(r != null, () => "The input to the tensor constructor must be a non-null value."); } -function Oo(r, e = [], t6 = false) { - if (e == null && (e = []), Array.isArray(r) || Wt(r) && !t6) - for (let o = 0; o < r.length; ++o) - Oo(r[o], e, t6); - else - e.push(r); - return e; -} -function ze(r) { +function We(r) { if (r.length === 0) return 1; let e = r[0]; - for (let t6 = 1; t6 < r.length; t6++) - e *= r[t6]; + for (let t10 = 1; t10 < r.length; t10++) + e *= r[t10]; return e; } -function EV(r) { +function qV(r) { return r.length === 0; } -function Pr(r, e) { +function Or(r, e) { if (r === e) return true; if (r == null || e == null || r.length !== e.length) return false; - for (let t6 = 0; t6 < r.length; t6++) - if (r[t6] !== e[t6]) + for (let t10 = 0; t10 < r.length; t10++) + if (r[t10] !== e[t10]) return false; return true; } -function na(r) { +function sa(r) { return r % 1 === 0; } -function $V(r) { +function jV(r) { if (Math.tanh != null) return Math.tanh(r); if (r === 1 / 0) @@ -2978,20 +2914,20 @@ function $V(r) { return (e - 1) / (e + 1); } } -function AV(r) { +function XV(r) { let e = Math.ceil(Math.sqrt(r)); return [e, Math.ceil(r / e)]; } -function RV(r) { +function YV(r) { let e = new Uint32Array(r); - for (let t6 = 0; t6 < r; ++t6) - e[t6] = t6; - return LI(e), e; + for (let t10 = 0; t10 < r; ++t10) + e[t10] = t10; + return UI(e), e; } -function ji(r, e) { +function Yi(r, e) { return e <= r.length ? r : r + " ".repeat(e - r.length); } -function FV(r, e = (n) => 0, t6, o) { +function QV(r, e = (n) => 0, t10, o) { return new Promise((n, s) => { let a = 0, i = () => { if (r()) { @@ -3000,7 +2936,7 @@ function FV(r, e = (n) => 0, t6, o) { } a++; let p = e(a); - if (t6 != null && a >= t6) { + if (t10 != null && a >= t10) { s(); return; } @@ -3009,11 +2945,11 @@ function FV(r, e = (n) => 0, t6, o) { i(); }); } -function DV(r, e) { - let t6 = 1, o = -1; +function ZV(r, e) { + let t10 = 1, o = -1; for (let s = 0; s < r.length; ++s) if (r[s] >= 0) - t6 *= r[s]; + t10 *= r[s]; else if (r[s] === -1) { if (o !== -1) throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${o} and dim ${s}`); @@ -3021,76 +2957,73 @@ function DV(r, e) { } else if (r[s] < 0) throw Error(`Shapes can not be < 0. Found ${r[s]} at dim ${s}`); if (o === -1) { - if (e > 0 && e !== t6) + if (e > 0 && e !== t10) throw Error(`Size(${e}) must match the product of shape ${r}`); return r; } - if (t6 === 0) + if (t10 === 0) throw Error(`Cannot infer the missing size in [${r}] when there are 0 elements`); - if (e % t6 !== 0) - throw Error(`The implicit shape can't be a fractional number. Got ${e} / ${t6}`); + if (e % t10 !== 0) + throw Error(`The implicit shape can't be a fractional number. Got ${e} / ${t10}`); let n = r.slice(); - return n[o] = e / t6, n; + return n[o] = e / t10, n; } -function Qa(r, e) { - let t6 = e.length; - return r = r == null ? e.map((o, n) => n) : [].concat(r), E(r.every((o) => o >= -t6 && o < t6), () => `All values in axis param must be in range [-${t6}, ${t6}) but got axis ${r}`), E(r.every((o) => na(o)), () => `All values in axis param must be integers but got axis ${r}`), r.map((o) => o < 0 ? t6 + o : o); +function Ja(r, e) { + let t10 = e.length; + return r = r == null ? e.map((o, n) => n) : [].concat(r), $(r.every((o) => o >= -t10 && o < t10), () => `All values in axis param must be in range [-${t10}, ${t10}) but got axis ${r}`), $(r.every((o) => sa(o)), () => `All values in axis param must be integers but got axis ${r}`), r.map((o) => o < 0 ? t10 + o : o); } -function pb(r, e) { - let t6 = [], o = [], n = e != null && Array.isArray(e) && e.length === 0, s = e == null || n ? null : Qa(e, r).sort(), a = 0; +function bb(r, e) { + let t10 = [], o = [], n = e != null && Array.isArray(e) && e.length === 0, s = e == null || n ? null : Ja(e, r).sort(), a = 0; for (let i = 0; i < r.length; ++i) { if (s != null) { if (s[a] === i && r[i] !== 1) throw new Error(`Can't squeeze axis ${i} since its dim '${r[i]}' is not 1`); - (s[a] == null || s[a] > i) && r[i] === 1 && (t6.push(r[i]), o.push(i)), s[a] <= i && a++; + (s[a] == null || s[a] > i) && r[i] === 1 && (t10.push(r[i]), o.push(i)), s[a] <= i && a++; } - r[i] !== 1 && (t6.push(r[i]), o.push(i)); + r[i] !== 1 && (t10.push(r[i]), o.push(i)); } - return { newShape: t6, keptDims: o }; + return { newShape: t10, keptDims: o }; } -function cb(r, e) { - let t6 = null; +function Cb(r, e) { + let t10 = null; if (r == null || r === "float32") - t6 = new Float32Array(e); + t10 = new Float32Array(e); else if (r === "int32") - t6 = new Int32Array(e); + t10 = new Int32Array(e); else if (r === "bool") - t6 = new Uint8Array(e); + t10 = new Uint8Array(e); else throw new Error(`Unknown data type ${r}`); - return t6; + return t10; } -function lb(r, e) { - let t6 = null; +function Sb(r, e) { + let t10 = null; if (r == null || r === "float32") - t6 = new Float32Array(e); + t10 = new Float32Array(e); else if (r === "int32") - t6 = new Int32Array(e); + t10 = new Int32Array(e); else if (r === "bool") - t6 = new Uint8Array(e); + t10 = new Uint8Array(e); else if (r === "string") - t6 = new Array(e); + t10 = new Array(e); else throw new Error(`Unknown data type ${r}`); - return t6; + return t10; } -function mb(r, e) { - for (let t6 = 0; t6 < r.length; t6++) { - let o = r[t6]; +function wb(r, e) { + for (let t10 = 0; t10 < r.length; t10++) { + let o = r[t10]; if (isNaN(o) || !isFinite(o)) throw Error(`A tensor of type ${e} being uploaded contains ${o}.`); } } -function db(r) { +function Ib(r) { return r === "bool" || r === "complex64" || r === "float32" || r === "int32" || r === "string"; } -function OV(r, e) { +function JV(r, e) { return !(e === "complex64" || e === "float32" && r !== "complex64" || e === "int32" && r !== "float32" && r !== "complex64" || e === "bool" && r === "bool"); } -function Wt(r) { - return r instanceof Float32Array || r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray; -} -function Sm(r) { +function up(r) { if (r === "float32" || r === "int32") return 4; if (r === "complex64") @@ -3099,73 +3032,84 @@ function Sm(r) { return 1; throw new Error(`Unknown dtype ${r}`); } -function fb(r) { +function vb(r) { if (r == null) return 0; let e = 0; - return r.forEach((t6) => e += t6.length), e; + return r.forEach((t10) => e += t10.length), e; } -function Po(r) { +function Oo(r) { return typeof r == "string" || r instanceof String; } -function BI(r) { +function GI(r) { return typeof r == "boolean"; } -function VI(r) { +function HI(r) { return typeof r == "number"; } -function np(r) { - return Array.isArray(r) ? np(r[0]) : r instanceof Float32Array ? "float32" : r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray ? "int32" : VI(r) ? "float32" : Po(r) ? "string" : BI(r) ? "bool" : "float32"; +function pp(r) { + return Array.isArray(r) ? pp(r[0]) : r instanceof Float32Array ? "float32" : r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray ? "int32" : HI(r) ? "float32" : Oo(r) ? "string" : GI(r) ? "bool" : "float32"; } -function fs(r) { +function gs(r) { return !!(r && r.constructor && r.call && r.apply); } -function sp(r, e) { - for (let t6 = e; t6 < r; ++t6) - if (r % t6 === 0) - return t6; +function cp(r, e) { + for (let t10 = e; t10 < r; ++t10) + if (r % t10 === 0) + return t10; return r; } -function hs(r) { +function xs(r) { let e = r.length; if (e < 2) return []; - let t6 = new Array(e - 1); - t6[e - 2] = r[e - 1]; + let t10 = new Array(e - 1); + t10[e - 2] = r[e - 1]; for (let o = e - 3; o >= 0; --o) - t6[o] = t6[o + 1] * r[o + 1]; - return t6; + t10[o] = t10[o + 1] * r[o + 1]; + return t10; } -function zI(r, e, t6, o = false) { +function KI(r, e, t10, o = false) { let n = new Array(); if (e.length === 1) { let s = e[0] * (o ? 2 : 1); for (let a = 0; a < s; a++) - n[a] = t6[r + a]; + n[a] = t10[r + a]; } else { let s = e[0], a = e.slice(1), i = a.reduce((p, u) => p * u) * (o ? 2 : 1); for (let p = 0; p < s; p++) - n[p] = zI(r + p * i, a, t6, o); + n[p] = KI(r + p * i, a, t10, o); } return n; } -function Ki(r, e, t6 = false) { +function Xi(r, e, t10 = false) { if (r.length === 0) return e[0]; - let o = r.reduce((n, s) => n * s) * (t6 ? 2 : 1); + let o = r.reduce((n, s) => n * s) * (t10 ? 2 : 1); if (o === 0) return []; if (o !== e.length) - throw new Error(`[${r}] does not match the input size ${e.length}${t6 ? " for a complex tensor" : ""}.`); - return zI(0, r, e, t6); + throw new Error(`[${r}] does not match the input size ${e.length}${t10 ? " for a complex tensor" : ""}.`); + return KI(0, r, e, t10); } -function zc(r, e) { - let t6 = ap(r, e); - for (let o = 0; o < t6.length; o++) - t6[o] = 1; - return t6; +function ez(r, e) { + if (Array.isArray(r)) + return r; + if (e === "float32") + return r instanceof Float32Array ? r : new Float32Array(r); + if (e === "int32") + return r instanceof Int32Array ? r : new Int32Array(r); + if (e === "bool" || e === "string") + return Uint8Array.from(new Int32Array(r)); + throw new Error(`Unknown dtype ${e}`); } -function ap(r, e) { +function Wc(r, e) { + let t10 = lp(r, e); + for (let o = 0; o < t10.length; o++) + t10[o] = 1; + return t10; +} +function lp(r, e) { if (e == null || e === "float32" || e === "complex64") return new Float32Array(r); if (e === "int32") @@ -3174,54 +3118,54 @@ function ap(r, e) { return new Uint8Array(r); throw new Error(`Unknown data type ${e}`); } -function PV(r, e) { - let t6 = r.reduce((o, n) => o * n, 1); +function tz(r, e) { + let t10 = r.reduce((o, n) => o * n, 1); if (e == null || e === "float32") - return Ki(r, new Float32Array(t6)); + return Xi(r, new Float32Array(t10)); if (e === "int32") - return Ki(r, new Int32Array(t6)); + return Xi(r, new Int32Array(t10)); if (e === "bool") - return Ki(r, new Uint8Array(t6)); + return Xi(r, new Uint8Array(t10)); throw new Error(`Unknown data type ${e}`); } -function yt(r) { +function bt(r) { r.forEach((e) => { - E(Number.isInteger(e) && e >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${r}].`); + $(Number.isInteger(e) && e >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${r}].`); }); } -function MV(r, e, t6) { +function rz(r, e, t10) { if (e === 0) return 0; if (e === 1) return r[0]; let o = r[r.length - 1]; for (let n = 0; n < r.length - 1; ++n) - o += t6[n] * r[n]; + o += t10[n] * r[n]; return o; } -function LV(r, e, t6) { +function oz(r, e, t10) { if (e === 0) return []; if (e === 1) return [r]; let o = new Array(e); for (let n = 0; n < o.length - 1; ++n) - o[n] = Math.floor(r / t6[n]), r -= o[n] * t6[n]; + o[n] = Math.floor(r / t10[n]), r -= o[n] * t10[n]; return o[o.length - 1] = r, o; } -function Wc(r) { +function Qi(r) { return r && r.then && typeof r.then == "function"; } -var WI = "tfjsflags"; +var qI = "tfjsflags"; var Uc = class { constructor(e) { - this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = VV, this.populateURLFlags(); + this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = sz, this.populateURLFlags(); } - setPlatform(e, t6) { - this.platform != null && (O().getBool("IS_TEST") || O().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t6; + setPlatform(e, t10) { + this.platform != null && (O().getBool("IS_TEST") || O().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t10; } - registerFlag(e, t6, o) { - if (this.flagRegistry[e] = { evaluationFn: t6, setHook: o }, this.urlFlags[e] != null) { + registerFlag(e, t10, o) { + if (this.flagRegistry[e] = { evaluationFn: t10, setHook: o }, this.urlFlags[e] != null) { let n = this.urlFlags[e]; O().getBool("IS_TEST") || O().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${n}.`), this.set(e, n); } @@ -3232,10 +3176,10 @@ var Uc = class { get(e) { if (e in this.flags) return this.flags[e]; - let t6 = this.evaluateFlag(e); - if (Wc(t6)) + let t10 = this.evaluateFlag(e); + if (Qi(t10)) throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`); - return this.flags[e] = t6, this.flags[e]; + return this.flags[e] = t10, this.flags[e]; } getNumber(e) { return this.get(e); @@ -3249,10 +3193,10 @@ var Uc = class { get features() { return this.flags; } - set(e, t6) { + set(e, t10) { if (this.flagRegistry[e] == null) throw new Error(`Cannot set flag ${e} as it has not been registered.`); - this.flags[e] = t6, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t6); + this.flags[e] = t10, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t10); } evaluateFlag(e) { if (this.flagRegistry[e] == null) @@ -3269,20 +3213,20 @@ var Uc = class { if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined") return; let e = this.getQueryParams(this.global.location.search); - WI in e && e[WI].split(",").forEach((o) => { + qI in e && e[qI].split(",").forEach((o) => { let [n, s] = o.split(":"); - this.urlFlags[n] = WV(n, s); + this.urlFlags[n] = iz(n, s); }); } }; -function VV(r) { +function sz(r) { let e = {}; - return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (t6, ...o) => (zV(e, o[0], o[1]), o.join("="))), e; + return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (t10, ...o) => (az(e, o[0], o[1]), o.join("="))), e; } -function zV(r, e, t6) { - r[decodeURIComponent(e)] = decodeURIComponent(t6 || ""); +function az(r, e, t10) { + r[decodeURIComponent(e)] = decodeURIComponent(t10 || ""); } -function WV(r, e) { +function iz(r, e) { if (e = e.toLowerCase(), e === "true" || e === "false") return e === "true"; if (`${+e}` === e) @@ -3290,15 +3234,15 @@ function WV(r, e) { throw new Error(`Could not parse value flag value ${e} for flag ${r}.`); } function O() { - return hb; + return kb; } -var hb = null; -function UI(r) { - hb = r; +var kb = null; +function jI(r) { + kb = r; } -var gb; -function xb() { - if (gb == null) { +var Nb; +function Tb() { + if (Nb == null) { let r; if (typeof window != "undefined") r = window; @@ -3310,370 +3254,388 @@ function xb() { r = self; else throw new Error("Could not find a global object"); - gb = r; + Nb = r; } - return gb; + return Nb; } -function UV() { - let r = xb(); +function uz() { + let r = Tb(); return r._tfGlobals == null && (r._tfGlobals = /* @__PURE__ */ new Map()), r._tfGlobals; } function Gc(r, e) { - let t6 = UV(); - if (t6.has(r)) - return t6.get(r); + let t10 = uz(); + if (t10.has(r)) + return t10.get(r); { let o = e(); - return t6.set(r, o), t6.get(r); + return t10.set(r, o), t10.get(r); } } -var gs = "Abs"; -var sa = "Acos"; -var aa = "Acosh"; +var ys = "Abs"; +var aa = "Acos"; +var ia = "Acosh"; var eo = "Add"; -var Mo = "AddN"; -var Lo = "All"; -var Bo = "Any"; -var Vo = "ArgMax"; -var Za = "ArgMin"; -var ia = "Asin"; -var ua = "Asinh"; -var pa = "Atan"; -var ca = "Atanh"; -var la = "Atan2"; -var zo = "AvgPool"; -var wm = "AvgPoolGrad"; -var ip = "AvgPool3D"; -var Im = "AvgPool3DGrad"; -var Wo = "BatchMatMul"; -var xs = "BatchToSpaceND"; -var Ja = "Bincount"; -var wne = "BroadcastTo"; -var up = "BroadcastArgs"; +var Po = "AddN"; +var Mo = "All"; +var Lo = "Any"; +var Bo = "ArgMax"; +var ei = "ArgMin"; +var ua = "Asin"; +var pa = "Asinh"; +var ca = "Atan"; +var la = "Atanh"; +var ma = "Atan2"; +var Vo = "AvgPool"; +var mp = "AvgPoolGrad"; +var dp = "AvgPool3D"; +var vm = "AvgPool3DGrad"; +var zo = "BatchMatMul"; +var bs = "BatchToSpaceND"; +var ti = "Bincount"; +var ise = "BroadcastTo"; +var fp = "BroadcastArgs"; var co = "Cast"; -var Uo = "Ceil"; +var Wo = "Ceil"; var lo = "ClipByValue"; -var ei = "Complex"; -var pp = "ComplexAbs"; -var ys = "Concat"; -var Go = "Conv2D"; -var cp = "Conv2DBackpropFilter"; -var Ho = "Conv2DBackpropInput"; -var lp = "Conv3D"; -var vm = "Conv3DBackpropFilterV2"; -var mp = "Conv3DBackpropInputV2"; -var qo = "Cos"; +var ri = "Complex"; +var hp = "ComplexAbs"; +var Cs = "Concat"; +var Uo = "Conv2D"; +var oi = "Conv2DBackpropFilter"; +var Go = "Conv2DBackpropInput"; +var gp = "Conv3D"; +var km = "Conv3DBackpropFilterV2"; +var xp = "Conv3DBackpropInputV2"; +var Ho = "Cos"; var Ko = "Cosh"; -var jo = "Cumprod"; -var Xo = "Cumsum"; -var Yo = "CropAndResize"; -var ti = "DenseBincount"; -var Qo = "DepthToSpace"; -var Zo = "DepthwiseConv2dNative"; -var dp = "DepthwiseConv2dNativeBackpropFilter"; -var fp = "DepthwiseConv2dNativeBackpropInput"; -var hp = "Diag"; -var gp = "Dilation2D"; -var yb = "Dilation2DBackpropInput"; -var bb = "Dilation2DBackpropFilter"; -var Jo = "RealDiv"; -var ri = "Einsum"; -var en = "Elu"; -var km = "EluGrad"; -var ma = "Erf"; -var tn = "Equal"; -var rn = "Exp"; -var bs = "ExpandDims"; -var da = "Expm1"; -var oi = "FFT"; -var Cs = "Fill"; -var on = "FlipLeftRight"; -var nn = "Floor"; -var sn = "FloorDiv"; -var an = "FusedBatchNorm"; -var Ss = "GatherV2"; -var un = "GatherNd"; -var pn = "Greater"; -var cn = "GreaterEqual"; +var qo = "Cumprod"; +var jo = "Cumsum"; +var Xo = "CropAndResize"; +var ni = "DenseBincount"; +var Yo = "DepthToSpace"; +var Qo = "DepthwiseConv2dNative"; +var yp = "DepthwiseConv2dNativeBackpropFilter"; +var bp = "DepthwiseConv2dNativeBackpropInput"; +var si = "Diag"; +var ai = "Dilation2D"; +var _b = "Dilation2DBackpropInput"; +var $b = "Dilation2DBackpropFilter"; +var Zo = "RealDiv"; +var ii = "Einsum"; +var Jo = "Elu"; +var Nm = "EluGrad"; +var da = "Erf"; +var en = "Equal"; +var tn = "Exp"; +var Ss = "ExpandDims"; +var fa = "Expm1"; +var ui = "FFT"; +var ws = "Fill"; +var rn = "FlipLeftRight"; +var on = "Floor"; +var nn = "FloorDiv"; +var sn = "FusedBatchNorm"; +var Is = "GatherV2"; +var an = "GatherNd"; +var un = "Greater"; +var pn = "GreaterEqual"; var mo = "Identity"; -var ni = "IFFT"; -var si = "Imag"; -var fa = "IsFinite"; -var ha = "IsInf"; -var ln = "IsNan"; -var mn = "LeakyRelu"; -var dn = "Less"; -var fn = "LessEqual"; -var xp = "LinSpace"; -var hn = "Log"; -var ga = "Log1p"; -var gn = "LogicalAnd"; -var xn = "LogicalNot"; -var xa = "LogicalOr"; -var GI = "LogicalXor"; -var Ine = "LogSoftmax"; -var vne = "LowerBound"; -var yp = "LRN"; -var Nm = "LRNGrad"; +var pi = "IFFT"; +var ci = "Imag"; +var ha = "IsFinite"; +var ga = "IsInf"; +var cn = "IsNan"; +var ln = "LeakyRelu"; +var mn = "Less"; +var dn = "LessEqual"; +var li = "LinSpace"; +var fn = "Log"; +var xa = "Log1p"; +var hn = "LogicalAnd"; +var gn = "LogicalNot"; +var xn = "LogicalOr"; +var XI = "LogicalXor"; +var use = "LogSoftmax"; +var pse = "LowerBound"; +var mi = "LRN"; +var Tm = "LRNGrad"; var yn = "Max"; var bn = "Maximum"; var Cn = "MaxPool"; -var Tm = "MaxPoolGrad"; -var bp = "MaxPool3D"; -var _m = "MaxPool3DGrad"; -var Cp = "MaxPoolWithArgmax"; +var _m = "MaxPoolGrad"; +var Cp = "MaxPool3D"; +var $m = "MaxPool3DGrad"; +var Sp = "MaxPoolWithArgmax"; var Sn = "Mean"; var wn = "Min"; var In = "Minimum"; var vn = "MirrorPad"; var ya = "Mod"; -var Sp = "Multinomial"; +var wp = "Multinomial"; var kn = "Multiply"; -var ws = "Neg"; +var vs = "Neg"; var Nn = "NotEqual"; var Tn = "NonMaxSuppressionV3"; var ba = "NonMaxSuppressionV4"; var _n = "NonMaxSuppressionV5"; -var Is = "OnesLike"; -var En = "OneHot"; -var vs = "Pack"; -var $n = "PadV2"; -var kne = "Pool"; +var ks = "OnesLike"; +var $n = "OneHot"; +var Ns = "Pack"; +var En = "PadV2"; +var cse = "Pool"; var An = "Pow"; var Rn = "Prelu"; -var Fn = "Prod"; -var wp = "RaggedGather"; -var Ip = "RaggedRange"; -var vp = "RaggedTensorToTensor"; -var ks = "Range"; -var ai = "Real"; -var Dn = "Reciprocal"; +var Dn = "Prod"; +var Ip = "RaggedGather"; +var vp = "RaggedRange"; +var kp = "RaggedTensorToTensor"; +var Ts = "Range"; +var di = "Real"; +var Fn = "Reciprocal"; var On = "Relu"; -var Ns = "Reshape"; +var _s = "Reshape"; var Pn = "ResizeNearestNeighbor"; var Em = "ResizeNearestNeighborGrad"; var Mn = "ResizeBilinear"; -var $m = "ResizeBilinearGrad"; +var Am = "ResizeBilinearGrad"; var Ln = "Relu6"; var Bn = "Reverse"; -var Ca = "Round"; -var Vn = "Rsqrt"; -var zn = "ScatterNd"; -var ii = "SearchSorted"; -var Ts = "Select"; -var Xi = "Selu"; -var _s = "Slice"; -var Wn = "Sin"; +var Vn = "Round"; +var zn = "Rsqrt"; +var Wn = "ScatterNd"; +var fi = "SearchSorted"; +var $s = "Select"; +var Ca = "Selu"; +var Es = "Slice"; +var Un = "Sin"; var Sa = "Sinh"; -var Yi = "Sign"; -var Un = "Sigmoid"; -var Qi = "Softplus"; -var Gn = "Sqrt"; -var Hn = "Sum"; -var Es = "SpaceToBatchND"; -var $s = "SplitV"; +var wa = "Sign"; +var Gn = "Sigmoid"; +var Ia = "Softplus"; +var Hn = "Sqrt"; +var Kn = "Sum"; +var As = "SpaceToBatchND"; +var Rs = "SplitV"; var qn = "Softmax"; -var ui = "SparseFillEmptyRows"; -var wa = "SparseReshape"; -var pi = "SparseSegmentMean"; -var ci = "SparseSegmentSum"; -var li = "SparseToDense"; -var Kn = "SquaredDifference"; -var mi = "Square"; -var jn = "StridedSlice"; -var As = "StringNGrams"; -var di = "StringSplit"; -var fi = "StringToHashBucketFast"; -var Xn = "Sub"; -var Yn = "Tan"; -var Qn = "Tanh"; +var hi = "SparseFillEmptyRows"; +var va = "SparseReshape"; +var gi = "SparseSegmentMean"; +var xi = "SparseSegmentSum"; +var yi = "SparseToDense"; +var jn = "SquaredDifference"; +var bi = "Square"; +var Xn = "StridedSlice"; +var Ds = "StringNGrams"; +var Ci = "StringSplit"; +var Si = "StringToHashBucketFast"; +var Yn = "Sub"; +var Qn = "Tan"; +var Zn = "Tanh"; var to = "Tile"; -var Zn = "TopK"; -var Jn = "Transform"; +var Jn = "TopK"; +var es = "Transform"; var ro = "Transpose"; -var kp = "Unique"; -var Rs = "Unpack"; -var Np = "UnsortedSegmentSum"; -var Nne = "UpperBound"; -var Fs = "ZerosLike"; -var Ds = "Step"; +var Np = "Unique"; +var Fs = "Unpack"; +var Tp = "UnsortedSegmentSum"; +var lse = "UpperBound"; +var Os = "ZerosLike"; +var fo = "Step"; var Zi = "FromPixels"; -var es = "RotateWithOffset"; -var fo = "_FusedMatMul"; -var ho = "FusedConv2D"; -var go = "FusedDepthwiseConv2D"; -function Os(...r) { +var ts = "RotateWithOffset"; +var ho = "_FusedMatMul"; +var go = "FusedConv2D"; +var xo = "FusedDepthwiseConv2D"; +function Ps(...r) { O().getBool("IS_TEST") || O().getBool("PROD") || console.warn(...r); } -function GV(...r) { +function pz(...r) { O().getBool("IS_TEST") || O().getBool("PROD") || console.log(...r); } -var Tp = Gc("kernelRegistry", () => /* @__PURE__ */ new Map()); +var _p = Gc("kernelRegistry", () => /* @__PURE__ */ new Map()); var Hc = Gc("gradRegistry", () => /* @__PURE__ */ new Map()); -function qc(r, e) { - let t6 = Sb(r, e); - return Tp.get(t6); +function Kc(r, e) { + let t10 = Ab(r, e); + return _p.get(t10); } -function Cb(r) { +function Eb(r) { return Hc.get(r); } -function Am(r) { - let e = Tp.entries(), t6 = []; +function Rm(r) { + let e = _p.entries(), t10 = []; for (; ; ) { let { done: o, value: n } = e.next(); if (o) break; let [s, a] = n, [i] = s.split("_"); - i === r && t6.push(a); + i === r && t10.push(a); } - return t6; + return t10; } -function Ia(r) { - let { kernelName: e, backendName: t6 } = r, o = Sb(e, t6); - Tp.has(o) && Os(`The kernel '${e}' for backend '${t6}' is already registered`), Tp.set(o, r); +function ka(r) { + let { kernelName: e, backendName: t10 } = r, o = Ab(e, t10); + _p.has(o) && Ps(`The kernel '${e}' for backend '${t10}' is already registered`), _p.set(o, r); } -function Ane(r) { +function gse(r) { let { kernelName: e } = r; - Hc.has(e) && O().getBool("DEBUG") && Os(`Overriding the gradient for '${e}'`), Hc.set(e, r); + Hc.has(e) && O().getBool("DEBUG") && Ps(`Overriding the gradient for '${e}'`), Hc.set(e, r); } -function Rne(r, e) { - let t6 = Sb(r, e); - if (!Tp.has(t6)) +function xse(r, e) { + let t10 = Ab(r, e); + if (!_p.has(t10)) throw new Error(`The kernel '${r}' for backend '${e}' is not registered`); - Tp.delete(t6); + _p.delete(t10); } -function Fne(r) { +function yse(r) { if (!Hc.has(r)) throw new Error(`The gradient '${r}' for backend is not registered`); Hc.delete(r); } -function Dne(r, e) { - Am(r).forEach((o) => { +function bse(r, e) { + Rm(r).forEach((o) => { let n = Object.assign({}, o, { backendName: e }); - Ia(n); + ka(n); }); } -function Sb(r, e) { +function Ab(r, e) { return `${e}_${r}`; } var y = {}; -Ue(y, { arraysEqual: () => Pr, assert: () => E, assertNonNegativeIntegerDimensions: () => yt, assertNonNull: () => Jr, assertShapesMatch: () => ht, bytesFromStringArray: () => fb, bytesPerElement: () => Sm, checkConversionForErrors: () => mb, clamp: () => op, computeStrides: () => hs, createScalarValue: () => QV, createShuffledIndices: () => RV, decodeString: () => Ap, distSquared: () => _V, encodeString: () => gi, fetch: () => JV, fingerPrint64: () => YV, flatten: () => Oo, getArrayFromDType: () => lb, getTypedArrayFromDType: () => cb, hasEncodingLoss: () => OV, hexToLong: () => Kc, indexToLoc: () => LV, inferDtype: () => np, inferFromImplicitShape: () => DV, isBoolean: () => BI, isFunction: () => fs, isInt: () => na, isNumber: () => VI, isPromise: () => Wc, isScalarShape: () => EV, isString: () => Po, isTypedArray: () => Wt, isValidDtype: () => db, locToIndex: () => MV, makeOnesTypedArray: () => zc, makeZerosNestedTypedArray: () => PV, makeZerosTypedArray: () => ap, nearestDivisor: () => sp, nearestLargerEven: () => kV, now: () => ou, parseAxisParam: () => Qa, randUniform: () => TV, repeatedTry: () => FV, rightPad: () => ji, shuffle: () => LI, shuffleCombo: () => vV, sizeFromShape: () => ze, sizeToSquarishShape: () => AV, squeezeShape: () => pb, sum: () => NV, swap: () => Cm, tanh: () => $V, toNestedArray: () => Ki, toTypedArray: () => $p }); -var kb = rp(r0()); -var ru = kb.default || kb; -function Kc(r) { +Ge(y, { arraysEqual: () => Or, assert: () => $, assertNonNegativeIntegerDimensions: () => bt, assertNonNull: () => Jr, assertShapesMatch: () => gt, bytesFromStringArray: () => vb, bytesPerElement: () => up, checkConversionForErrors: () => wb, clamp: () => ip, computeStrides: () => xs, convertBackendValuesAndArrayBuffer: () => ez, createScalarValue: () => gz, createShuffledIndices: () => YV, decodeString: () => Rp, distSquared: () => KV, encodeString: () => Ii, fetch: () => yz, fingerPrint64: () => hz, flatten: () => os, getArrayFromDType: () => Sb, getTypedArrayFromDType: () => Cb, hasEncodingLoss: () => JV, hexToLong: () => qc, indexToLoc: () => oz, inferDtype: () => pp, inferFromImplicitShape: () => ZV, isBoolean: () => GI, isFunction: () => gs, isInt: () => sa, isNumber: () => HI, isPromise: () => Qi, isScalarShape: () => qV, isString: () => Oo, isTypedArray: () => Bt, isValidDtype: () => Ib, locToIndex: () => rz, makeOnesTypedArray: () => Wc, makeZerosNestedTypedArray: () => tz, makeZerosTypedArray: () => lp, nearestDivisor: () => cp, nearestLargerEven: () => UV, now: () => ou, parseAxisParam: () => Ja, randUniform: () => HV, repeatedTry: () => QV, rightPad: () => Yi, shuffle: () => UI, shuffleCombo: () => WV, sizeFromShape: () => We, sizeToSquarishShape: () => XV, squeezeShape: () => bb, sum: () => GV, swap: () => Im, tanh: () => jV, toNestedArray: () => Xi, toTypedArray: () => Ap }); +var Ob = ap(i0()); +var ru = Ob.default || Ob; +function qc(r) { return ru.fromString(r, true, 16); } -var n0 = Kc("c3a5c85c97cb3127"); -var tu = Kc("b492b66fbe98f273"); -var gr = Kc("9ae16a3b2f90404f"); -function vb(r) { +var p0 = qc("c3a5c85c97cb3127"); +var tu = qc("b492b66fbe98f273"); +var gr = qc("9ae16a3b2f90404f"); +function Fb(r) { return r.xor(r.shru(47)); } -function s0(r, e, t6) { - let o = r.slice(e, e + t6); +function c0(r, e, t10) { + let o = r.slice(e, e + t10); return ru.fromBytes(Array.from(o), true, true); } -function bt(r, e) { - return s0(r, e, 8); +function Ct(r, e) { + return c0(r, e, 8); } -function o0(r, e) { - return s0(r, e, 4); +function u0(r, e) { + return c0(r, e, 4); } function jt(r, e) { return e === 0 ? r : r.shru(e).or(r.shl(64 - e)); } -function hi(r, e, t6 = Kc("9ddfea08eb382d69")) { - let o = r.xor(e).mul(t6); +function wi(r, e, t10 = qc("9ddfea08eb382d69")) { + let o = r.xor(e).mul(t10); o = o.xor(o.shru(47)); - let n = e.xor(o).mul(t6); - return n = n.xor(n.shru(47)), n = n.mul(t6), n; + let n = e.xor(o).mul(t10); + return n = n.xor(n.shru(47)), n = n.mul(t10), n; } -function qV(r, e, t6, o, n, s) { +function lz(r, e, t10, o, n, s) { n = n.add(r), s = jt(s.add(n).add(o), 21); let a = n; - return n = n.add(e), n = n.add(t6), s = s.add(jt(n, 44)), [n.add(o), s.add(a)]; + return n = n.add(e), n = n.add(t10), s = s.add(jt(n, 44)), [n.add(o), s.add(a)]; } -function Fm(r, e, t6, o) { - return qV(bt(r, e), bt(r, e + 8), bt(r, e + 16), bt(r, e + 24), t6, o); +function Fm(r, e, t10, o) { + return lz(Ct(r, e), Ct(r, e + 8), Ct(r, e + 16), Ct(r, e + 24), t10, o); } -function KV(r, e = r.length) { +function mz(r, e = r.length) { if (e >= 8) { - let t6 = gr.add(e * 2), o = bt(r, 0).add(gr), n = bt(r, e - 8), s = jt(n, 37).mul(t6).add(o), a = jt(o, 25).add(n).mul(t6); - return hi(s, a, t6); + let t10 = gr.add(e * 2), o = Ct(r, 0).add(gr), n = Ct(r, e - 8), s = jt(n, 37).mul(t10).add(o), a = jt(o, 25).add(n).mul(t10); + return wi(s, a, t10); } if (e >= 4) { - let t6 = gr.add(e * 2), o = o0(r, 0); - return hi(o.shl(3).add(e), o0(r, e - 4), t6); + let t10 = gr.add(e * 2), o = u0(r, 0); + return wi(o.shl(3).add(e), u0(r, e - 4), t10); } if (e > 0) { - let t6 = r[0], o = r[e >> 1], n = r[e - 1], s = t6 + (o << 8), a = e + (n << 2); - return vb(gr.mul(s).xor(n0.mul(a))).mul(gr); + let t10 = r[0], o = r[e >> 1], n = r[e - 1], s = t10 + (o << 8), a = e + (n << 2); + return Fb(gr.mul(s).xor(p0.mul(a))).mul(gr); } return gr; } -function jV(r, e = r.length) { - let t6 = gr.add(e * 2), o = bt(r, 0).mul(tu), n = bt(r, 8), s = bt(r, e - 8).mul(t6), a = bt(r, e - 16).mul(gr); - return hi(jt(o.add(n), 43).add(jt(s, 30)).add(a), o.add(jt(n.add(gr), 18)).add(s), t6); +function dz(r, e = r.length) { + let t10 = gr.add(e * 2), o = Ct(r, 0).mul(tu), n = Ct(r, 8), s = Ct(r, e - 8).mul(t10), a = Ct(r, e - 16).mul(gr); + return wi(jt(o.add(n), 43).add(jt(s, 30)).add(a), o.add(jt(n.add(gr), 18)).add(s), t10); } -function XV(r, e = r.length) { - let t6 = gr.add(e * 2), o = bt(r, 0).mul(gr), n = bt(r, 8), s = bt(r, e - 8).mul(t6), a = bt(r, e - 16).mul(gr), i = jt(o.add(n), 43).add(jt(s, 30)).add(a), p = hi(i, o.add(jt(n.add(gr), 18)).add(s), t6), u = bt(r, 16).mul(t6), c = bt(r, 24), l = i.add(bt(r, e - 32)).mul(t6), m = p.add(bt(r, e - 24)).mul(t6); - return hi(jt(u.add(c), 43).add(jt(l, 30)).add(m), u.add(jt(c.add(o), 18)).add(l), t6); +function fz(r, e = r.length) { + let t10 = gr.add(e * 2), o = Ct(r, 0).mul(gr), n = Ct(r, 8), s = Ct(r, e - 8).mul(t10), a = Ct(r, e - 16).mul(gr), i = jt(o.add(n), 43).add(jt(s, 30)).add(a), p = wi(i, o.add(jt(n.add(gr), 18)).add(s), t10), u = Ct(r, 16).mul(t10), c = Ct(r, 24), l = i.add(Ct(r, e - 32)).mul(t10), m = p.add(Ct(r, e - 24)).mul(t10); + return wi(jt(u.add(c), 43).add(jt(l, 30)).add(m), u.add(jt(c.add(o), 18)).add(l), t10); } -function YV(r, e = r.length) { - let t6 = ru.fromNumber(81, true); +function hz(r, e = r.length) { + let t10 = ru.fromNumber(81, true); if (e <= 32) - return e <= 16 ? KV(r, e) : jV(r, e); + return e <= 16 ? mz(r, e) : dz(r, e); if (e <= 64) - return XV(r, e); - let o = t6, n = t6.mul(tu).add(113), s = vb(n.mul(gr).add(113)).mul(gr), a = [ru.UZERO, ru.UZERO], i = [ru.UZERO, ru.UZERO]; - o = o.mul(gr).add(bt(r, 0)); + return fz(r, e); + let o = t10, n = t10.mul(tu).add(113), s = Fb(n.mul(gr).add(113)).mul(gr), a = [ru.UZERO, ru.UZERO], i = [ru.UZERO, ru.UZERO]; + o = o.mul(gr).add(Ct(r, 0)); let p = 0, u = (e - 1 >> 6) * 64, c = u + (e - 1 & 63) - 63; do - o = jt(o.add(n).add(a[0]).add(bt(r, p + 8)), 37).mul(tu), n = jt(n.add(a[1]).add(bt(r, p + 48)), 42).mul(tu), o = o.xor(i[1]), n = n.add(a[0]).add(bt(r, p + 40)), s = jt(s.add(i[0]), 33).mul(tu), a = Fm(r, p, a[1].mul(tu), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(bt(r, p + 16))), [s, o] = [o, s], p += 64; + o = jt(o.add(n).add(a[0]).add(Ct(r, p + 8)), 37).mul(tu), n = jt(n.add(a[1]).add(Ct(r, p + 48)), 42).mul(tu), o = o.xor(i[1]), n = n.add(a[0]).add(Ct(r, p + 40)), s = jt(s.add(i[0]), 33).mul(tu), a = Fm(r, p, a[1].mul(tu), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(Ct(r, p + 16))), [s, o] = [o, s], p += 64; while (p !== u); let l = tu.add(s.and(255).shl(1)); - return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = jt(o.add(n).add(a[0]).add(bt(r, p + 8)), 37).mul(l), n = jt(n.add(a[1]).add(bt(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(bt(r, p + 40))), s = jt(s.add(i[0]), 33).mul(l), a = Fm(r, p, a[1].mul(l), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(bt(r, p + 16))), [s, o] = [o, s], hi(hi(a[0], i[0], l).add(vb(n).mul(n0)).add(s), hi(a[1], i[1], l).add(o), l); + return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = jt(o.add(n).add(a[0]).add(Ct(r, p + 8)), 37).mul(l), n = jt(n.add(a[1]).add(Ct(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(Ct(r, p + 40))), s = jt(s.add(i[0]), 33).mul(l), a = Fm(r, p, a[1].mul(l), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(Ct(r, p + 16))), [s, o] = [o, s], wi(wi(a[0], i[0], l).add(Fb(n).mul(p0)).add(s), wi(a[1], i[1], l).add(o), l); } -function QV(r, e) { - return e === "string" ? gi(r) : $p([r], e); +function gz(r, e) { + return e === "string" ? Ii(r) : Ap([r], e); } -function ZV(r, e) { +function xz(r, e) { return r instanceof Float32Array && e === "float32" || r instanceof Int32Array && e === "int32" || r instanceof Uint8Array && e === "bool"; } -function $p(r, e) { +function Ap(r, e) { if (e === "string") throw new Error("Cannot convert a string[] to a TypedArray"); - if (Array.isArray(r) && (r = Oo(r)), O().getBool("DEBUG") && mb(r, e), ZV(r, e)) + if (Array.isArray(r) && (r = os(r)), O().getBool("DEBUG") && wb(r, e), xz(r, e)) return r; if (e == null || e === "float32" || e === "complex64") return new Float32Array(r); if (e === "int32") return new Int32Array(r); if (e === "bool") { - let t6 = new Uint8Array(r.length); - for (let o = 0; o < t6.length; ++o) - Math.round(r[o]) !== 0 && (t6[o] = 1); - return t6; + let t10 = new Uint8Array(r.length); + for (let o = 0; o < t10.length; ++o) + Math.round(r[o]) !== 0 && (t10[o] = 1); + return t10; } else throw new Error(`Unknown data type ${e}`); } function ou() { return O().platform.now(); } -function JV(r, e) { +function yz(r, e) { return O().platform.fetch(r, e); } -function gi(r, e = "utf-8") { +function Ii(r, e = "utf-8") { return e = e || "utf-8", O().platform.encode(r, e); } -function Ap(r, e = "utf-8") { +function Rp(r, e = "utf-8") { return e = e || "utf-8", O().platform.decode(r, e); } -var Dm = class { - constructor(e, t6) { - this.backendTimer = e, this.logger = t6, t6 == null && (this.logger = new Nb()); +function Bt(r) { + return O().platform.isTypedArray(r); +} +function os(r, e = [], t10 = false) { + if (e == null && (e = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Qi(r) || r == null || Bt(r) && t10) + e.push(r); + else if (Array.isArray(r) || Bt(r)) + for (let o = 0; o < r.length; ++o) + os(r[o], e, t10); + else { + let o = -1; + for (let n of Object.keys(r)) + /^([1-9]+[0-9]*|0)$/.test(n) && (o = Math.max(o, Number(n))); + for (let n = 0; n <= o; n++) + os(r[n], e, t10); } - profileKernel(e, t6, o) { + return e; +} +var Om = class { + constructor(e, t10) { + this.backendTimer = e, this.logger = t10, t10 == null && (this.logger = new Pb()); + } + profileKernel(e, t10, o) { let n, s = () => { n = o(); }, a, i = ou(); @@ -3689,44 +3651,44 @@ var Dm = class { for (let u = 0; u < n.length; u++) { let c = n[u]; c.data().then((l) => { - ez(l, c.dtype, e); + bz(l, c.dtype, e); }); } - return { kernelName: e, outputs: n, inputs: t6, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; + return { kernelName: e, outputs: n, inputs: t10, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; } logKernelProfile(e) { - let { kernelName: t6, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e; + let { kernelName: t10, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e; o.forEach((i) => { Promise.all([i.data(), n, a]).then((p) => { - this.logger.logKernelProfile(t6, i, p[0], p[1], s, p[2]); + this.logger.logKernelProfile(t10, i, p[0], p[1], s, p[2]); }); }); } }; -function ez(r, e, t6) { +function bz(r, e, t10) { if (e !== "float32") return false; for (let o = 0; o < r.length; o++) { let n = r[o]; if (isNaN(n) || !isFinite(n)) - return console.warn(`Found ${n} in the result of '${t6}'`), true; + return console.warn(`Found ${n} in the result of '${t10}'`), true; } return false; } -var Nb = class { - logKernelProfile(e, t6, o, n, s, a) { - let i = typeof n == "number" ? ji(`${n}ms`, 9) : n.error, p = ji(e, 25), u = t6.rank, c = t6.size, l = ji(t6.shape.toString(), 14), m = ""; +var Pb = class { + logKernelProfile(e, t10, o, n, s, a) { + let i = typeof n == "number" ? Yi(`${n}ms`, 9) : n.error, p = Yi(e, 25), u = t10.rank, c = t10.size, l = Yi(t10.shape.toString(), 14), m = ""; for (let d in s) { let f = s[d]; if (f != null) { - let h = f.shape || t6.shape, g = h.length; + let h = f.shape || t10.shape, g = h.length; m += `${d}: ${g}D ${g > 0 ? h : ""} `; } } console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); } }; -function a0(r, e, t6) { +function l0(r, e, t10) { let o = {}, n = {}; for (let p = 0; p < e.length; p++) o[e[p].id] = true; @@ -3744,7 +3706,7 @@ function a0(r, e, t6) { } } let s = {}; - s[t6.id] = true; + s[t10.id] = true; let a = {}; for (let p = r.length - 1; p >= 0; p--) { let u = r[p], c = u.inputs; @@ -3770,7 +3732,7 @@ function a0(r, e, t6) { } return i; } -function i0(r, e, t6, o) { +function m0(r, e, t10, o) { for (let n = e.length - 1; n >= 0; n--) { let s = e[n], a = []; if (s.outputs.forEach((p) => { @@ -3782,11 +3744,11 @@ function i0(r, e, t6, o) { for (let p in s.inputs) { if (!(p in i)) throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(i)}.`); - let u = t6(() => i[p]()); + let u = t10(() => i[p]()); if (u.dtype !== "float32") throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`); let c = s.inputs[p]; - if (!Pr(u.shape, c.shape)) + if (!Or(u.shape, c.shape)) throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`); if (r[c.id] == null) r[c.id] = u; @@ -3797,66 +3759,66 @@ function i0(r, e, t6, o) { } } } -var u0 = 20; +var d0 = 20; var jc = 3; -var Tb = 7; -function p0(r, e, t6, o) { - let n = hs(e), s = tz(r, e, t6, n), a = e.length, i = Om(r, e, t6, n, s), p = ["Tensor"]; - return o && (p.push(` dtype: ${t6}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` +var Mb = 7; +function f0(r, e, t10, o) { + let n = xs(e), s = Cz(r, e, t10, n), a = e.length, i = Pm(r, e, t10, n, s), p = ["Tensor"]; + return o && (p.push(` dtype: ${t10}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` `)), p.join(` `); } -function tz(r, e, t6, o) { - let n = ze(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t6 === "complex64" ? Yc(r) : r; +function Cz(r, e, t10, o) { + let n = We(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t10 === "complex64" ? Yc(r) : r; if (i > 1) for (let u = 0; u < n / s; u++) { let c = u * s; for (let l = 0; l < s; l++) - a[l] = Math.max(a[l], Xc(p[c + l], 0, t6).length); + a[l] = Math.max(a[l], Xc(p[c + l], 0, t10).length); } return a; } -function Xc(r, e, t6) { +function Xc(r, e, t10) { let o; - return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Tb))} + ${parseFloat(r[1].toFixed(Tb))}j` : Po(r) ? o = `'${r}'` : t6 === "bool" ? o = c0(r) : o = parseFloat(r.toFixed(Tb)).toString(), ji(o, e); + return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Mb))} + ${parseFloat(r[1].toFixed(Mb))}j` : Oo(r) ? o = `'${r}'` : t10 === "bool" ? o = h0(r) : o = parseFloat(r.toFixed(Mb)).toString(), Yi(o, e); } -function c0(r) { +function h0(r) { return r === 0 ? "false" : "true"; } -function Om(r, e, t6, o, n, s = true) { - let a = t6 === "complex64" ? 2 : 1, i = e[0], p = e.length; +function Pm(r, e, t10, o, n, s = true) { + let a = t10 === "complex64" ? 2 : 1, i = e[0], p = e.length; if (p === 0) { - if (t6 === "complex64") { + if (t10 === "complex64") { let h = Yc(r); - return [Xc(h[0], 0, t6)]; + return [Xc(h[0], 0, t10)]; } - return t6 === "bool" ? [c0(r[0])] : [r[0].toString()]; + return t10 === "bool" ? [h0(r[0])] : [r[0].toString()]; } if (p === 1) { - if (i > u0) { + if (i > d0) { let g = jc * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - jc) * a, i * a)); - return t6 === "complex64" && (x = Yc(x), b = Yc(b)), ["[" + x.map((C, w) => Xc(C, n[w], t6)).join(", ") + ", ..., " + b.map((C, w) => Xc(C, n[i - jc + w], t6)).join(", ") + "]"]; + return t10 === "complex64" && (x = Yc(x), b = Yc(b)), ["[" + x.map((C, w) => Xc(C, n[w], t10)).join(", ") + ", ..., " + b.map((C, w) => Xc(C, n[i - jc + w], t10)).join(", ") + "]"]; } - return ["[" + (t6 === "complex64" ? Yc(r) : Array.from(r)).map((g, x) => Xc(g, n[x], t6)).join(", ") + "]"]; + return ["[" + (t10 === "complex64" ? Yc(r) : Array.from(r)).map((g, x) => Xc(g, n[x], t10)).join(", ") + "]"]; } let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = []; - if (i > u0) { + if (i > d0) { for (let h = 0; h < jc; h++) { let g = h * l, x = g + l; - m.push(...Om(r.slice(g, x), u, t6, c, n, false)); + m.push(...Pm(r.slice(g, x), u, t10, c, n, false)); } m.push("..."); for (let h = i - jc; h < i; h++) { let g = h * l, x = g + l; - m.push(...Om(r.slice(g, x), u, t6, c, n, h === i - 1)); + m.push(...Pm(r.slice(g, x), u, t10, c, n, h === i - 1)); } } else for (let h = 0; h < i; h++) { let g = h * l, x = g + l; - m.push(...Om(r.slice(g, x), u, t6, c, n, h === i - 1)); + m.push(...Pm(r.slice(g, x), u, t10, c, n, h === i - 1)); } let d = p === 2 ? "," : ""; - m[0] = "[" + m[0] + d; + m[0] = "[" + (i > 0 ? m[0] + d : ""); for (let h = 1; h < m.length - 1; h++) m[h] = " " + m[h] + d; let f = `, @@ -3868,34 +3830,34 @@ function Om(r, e, t6, o, n, s = true) { } function Yc(r) { let e = []; - for (let t6 = 0; t6 < r.length; t6 += 2) - e.push([r[t6], r[t6 + 1]]); + for (let t10 = 0; t10 < r.length; t10 += 2) + e.push([r[t10], r[t10 + 1]]); return e; } var st = class { - constructor(e, t6, o) { - if (this.dtype = t6, this.shape = e.slice(), this.size = ze(e), o != null) { + constructor(e, t10, o) { + if (this.dtype = t10, this.shape = e.slice(), this.size = We(e), o != null) { let n = o.length; - E(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + $(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); } - if (t6 === "complex64") + if (t10 === "complex64") throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag)."); - this.values = o || lb(t6, this.size), this.strides = hs(e); + this.values = o || Sb(t10, this.size), this.strides = xs(e); } - set(e, ...t6) { - t6.length === 0 && (t6 = [0]), E(t6.length === this.rank, () => `The number of provided coordinates (${t6.length}) must match the rank (${this.rank})`); - let o = this.locToIndex(t6); + set(e, ...t10) { + t10.length === 0 && (t10 = [0]), $(t10.length === this.rank, () => `The number of provided coordinates (${t10.length}) must match the rank (${this.rank})`); + let o = this.locToIndex(t10); this.values[o] = e; } get(...e) { e.length === 0 && (e = [0]); - let t6 = 0; + let t10 = 0; for (let n of e) { - if (n < 0 || n >= this.shape[t6]) { + if (n < 0 || n >= this.shape[t10]) { let s = `Requested out of range element at ${e}. Buffer shape=${this.shape}`; throw new Error(s); } - t6++; + t10++; } let o = e[e.length - 1]; for (let n = 0; n < e.length - 1; ++n) @@ -3907,68 +3869,68 @@ var st = class { return 0; if (this.rank === 1) return e[0]; - let t6 = e[e.length - 1]; + let t10 = e[e.length - 1]; for (let o = 0; o < e.length - 1; ++o) - t6 += this.strides[o] * e[o]; - return t6; + t10 += this.strides[o] * e[o]; + return t10; } indexToLoc(e) { if (this.rank === 0) return []; if (this.rank === 1) return [e]; - let t6 = new Array(this.shape.length); - for (let o = 0; o < t6.length - 1; ++o) - t6[o] = Math.floor(e / this.strides[o]), e -= t6[o] * this.strides[o]; - return t6[t6.length - 1] = e, t6; + let t10 = new Array(this.shape.length); + for (let o = 0; o < t10.length - 1; ++o) + t10[o] = Math.floor(e / this.strides[o]), e -= t10[o] * this.strides[o]; + return t10[t10.length - 1] = e, t10; } get rank() { return this.shape.length; } toTensor() { - return rs().makeTensor(this.values, this.shape, this.dtype); + return ns().makeTensor(this.values, this.shape, this.dtype); } }; -var rs = null; -var Rp = null; -var rz = null; -function l0(r) { - rs = r; +var ns = null; +var Dp = null; +var Sz = null; +function g0(r) { + ns = r; } -function m0(r) { - Rp = r; +function x0(r) { + Dp = r; } -function d0(r) { - rz = r; +function y0(r) { + Sz = r; } var it = class { - constructor(e, t6, o, n) { - this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t6 || "float32", this.size = ze(e), this.strides = hs(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; + constructor(e, t10, o, n) { + this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t10 || "float32", this.size = We(e), this.strides = xs(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; } get rank() { return this.shape.length; } async buffer() { let e = await this.data(); - return Rp.buffer(this.shape, this.dtype, e); + return Dp.buffer(this.shape, this.dtype, e); } bufferSync() { - return Rp.buffer(this.shape, this.dtype, this.dataSync()); + return Dp.buffer(this.shape, this.dtype, this.dataSync()); } async array() { let e = await this.data(); - return Ki(this.shape, e, this.dtype === "complex64"); + return Xi(this.shape, e, this.dtype === "complex64"); } arraySync() { - return Ki(this.shape, this.dataSync(), this.dtype === "complex64"); + return Xi(this.shape, this.dataSync(), this.dtype === "complex64"); } async data() { this.throwIfDisposed(); - let e = rs().read(this.dataId); + let e = ns().read(this.dataId); if (this.dtype === "string") { - let t6 = await e; + let t10 = await e; try { - return t6.map((o) => Ap(o)); + return t10.map((o) => Rp(o)); } catch (o) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } @@ -3976,26 +3938,26 @@ var it = class { return e; } dataToGPU(e) { - return this.throwIfDisposed(), rs().readToGPU(this.dataId, e); + return this.throwIfDisposed(), ns().readToGPU(this.dataId, e); } dataSync() { this.throwIfDisposed(); - let e = rs().readSync(this.dataId); + let e = ns().readSync(this.dataId); if (this.dtype === "string") try { - return e.map((t6) => Ap(t6)); - } catch (t6) { + return e.map((t10) => Rp(t10)); + } catch (t10) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } return e; } async bytes() { this.throwIfDisposed(); - let e = await rs().read(this.dataId); + let e = await ns().read(this.dataId); return this.dtype === "string" ? e : new Uint8Array(e.buffer); } dispose() { - this.isDisposed || (rs().disposeTensor(this), this.isDisposedInternal = true); + this.isDisposed || (ns().disposeTensor(this), this.isDisposedInternal = true); } get isDisposed() { return this.isDisposedInternal; @@ -4005,115 +3967,115 @@ var it = class { throw new Error("Tensor is disposed."); } print(e = false) { - return Rp.print(this, e); + return Dp.print(this, e); } clone() { - return this.throwIfDisposed(), Rp.clone(this); + return this.throwIfDisposed(), Dp.clone(this); } toString(e = false) { - let t6 = this.dataSync(); - return p0(t6, this.shape, this.dtype, e); + let t10 = this.dataSync(); + return f0(t10, this.shape, this.dtype, e); } cast(e) { - return this.throwIfDisposed(), Rp.cast(this, e); + return this.throwIfDisposed(), Dp.cast(this, e); } - variable(e = true, t6, o) { - return this.throwIfDisposed(), rs().makeVariable(this, e, t6, o); + variable(e = true, t10, o) { + return this.throwIfDisposed(), ns().makeVariable(this, e, t10, o); } }; Object.defineProperty(it, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null }); -function oz() { +function wz() { return Gc("Tensor", () => it); } -oz(); -var va = class extends it { - constructor(e, t6, o, n) { - super(e.shape, e.dtype, e.dataId, n), this.trainable = t6, this.name = o; +wz(); +var Na = class extends it { + constructor(e, t10, o, n) { + super(e.shape, e.dtype, e.dataId, n), this.trainable = t10, this.name = o; } assign(e) { if (e.dtype !== this.dtype) throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`); - if (!Pr(e.shape, this.shape)) + if (!Or(e.shape, this.shape)) throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`); - rs().disposeTensor(this), this.dataId = e.dataId, rs().incRef(this, null); + ns().disposeTensor(this), this.dataId = e.dataId, ns().incRef(this, null); } dispose() { - rs().disposeVariable(this), this.isDisposedInternal = true; + ns().disposeVariable(this), this.isDisposedInternal = true; } }; -Object.defineProperty(va, Symbol.hasInstance, { value: (r) => r instanceof it && r.assign != null && r.assign instanceof Function }); -var h0 = {}; -Ue(h0, { assertTypesMatch: () => Fb, getTensorsInContainer: () => Qc, isTensorInList: () => sz, makeTypesMatch: () => Re }); -var _b; +Object.defineProperty(Na, Symbol.hasInstance, { value: (r) => r instanceof it && r.assign != null && r.assign instanceof Function }); +var C0 = {}; +Ge(C0, { assertTypesMatch: () => Ub, getTensorsInContainer: () => Qc, isTensorInList: () => vz, makeTypesMatch: () => De }); +var Lb; (function(r) { r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6"; -})(_b || (_b = {})); -var Eb; +})(Lb || (Lb = {})); +var Bb; (function(r) { r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64"; -})(Eb || (Eb = {})); -var $b; +})(Bb || (Bb = {})); +var Vb; (function(r) { r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64"; -})($b || ($b = {})); -var Ab; +})(Vb || (Vb = {})); +var zb; (function(r) { r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64"; -})(Ab || (Ab = {})); -var Rb; +})(zb || (zb = {})); +var Wb; (function(r) { r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64"; -})(Rb || (Rb = {})); -var nz = { float32: Ab, int32: Eb, bool: $b, complex64: Rb }; +})(Wb || (Wb = {})); +var Iz = { float32: zb, int32: Bb, bool: Vb, complex64: Wb }; function dt(r, e) { if (r === "string" || e === "string") { if (r === "string" && e === "string") return "string"; throw new Error(`Can not upcast ${r} with ${e}`); } - return nz[r][e]; + return Iz[r][e]; } -function ka(r) { +function Ta(r) { return dt(r, "int32"); } -function Re(r, e) { +function De(r, e) { if (r.dtype === e.dtype) return [r, e]; - let t6 = dt(r.dtype, e.dtype); - return [r.cast(t6), e.cast(t6)]; + let t10 = dt(r.dtype, e.dtype); + return [r.cast(t10), e.cast(t10)]; } -function Fb(r, e) { - E(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`); +function Ub(r, e) { + $(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`); } -function sz(r, e) { - return e.some((t6) => t6.id === r.id); +function vz(r, e) { + return e.some((t10) => t10.id === r.id); } function Qc(r) { let e = []; - return f0(r, e, /* @__PURE__ */ new Set()), e; + return b0(r, e, /* @__PURE__ */ new Set()), e; } -function f0(r, e, t6) { +function b0(r, e, t10) { if (r == null) return; if (r instanceof it) { e.push(r); return; } - if (!az(r)) + if (!kz(r)) return; let o = r; for (let n in o) { let s = o[n]; - t6.has(s) || (t6.add(s), f0(s, e, t6)); + t10.has(s) || (t10.add(s), b0(s, e, t10)); } } -function az(r) { +function kz(r) { return Array.isArray(r) || typeof r == "object"; } -function Db(r) { +function Gb(r) { return r.kernelName != null; } -var Pm = class { +var Mm = class { constructor() { this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() { return Array.from(new Set(this.kernels.map((e) => e.name))); @@ -4124,9 +4086,9 @@ var Pm = class { this.registeredVariables[e].dispose(); } }; -var xi = class { +var vi = class { constructor(e) { - this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new Pm(); + this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new Mm(); } async ready() { if (this.pendingBackendInit != null) @@ -4135,8 +4097,8 @@ var xi = class { if (this.backendInstance != null) return; let e = this.getSortedBackends(); - for (let t6 = 0; t6 < e.length; t6++) { - let o = e[t6]; + for (let t10 = 0; t10 < e.length; t10++) { + let o = e[t10]; if (await this.initializeBackend(o).success) { await this.setBackend(o); return; @@ -4148,8 +4110,8 @@ var xi = class { if (this.pendingBackendInit != null) throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); if (this.backendInstance == null) { - let { name: e, asyncInit: t6 } = this.initializeBackendsAndReturnBest(); - if (t6) + let { name: e, asyncInit: t10 } = this.initializeBackendsAndReturnBest(); + if (t10) throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); this.setBackend(e); } @@ -4161,8 +4123,8 @@ var xi = class { findBackend(e) { if (!(e in this.registry)) if (e in this.registryFactory) { - let { asyncInit: t6 } = this.initializeBackend(e); - if (t6) + let { asyncInit: t10 } = this.initializeBackend(e); + if (t10) return null; } else return null; @@ -4171,43 +4133,43 @@ var xi = class { findBackendFactory(e) { return e in this.registryFactory ? this.registryFactory[e].factory : null; } - registerBackend(e, t6, o = 1) { - return e in this.registryFactory ? (Os(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t6, priority: o }, true); + registerBackend(e, t10, o = 1) { + return e in this.registryFactory ? (Ps(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t10, priority: o }, true); } async setBackend(e) { if (this.registryFactory[e] == null) throw new Error(`Backend name '${e}' not found in registry`); if (this.backendName = e, this.registry[e] == null) { this.backendInstance = null; - let { success: t6, asyncInit: o } = this.initializeBackend(e); - if (!(o ? await t6 : t6)) + let { success: t10, asyncInit: o } = this.initializeBackend(e); + if (!(o ? await t10 : t10)) return false; } - return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Dm(this.backendInstance), true; + return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Om(this.backendInstance), true; } setupRegisteredKernels() { - Am(this.backendName).forEach((t6) => { - t6.setupFunc != null && t6.setupFunc(this.backendInstance); + Rm(this.backendName).forEach((t10) => { + t10.setupFunc != null && t10.setupFunc(this.backendInstance); }); } disposeRegisteredKernels(e) { - Am(e).forEach((o) => { + Rm(e).forEach((o) => { o.disposeFunc != null && o.disposeFunc(this.registry[e]); }); } initializeBackend(e) { - let t6 = this.registryFactory[e]; - if (t6 == null) + let t10 = this.registryFactory[e]; + if (t10 == null) throw new Error(`Cannot initialize backend ${e}, no registration found.`); try { - let o = t6.factory(); + let o = t10.factory(); if (o && !(o instanceof Zr) && typeof o.then == "function") { - let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, Os(`Initialization of backend ${e} failed`), Os(a.stack || a.message)), false)); + let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, Ps(`Initialization of backend ${e} failed`), Ps(a.stack || a.message)), false)); return this.pendingBackendInit = s, { success: s, asyncInit: true }; } else return this.registry[e] = o, { success: true, asyncInit: false }; } catch (o) { - return Os(`Initialization of backend ${e} failed`), Os(o.stack || o.message), { success: false, asyncInit: false }; + return Ps(`Initialization of backend ${e} failed`), Ps(o.stack || o.message), { success: false, asyncInit: false }; } } removeBackend(e) { @@ -4218,87 +4180,87 @@ var xi = class { getSortedBackends() { if (Object.keys(this.registryFactory).length === 0) throw new Error("No backend found in registry."); - return Object.keys(this.registryFactory).sort((e, t6) => this.registryFactory[t6].priority - this.registryFactory[e].priority); + return Object.keys(this.registryFactory).sort((e, t10) => this.registryFactory[t10].priority - this.registryFactory[e].priority); } initializeBackendsAndReturnBest() { let e = this.getSortedBackends(); - for (let t6 = 0; t6 < e.length; t6++) { - let o = e[t6], { success: n, asyncInit: s } = this.initializeBackend(o); + for (let t10 = 0; t10 < e.length; t10++) { + let o = e[t10], { success: n, asyncInit: s } = this.initializeBackend(o); if (s || n) return { name: o, asyncInit: s }; } throw new Error("Could not initialize any backends, all backend initializations failed."); } - moveData(e, t6) { - let o = this.state.tensorInfo.get(t6), n = o.backend, s = this.readSync(t6), a = n.refCount(t6); - n.disposeData(t6, true), o.backend = e, e.move(t6, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + moveData(e, t10) { + let o = this.state.tensorInfo.get(t10), n = o.backend, s = this.readSync(t10), a = n.refCount(t10); + n.disposeData(t10, true), o.backend = e, e.move(t10, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; } - tidy(e, t6) { + tidy(e, t10) { let o = null; - if (t6 == null) { + if (t10 == null) { if (typeof e != "function") throw new Error("Please provide a function to tidy()"); - t6 = e; + t10 = e; } else { if (typeof e != "string" && !(e instanceof String)) throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); - if (typeof t6 != "function") + if (typeof t10 != "function") throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); o = e; } let n; - return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t6(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n)); + return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t10(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n)); } - scopedRun(e, t6, o) { + scopedRun(e, t10, o) { e(); try { let n = o(); - return t6(), n; + return t10(), n; } catch (n) { - throw t6(), n; + throw t10(), n; } } nextTensorId() { - return xi.nextTensorId++; + return vi.nextTensorId++; } nextVariableId() { - return xi.nextVariableId++; + return vi.nextVariableId++; } clone(e) { - let t6 = T.runKernel(mo, { x: e }), o = { x: e }, n = (a) => ({ x: () => { + let t10 = T.runKernel(mo, { x: e }), o = { x: e }, n = (a) => ({ x: () => { let i = "float32", p = { x: a }, u = { dtype: i }; return T.runKernel(co, p, u); } }), s = []; - return this.addTapeNode(this.state.activeScope.name, o, [t6], n, s, {}), t6; + return this.addTapeNode(this.state.activeScope.name, o, [t10], n, s, {}), t10; } - runKernel(e, t6, o) { - if (this.backendName == null && this.backend, !(qc(e, this.backendName) != null)) + runKernel(e, t10, o) { + if (this.backendName == null && this.backend, !(Kc(e, this.backendName) != null)) throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`); - return this.runKernelFunc({ kernelName: e, inputs: t6, attrs: o }); + return this.runKernelFunc({ kernelName: e, inputs: t10, attrs: o }); } shouldCheckForMemLeaks() { return this.ENV.getBool("IS_TEST"); } - checkKernelForMemLeak(e, t6, o) { + checkKernelForMemLeak(e, t10, o) { let n = this.backend.numDataIds(), s = 0; o.forEach((p) => { s += p.dtype === "complex64" ? 3 : 1; }); - let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t6 - s - a; + let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t10 - s - a; if (i > 0) throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`); } runKernelFunc(e) { - let t6, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors; + let t10, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors; this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0); let i; this.backendName == null && this.backend; - let p, u = Db(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; - if (Db(e)) { + let p, u = Gb(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (Gb(e)) { let { kernelName: f, inputs: h, attrs: g } = e; this.backendName == null && this.backend; - let x = qc(f, this.backendName); - E(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => { + let x = Kc(f, this.backendName); + $(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => { let b = this.backend.numDataIds(); p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend }); let C = Array.isArray(p) ? p : [p]; @@ -4321,78 +4283,78 @@ var xi = class { return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, x), x; }; } - let { inputs: c, attrs: l } = e, m = Db(e) ? null : e.backwardsFunc, d; + let { inputs: c, attrs: l } = e, m = Gb(e) ? null : e.backwardsFunc, d; return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { - !this.ENV.getBool("DEBUG") && !this.state.profiling ? t6 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t6 = d.outputs); - }), n && this.addTapeNode(u, c, t6, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t6.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t6 : t6[0]; + !this.ENV.getBool("DEBUG") && !this.state.profiling ? t10 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t10 = d.outputs); + }), n && this.addTapeNode(u, c, t10, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t10.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t10 : t10[0]; } saveTensorsForBackwardMode(e) { return e.map((o) => this.keep(this.clone(o))); } - getTensorsForGradient(e, t6, o) { - let n = Cb(e); + getTensorsForGradient(e, t10, o) { + let n = Eb(e); if (n != null) { let s = n.inputsToSave || [], a = n.outputsToSave || [], i; - n.saveAllInputs ? (E(Array.isArray(t6), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t6).map((u) => t6[u])) : i = s.map((u) => t6[u]); + n.saveAllInputs ? ($(Array.isArray(t10), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t10).map((u) => t10[u])) : i = s.map((u) => t10[u]); let p = o.filter((u, c) => a[c]); return i.concat(p); } return []; } - makeTensor(e, t6, o, n) { + makeTensor(e, t10, o, n) { if (e == null) throw new Error("Values passed to engine.makeTensor() are null"); o = o || "float32", n = n || this.backend; let s = e; - o === "string" && Po(e[0]) && (s = e.map((p) => gi(p))); - let a = n.write(s, t6, o), i = new it(t6, o, a, this.nextTensorId()); + o === "string" && Oo(e[0]) && (s = e.map((p) => Ii(p))); + let a = n.write(s, t10, o), i = new it(t10, o, a, this.nextTensorId()); if (this.trackTensor(i, n), o === "string") { - let p = this.state.tensorInfo.get(a), u = fb(s); + let p = this.state.tensorInfo.get(a), u = vb(s); this.state.numBytes += u - p.bytes, p.bytes = u; } return i; } - makeTensorFromDataId(e, t6, o, n) { + makeTensorFromDataId(e, t10, o, n) { o = o || "float32"; - let s = { dataId: e, shape: t6, dtype: o }; + let s = { dataId: e, shape: t10, dtype: o }; return this.makeTensorFromTensorInfo(s, n); } - makeTensorFromTensorInfo(e, t6) { + makeTensorFromTensorInfo(e, t10) { let { dataId: o, shape: n, dtype: s } = e, a = new it(n, s, o, this.nextTensorId()); - return this.trackTensor(a, t6), a; + return this.trackTensor(a, t10), a; } - makeVariable(e, t6 = true, o, n) { + makeVariable(e, t10 = true, o, n) { o = o || this.nextVariableId().toString(), n != null && n !== e.dtype && (e = e.cast(n)); - let s = new va(e, t6, o, this.nextTensorId()); + let s = new Na(e, t10, o, this.nextTensorId()); if (this.state.registeredVariables[s.name] != null) throw new Error(`Variable with name ${s.name} was already registered`); return this.state.registeredVariables[s.name] = s, this.incRef(s, this.backend), s; } - trackTensor(e, t6) { + trackTensor(e, t10) { this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++; let o = 0; - e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * Sm(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t6 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof va || this.track(e); + e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * up(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t10 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof Na || this.track(e); } - incRef(e, t6) { - this.trackTensor(e, t6), this.backend.incRef(e.dataId); + incRef(e, t10) { + this.trackTensor(e, t10), this.backend.incRef(e.dataId); } - removeDataId(e, t6) { - this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t6 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--); + removeDataId(e, t10) { + this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t10 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--); } disposeTensor(e) { if (!this.state.tensorInfo.has(e.dataId)) return; - let t6 = this.state.tensorInfo.get(e.dataId); - if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t6.bytes), e.dtype !== "complex64" && e.dtype !== "string") { - let o = e.size * Sm(e.dtype); + let t10 = this.state.tensorInfo.get(e.dataId); + if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t10.bytes), e.dtype !== "complex64" && e.dtype !== "string") { + let o = e.size * up(e.dtype); this.state.numBytes -= o; } - t6.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t6.backend); + t10.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t10.backend); } disposeVariables() { for (let e in this.state.registeredVariables) { - let t6 = this.state.registeredVariables[e]; - this.disposeVariable(t6); + let t10 = this.state.registeredVariables[e]; + this.disposeVariable(t10); } } disposeVariable(e) { @@ -4404,8 +4366,8 @@ var xi = class { } async profile(e) { this.state.profiling = true; - let t6 = this.state.numBytes, o = this.state.numTensors; - this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t6, this.state.activeProfile.newTensors = this.state.numTensors - o; + let t10 = this.state.numBytes, o = this.state.numTensors; + this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t10, this.state.activeProfile.newTensors = this.state.numTensors - o; for (let n of this.state.activeProfile.kernels) n.kernelTimeMs = await n.kernelTimeMs, n.extraInfo = await n.extraInfo; return this.state.activeProfile; @@ -4413,11 +4375,11 @@ var xi = class { isTapeOn() { return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; } - addTapeNode(e, t6, o, n, s, a) { - let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t6, outputs: o, saved: s }, p = Cb(e); + addTapeNode(e, t10, o, n, s, a) { + let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t10, outputs: o, saved: s }, p = Eb(e); p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => { if (c == null) { - let m = o[l], d = ap(m.size, m.dtype); + let m = o[l], d = lp(m.size, m.dtype); return this.makeTensor(d, m.shape, m.dtype); } return c; @@ -4433,32 +4395,32 @@ var xi = class { this.state.gradientDepth--; } startScope(e) { - let t6 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ }; - e && (t6.name = e), this.state.scopeStack.push(t6), this.state.activeScope = t6; + let t10 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ }; + e && (t10.name = e), this.state.scopeStack.push(t10), this.state.activeScope = t10; } endScope(e) { - let t6 = Qc(e), o = new Set(t6.map((s) => s.id)); + let t10 = Qc(e), o = new Set(t10.map((s) => s.id)); for (let s = 0; s < this.state.activeScope.track.length; s++) { let a = this.state.activeScope.track[s]; !a.kept && !o.has(a.id) && a.dispose(); } let n = this.state.scopeStack.pop(); - this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t6.forEach((s) => { + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t10.forEach((s) => { !s.kept && s.scopeId === n.id && this.track(s); }); } - gradients(e, t6, o, n = false) { - if (E(t6.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32") + gradients(e, t10, o, n = false) { + if ($(t10.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32") throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`); let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e)); - E(s instanceof it, () => "The result y returned by f() must be a tensor."); - let a = a0(this.state.activeTape, t6, s); - if (!n && a.length === 0 && t6.length > 0) + $(s instanceof it, () => "The result y returned by f() must be a tensor."); + let a = l0(this.state.activeTape, t10, s); + if (!n && a.length === 0 && t10.length > 0) throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); return this.tidy("backward", () => { let i = {}; - i[s.id] = o == null ? iz(s.shape) : o, i0(i, a, (u) => this.tidy(u), uz); - let p = t6.map((u) => i[u.id]); + i[s.id] = o == null ? Nz(s.shape) : o, m0(i, a, (u) => this.tidy(u), Tz); + let p = t10.map((u) => i[u.id]); return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => { for (let c of u.saved) c.dispose(); @@ -4466,15 +4428,15 @@ var xi = class { }); } customGrad(e) { - return E(fs(e), () => "The f passed in customGrad(f) must be a function."), (...t6) => { - E(t6.every((i) => i instanceof it), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + return $(gs(e), () => "The f passed in customGrad(f) must be a function."), (...t10) => { + $(t10.every((i) => i instanceof it), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); let o, n = {}; - t6.forEach((i, p) => { + t10.forEach((i, p) => { n[p] = i; }); - let s = (i, p) => (o = e(...t6, p), E(o.value instanceof it, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), E(fs(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { + let s = (i, p) => (o = e(...t10, p), $(o.value instanceof it, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(gs(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { let u = o.gradFunc(i, p), c = Array.isArray(u) ? u : [u]; - E(c.length === t6.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), E(c.every((m) => m instanceof it), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + $(c.length === t10.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof it), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); let l = {}; return c.forEach((m, d) => { l[d] = () => m; @@ -4489,12 +4451,12 @@ var xi = class { read(e) { return this.state.tensorInfo.get(e).backend.read(e); } - readToGPU(e, t6) { - return this.state.tensorInfo.get(e).backend.readToGPU(e, t6); + readToGPU(e, t10) { + return this.state.tensorInfo.get(e).backend.readToGPU(e, t10); } async time(e) { - let t6 = ou(), o = await this.backend.time(e); - return o.wallMs = ou() - t6, o; + let t10 = ou(), o = await this.backend.time(e); + return o.wallMs = ou() - t10, o; } track(e) { return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e; @@ -4503,63 +4465,63 @@ var xi = class { return this.state.registeredVariables; } reset() { - this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new Pm(); + this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new Mm(); for (let e in this.registry) this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e]; this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null; } }; -xi.nextTensorId = 0; -xi.nextVariableId = 0; -function iz(r) { - let e = zc(ze(r), "float32"); +vi.nextTensorId = 0; +vi.nextVariableId = 0; +function Nz(r) { + let e = Wc(We(r), "float32"); return T.makeTensor(e, r, "float32"); } -function Ob() { - let r = xb(); +function Hb() { + let r = Tb(); if (r._tfengine == null) { let e = new Uc(r); - r._tfengine = new xi(e); + r._tfengine = new vi(e); } - return UI(r._tfengine.ENV), l0(() => r._tfengine), r._tfengine; + return jI(r._tfengine.ENV), g0(() => r._tfengine), r._tfengine; } -var T = Ob(); -function uz(r, e) { - let t6 = { a: r, b: e }; - return T.runKernel(eo, t6); +var T = Hb(); +function Tz(r, e) { + let t10 = { a: r, b: e }; + return T.runKernel(eo, t10); } -var yi = {}; -Ue(yi, { isBrowser: () => Mb, isMobile: () => lz, mockIsMobile: () => cz }); -function pz() { +var ki = {}; +Ge(ki, { isBrowser: () => qb, isMobile: () => Ez, mockIsMobile: () => $z }); +function _z() { return typeof navigator != "undefined" && navigator != null; } -var Pb; -function cz(r) { - Pb = r; +var Kb; +function $z(r) { + Kb = r; } -function lz(r) { - if (Pb !== void 0) - return Pb; - if (r || pz()) { +function Ez(r) { + if (Kb !== void 0) + return Kb; + if (r || _z()) { if (r || (r = navigator), r.product === "ReactNative") return true; let e = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : ""); if (!e) { - let t6 = r; - return t6.userAgentData && t6.userAgentData.mobile; + let t10 = r; + return t10.userAgentData && t10.userAgentData.mobile; } return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0, 4)); } return false; } -function Mb() { +function qb() { return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined"; } var oo = O(); oo.registerFlag("DEBUG", () => false, (r) => { r && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); }); -oo.registerFlag("IS_BROWSER", () => Mb()); +oo.registerFlag("IS_BROWSER", () => qb()); oo.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); oo.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); oo.registerFlag("PROD", () => false); @@ -4571,110 +4533,115 @@ oo.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); oo.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); oo.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); function or(r, e) { - let t6 = r; - if (Wt(r)) + let t10 = r; + if (Bt(r)) return e === "string" ? [] : [r.length]; - if (typeof r == "object" && "texture" in r) { - let n = r.channels || "RGBA"; - return [r.height, r.width * n.length]; + if (typeof r == "object") { + if ("texture" in r) { + let s = r.channels || "RGBA"; + return [r.height, r.width * s.length]; + } else if ("buffer" in r && !(r.buffer instanceof ArrayBuffer)) + return [r.buffer.size / (e == null ? 4 : up(e))]; } if (!Array.isArray(r)) return []; - let o = []; - for (; Array.isArray(t6) || Wt(t6) && e !== "string"; ) - o.push(t6.length), t6 = t6[0]; - return Array.isArray(r) && O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && x0(r, o, []), o; + let n = []; + for (; Array.isArray(t10) || Bt(t10) && e !== "string"; ) + n.push(t10.length), t10 = t10[0]; + return Array.isArray(r) && O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && w0(r, n, []), n; } -function x0(r, e, t6) { - if (t6 = t6 || [], !Array.isArray(r) && !Wt(r)) { - E(e.length === 0, () => `Element arr[${t6.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`); +function w0(r, e, t10) { + if (t10 = t10 || [], !Array.isArray(r) && !Bt(r)) { + $(e.length === 0, () => `Element arr[${t10.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`); return; } - E(e.length > 0, () => `Element arr[${t6.join("][")}] should be a primitive, but is an array of ${r.length} elements`), E(r.length === e[0], () => `Element arr[${t6.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`); + $(e.length > 0, () => `Element arr[${t10.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t10.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`); let o = e.slice(1); for (let n = 0; n < r.length; ++n) - x0(r[n], o, t6.concat(n)); + w0(r[n], o, t10.concat(n)); } -function g0(r, e, t6, o) { +function S0(r, e, t10, o) { if (r !== "string_or_numeric") { if (r == null) throw new Error("Expected dtype cannot be null."); if (r !== "numeric" && r !== e || r === "numeric" && e === "string") - throw new Error(`Argument '${t6}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`); + throw new Error(`Argument '${t10}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`); } } -function v(r, e, t6, o = "numeric") { +function v(r, e, t10, o = "numeric") { if (r instanceof it) - return g0(o, r.dtype, e, t6), r; - let n = np(r); - if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), g0(o, n, e, t6), r == null || !Wt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { + return S0(o, r.dtype, e, t10), r; + let n = pp(r); + if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), S0(o, n, e, t10), r == null || !Bt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { let p = r == null ? "null" : r.constructor.name; - throw new Error(`Argument '${e}' passed to '${t6}' must be a Tensor or TensorLike, but got '${p}'`); + throw new Error(`Argument '${e}' passed to '${t10}' must be a Tensor or TensorLike, but got '${p}'`); } let s = or(r, n); - !Wt(r) && !Array.isArray(r) && (r = [r]); - let i = n !== "string" ? $p(r, n) : Oo(r, [], true); + !Bt(r) && !Array.isArray(r) && (r = [r]); + let i = n !== "string" ? Ap(r, n) : os(r, [], true); return T.makeTensor(i, s, n); } -function Na(r, e, t6, o = "numeric") { +function _a(r, e, t10, o = "numeric") { if (!Array.isArray(r)) - throw new Error(`Argument ${e} passed to ${t6} must be a \`Tensor[]\` or \`TensorLike[]\``); - return r.map((s, a) => v(s, `${e}[${a}]`, t6, o)); + throw new Error(`Argument ${e} passed to ${t10} must be a \`Tensor[]\` or \`TensorLike[]\``); + return r.map((s, a) => v(s, `${e}[${a}]`, t10, o)); } -var Lb = "__op"; +var jb = "__op"; function N(r) { let e = Object.keys(r); if (e.length !== 1) throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${e.length} keys.`); - let t6 = e[0], o = r[t6]; - t6.endsWith("_") && (t6 = t6.substring(0, t6.length - 1)), t6 = t6 + Lb; + let t10 = e[0], o = r[t10]; + t10.endsWith("_") && (t10 = t10.substring(0, t10.length - 1)), t10 = t10 + jb; let n = (...s) => { - T.startScope(t6); + T.startScope(t10); try { let a = o(...s); - return Wc(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; + return Qi(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; } catch (a) { throw T.endScope(null), a; } }; - return Object.defineProperty(n, "name", { value: t6, configurable: true }), n; + return Object.defineProperty(n, "name", { value: t10, configurable: true }), n; } -function mz(r, e) { - let t6 = v(r, "real", "complex"), o = v(e, "imag", "complex"); - ht(t6.shape, o.shape, `real and imag shapes, ${t6.shape} and ${o.shape}, must match in call to tf.complex().`); - let n = { real: t6, imag: o }; - return T.runKernel(ei, n); +function Az(r, e) { + let t10 = v(r, "real", "complex"), o = v(e, "imag", "complex"); + gt(t10.shape, o.shape, `real and imag shapes, ${t10.shape} and ${o.shape}, must match in call to tf.complex().`); + let n = { real: t10, imag: o }; + return T.runKernel(ri, n); } -var Tr = N({ complex_: mz }); -function xr(r, e, t6, o) { - if (o == null && (o = np(r)), o === "complex64") +var Tr = N({ complex_: Az }); +function xr(r, e, t10, o) { + if (o == null) + o = pp(r); + else if (o === "complex64") throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag)."); - if (typeof r == "object" && "texture" in r) { + if (typeof r == "object" && ("texture" in r || "buffer" in r && !(r.buffer instanceof ArrayBuffer))) { if (o !== "float32" && o !== "int32") - throw new Error(`Creating tensor from texture only supports 'float32'|'int32' dtype, while the dtype is ${o}.`); - return r.channels = r.channels || "RGBA", T.backend.createTensorFromTexture(r, e || t6, o); + throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${o}.`); + return T.backend.createTensorFromGPUData(r, e || t10, o); } - if (!Wt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") + if (!Bt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); if (e != null) { - yt(e); - let n = ze(e), s = ze(t6); - E(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`); - for (let a = 0; a < t6.length; ++a) { - let i = t6[a], p = a === t6.length - 1 ? i !== ze(e.slice(a)) : true; - E(t6[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t6}) does not match the provided shape (${e}). `); + bt(e); + let n = We(e), s = We(t10); + $(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`); + for (let a = 0; a < t10.length; ++a) { + let i = t10[a], p = a === t10.length - 1 ? i !== We(e.slice(a)) : true; + $(t10[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t10}) does not match the provided shape (${e}). `); } } - return !Wt(r) && !Array.isArray(r) && (r = [r]), e = e || t6, r = o !== "string" ? $p(r, o) : Oo(r, [], true), T.makeTensor(r, e, o); + return !Bt(r) && !Array.isArray(r) && (r = [r]), e = e || t10, r = o !== "string" ? Ap(r, o) : os(r, [], true), T.makeTensor(r, e, o); } -function nr(r, e, t6) { - let o = or(r, t6); - return xr(r, e, o, t6); +function nr(r, e, t10) { + let o = or(r, t10); + return xr(r, e, o, t10); } var Zc = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; -var Mm = 4; -async function b0(r, e) { - let t6 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); +var Lm = 4; +async function v0(r, e) { + let t10 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); for (let a = 0; a < n.length; ++a) { let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i]; if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64") @@ -4682,25 +4649,25 @@ async function b0(r, e) { let u = { name: i, shape: p.shape, dtype: p.dtype }; if (p.dtype === "string") { let c = new Promise(async (l) => { - let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + Mm * m.length, f = new Uint8Array(d), h = 0; + let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + Lm * m.length, f = new Uint8Array(d), h = 0; for (let g = 0; g < m.length; g++) { let x = m[g], b = new Uint8Array(new Uint32Array([x.length]).buffer); - f.set(b, h), h += Mm, f.set(x, h), h += x.length; + f.set(b, h), h += Lm, f.set(x, h), h += x.length; } l(f); }); o.push(c); } else o.push(p.data()); - e != null && (u.group = e), t6.push(u); + e != null && (u.group = e), t10.push(u); } let s = await Promise.all(o); - return { data: dz(s), specs: t6 }; + return { data: Rz(s), specs: t10 }; } -function Lm(r, e) { - let t6 = {}, o, n = 0; +function Bm(r, e) { + let t10 = {}, o, n = 0; for (let s of e) { - let a = s.name, i = s.dtype, p = s.shape, u = ze(p), c; + let a = s.name, i = s.dtype, p = s.shape, u = We(p), c; if ("quantization" in s) { let l = s.quantization; if (l.dtype === "uint8" || l.dtype === "uint16") { @@ -4720,7 +4687,7 @@ function Lm(r, e) { c[h] = g * l.scale + l.min; } } else if (l.dtype === "float16") - o === void 0 && (o = xz()), c = o(f); + o === void 0 && (o = Pz()), c = o(f); else throw new Error(`Unsupported quantization type ${l.dtype} for weight type float32.`); else if (i === "int32") { @@ -4735,11 +4702,11 @@ function Lm(r, e) { throw new Error(`Unsupported dtype in weight '${a}': ${i}`); n += u * m; } else if (i === "string") { - let l = ze(s.shape); + let l = We(s.shape); c = []; for (let m = 0; m < l; m++) { - let d = new Uint32Array(r.slice(n, n + Mm))[0]; - n += Mm; + let d = new Uint32Array(r.slice(n, n + Lm))[0]; + n += Lm; let f = new Uint8Array(r.slice(n, n + d)); c.push(f), n += d; } @@ -4757,49 +4724,49 @@ function Lm(r, e) { for (let x = 0; x < d.length; x++) d[x] = c[x * 2], f[x] = c[x * 2 + 1]; let h = nr(d, p, "float32"), g = nr(f, p, "float32"); - t6[a] = Tr(h, g), h.dispose(), g.dispose(); + t10[a] = Tr(h, g), h.dispose(), g.dispose(); } else throw new Error(`Unsupported dtype in weight '${a}': ${i}`); n += u * l; } - i !== "complex64" && (t6[a] = nr(c, p, i)); + i !== "complex64" && (t10[a] = nr(c, p, i)); } - return t6; + return t10; } -function dz(r) { +function Rz(r) { if (r === null) throw new Error(`Invalid input value: ${JSON.stringify(r)}`); - let e = 0, t6 = []; + let e = 0, t10 = []; r.forEach((s) => { - if (e += s.byteLength, t6.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array)) + if (e += s.byteLength, t10.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array)) throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`); }); let o = new Uint8Array(e), n = 0; - return t6.forEach((s) => { + return t10.forEach((s) => { o.set(new Uint8Array(s.buffer), n), n += s.byteLength; }), o.buffer; } -var Bb = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); -function y0(r) { - return Bb ? Buffer.byteLength(r) : new Blob([r]).size; +var Xb = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); +function I0(r) { + return Xb ? Buffer.byteLength(r) : new Blob([r]).size; } -function C0(r) { - if (Bb) +function k0(r) { + if (Xb) return Buffer.from(r).toString("base64"); - let e = new Uint8Array(r), t6 = ""; + let e = new Uint8Array(r), t10 = ""; for (let o = 0, n = e.length; o < n; o++) - t6 += String.fromCharCode(e[o]); - return btoa(t6); + t10 += String.fromCharCode(e[o]); + return btoa(t10); } -function S0(r) { - if (Bb) { +function N0(r) { + if (Xb) { let o = Buffer.from(r, "base64"); return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength); } - let e = atob(r), t6 = new Uint8Array(e.length); + let e = atob(r), t10 = new Uint8Array(e.length); for (let o = 0; o < e.length; ++o) - t6.set([e.charCodeAt(o)], o); - return t6.buffer; + t10.set([e.charCodeAt(o)], o); + return t10.buffer; } function Fp(r) { if (r.length === 1) @@ -4808,63 +4775,63 @@ function Fp(r) { r.forEach((n) => { e += n.byteLength; }); - let t6 = new Uint8Array(e), o = 0; + let t10 = new Uint8Array(e), o = 0; return r.forEach((n) => { - t6.set(new Uint8Array(n), o), o += n.byteLength; - }), t6.buffer; + t10.set(new Uint8Array(n), o), o += n.byteLength; + }), t10.buffer; } -function Vb(r) { +function Yb(r) { let e = "/"; for (r = r.trim(); r.endsWith(e); ) r = r.slice(0, r.length - 1); - let t6 = r.split(e); - return t6[t6.length - 1]; + let t10 = r.split(e); + return t10[t10.length - 1]; } -function Bm(r, e) { - let t6 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e }; - return r.signature != null && (t6.signature = r.signature), r.userDefinedMetadata != null && (t6.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t6.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t6.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t6.trainingConfig = r.trainingConfig), t6; +function Vm(r, e) { + let t10 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e }; + return r.signature != null && (t10.signature = r.signature), r.userDefinedMetadata != null && (t10.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t10.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t10.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t10.trainingConfig = r.trainingConfig), t10; } -function zb(r, e, t6) { +function Qb(r, e, t10) { let o = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy }; if (r.trainingConfig != null && (o.trainingConfig = r.trainingConfig), r.weightsManifest != null) { if (!e) throw new Error("modelJSON has weightsManifest but weightSpecs is null"); - if (!t6) + if (!t10) throw new Error("modelJSON has weightsManifest but weightData is null"); - o.weightSpecs = e, o.weightData = t6; + o.weightSpecs = e, o.weightData = t10; } return r.signature != null && (o.signature = r.signature), r.userDefinedMetadata != null && (o.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (o.modelInitializer = r.modelInitializer), r.initializerSignature != null && (o.initializerSignature = r.initializerSignature), o; } -async function Dp(r, e) { - let t6, o; - return r.weightsManifest != null && ([t6, o] = await e(r.weightsManifest)), zb(r, t6, o); +async function Op(r, e) { + let t10, o; + return r.weightsManifest != null && ([t10, o] = await e(r.weightsManifest)), Qb(r, t10, o); } -function Ps(r) { +function Ms(r) { if (r.modelTopology instanceof ArrayBuffer) throw new Error("Expected JSON model topology, received ArrayBuffer."); - return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : y0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : y0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength }; + return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : I0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : I0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength }; } -function Vm(r) { +function zm(r) { let e = []; - for (let t6 of r) - e.push(...t6.weights); + for (let t10 of r) + e.push(...t10.weights); return e; } -function fz() { - let r = (t6) => { - let o = t6 << 13, n = 0; +function Dz() { + let r = (t10) => { + let o = t10 << 13, n = 0; for (; (o & 8388608) === 0; ) n -= 8388608, o <<= 1; return o &= -8388609, n += 947912704, o | n; }, e = new Uint32Array(2048); e[0] = 0; - for (let t6 = 1; t6 < 1024; t6++) - e[t6] = r(t6); - for (let t6 = 1024; t6 < 2048; t6++) - e[t6] = 939524096 + (t6 - 1024 << 13); + for (let t10 = 1; t10 < 1024; t10++) + e[t10] = r(t10); + for (let t10 = 1024; t10 < 2048; t10++) + e[t10] = 939524096 + (t10 - 1024 << 13); return e; } -function hz() { +function Fz() { let r = new Uint32Array(64); r[0] = 0, r[31] = 1199570944, r[32] = 2147483648, r[63] = 3347054592; for (let e = 1; e < 31; e++) @@ -4873,59 +4840,59 @@ function hz() { r[e] = 2147483648 + (e - 32 << 23); return r; } -function gz() { +function Oz() { let r = new Uint32Array(64); for (let e = 0; e < 64; e++) r[e] = 1024; return r[0] = r[32] = 0, r; } -function xz() { - let r = fz(), e = hz(), t6 = gz(); +function Pz() { + let r = Dz(), e = Fz(), t10 = Oz(); return (o) => { let n = new ArrayBuffer(4 * o.length), s = new Uint32Array(n); for (let a = 0; a < o.length; a++) { - let i = o[a], p = r[t6[i >> 10] + (i & 1023)] + e[i >> 10]; + let i = o[a], p = r[t10[i >> 10] + (i & 1023)] + e[i >> 10]; s[a] = p; } return new Float32Array(n); }; } -var lt = class { +var mt = class { constructor() { this.saveRouters = [], this.loadRouters = []; } static getInstance() { - return lt.instance == null && (lt.instance = new lt()), lt.instance; + return mt.instance == null && (mt.instance = new mt()), mt.instance; } static registerSaveRouter(e) { - lt.getInstance().saveRouters.push(e); + mt.getInstance().saveRouters.push(e); } static registerLoadRouter(e) { - lt.getInstance().loadRouters.push(e); + mt.getInstance().loadRouters.push(e); } static getSaveHandlers(e) { - return lt.getHandlers(e, "save"); + return mt.getHandlers(e, "save"); } - static getLoadHandlers(e, t6) { - return lt.getHandlers(e, "load", t6); + static getLoadHandlers(e, t10) { + return mt.getHandlers(e, "load", t10); } - static getHandlers(e, t6, o) { + static getHandlers(e, t10, o) { let n = []; - return (t6 === "load" ? lt.getInstance().loadRouters : lt.getInstance().saveRouters).forEach((a) => { + return (t10 === "load" ? mt.getInstance().loadRouters : mt.getInstance().saveRouters).forEach((a) => { let i = a(e, o); i !== null && n.push(i); }), n; } }; -var w0 = (r) => lt.registerSaveRouter(r); -var I0 = (r) => lt.registerLoadRouter(r); -var v0 = (r) => lt.getSaveHandlers(r); -var k0 = (r, e) => lt.getLoadHandlers(r, e); -var Wb = "tensorflowjs"; -var Ub = 1; +var T0 = (r) => mt.registerSaveRouter(r); +var _0 = (r) => mt.registerLoadRouter(r); +var $0 = (r) => mt.getSaveHandlers(r); +var E0 = (r, e) => mt.getLoadHandlers(r, e); +var Zb = "tensorflowjs"; +var Jb = 1; var nu = "models_store"; -var bi = "model_info_store"; -function N0() { +var Ni = "model_info_store"; +function A0() { if (!O().getBool("IS_BROWSER")) throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); let r = typeof window == "undefined" ? self : window, e = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB; @@ -4933,13 +4900,13 @@ function N0() { throw new Error("The current browser does not appear to support IndexedDB."); return e; } -function Gb(r) { +function eC(r) { let e = r.result; - e.createObjectStore(nu, { keyPath: "modelPath" }), e.createObjectStore(bi, { keyPath: "modelPath" }); + e.createObjectStore(nu, { keyPath: "modelPath" }), e.createObjectStore(Ni, { keyPath: "modelPath" }); } -var Ms = class { +var Ls = class { constructor(e) { - if (this.indexedDB = N0(), e == null || !e) + if (this.indexedDB = A0(), e == null || !e) throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); this.modelPath = e; } @@ -4951,12 +4918,12 @@ var Ms = class { async load() { return this.databaseAction(this.modelPath); } - databaseAction(e, t6) { + databaseAction(e, t10) { return new Promise((o, n) => { - let s = this.indexedDB.open(Wb, Ub); - s.onupgradeneeded = () => Gb(s), s.onsuccess = () => { + let s = this.indexedDB.open(Zb, Jb); + s.onupgradeneeded = () => eC(s), s.onsuccess = () => { let a = s.result; - if (t6 == null) { + if (t10 == null) { let i = a.transaction(nu, "readonly"), u = i.objectStore(nu).get(this.modelPath); u.onsuccess = () => { if (u.result == null) @@ -4964,12 +4931,12 @@ var Ms = class { o(u.result.modelArtifacts); }, u.onerror = (c) => (a.close(), n(u.error)), i.oncomplete = () => a.close(); } else { - let i = Ps(t6), p = a.transaction(bi, "readwrite"), u = p.objectStore(bi), c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), l; + let i = Ms(t10), p = a.transaction(Ni, "readwrite"), u = p.objectStore(Ni), c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), l; c.onsuccess = () => { l = a.transaction(nu, "readwrite"); - let d = l.objectStore(nu).put({ modelPath: this.modelPath, modelArtifacts: t6, modelArtifactsInfo: i }); + let d = l.objectStore(nu).put({ modelPath: this.modelPath, modelArtifacts: t10, modelArtifactsInfo: i }); d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => { - u = p.objectStore(bi); + u = p.objectStore(Ni); let h = u.delete(this.modelPath); h.onsuccess = () => (a.close(), n(d.error)), h.onerror = (g) => (a.close(), n(d.error)); }; @@ -4981,39 +4948,39 @@ var Ms = class { }); } }; -Ms.URL_SCHEME = "indexeddb://"; -var T0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ms.URL_SCHEME) ? yz(r.slice(Ms.URL_SCHEME.length)) : null; -lt.registerSaveRouter(T0); -lt.registerLoadRouter(T0); -function yz(r) { - return new Ms(r); +Ls.URL_SCHEME = "indexeddb://"; +var R0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ls.URL_SCHEME) ? Mz(r.slice(Ls.URL_SCHEME.length)) : null; +mt.registerSaveRouter(R0); +mt.registerLoadRouter(R0); +function Mz(r) { + return new Ls(r); } -function bz(r) { - return r.startsWith(Ms.URL_SCHEME) ? r.slice(Ms.URL_SCHEME.length) : r; +function Lz(r) { + return r.startsWith(Ls.URL_SCHEME) ? r.slice(Ls.URL_SCHEME.length) : r; } -var zm = class { +var Wm = class { constructor() { - this.indexedDB = N0(); + this.indexedDB = A0(); } async listModels() { - return new Promise((e, t6) => { - let o = this.indexedDB.open(Wb, Ub); - o.onupgradeneeded = () => Gb(o), o.onsuccess = () => { - let n = o.result, s = n.transaction(bi, "readonly"), i = s.objectStore(bi).getAll(); + return new Promise((e, t10) => { + let o = this.indexedDB.open(Zb, Jb); + o.onupgradeneeded = () => eC(o), o.onsuccess = () => { + let n = o.result, s = n.transaction(Ni, "readonly"), i = s.objectStore(Ni).getAll(); i.onsuccess = () => { let p = {}; for (let u of i.result) p[u.modelPath] = u.modelArtifactsInfo; e(p); - }, i.onerror = (p) => (n.close(), t6(i.error)), s.oncomplete = () => n.close(); - }, o.onerror = (n) => t6(o.error); + }, i.onerror = (p) => (n.close(), t10(i.error)), s.oncomplete = () => n.close(); + }, o.onerror = (n) => t10(o.error); }); } async removeModel(e) { - return e = bz(e), new Promise((t6, o) => { - let n = this.indexedDB.open(Wb, Ub); - n.onupgradeneeded = () => Gb(n), n.onsuccess = () => { - let s = n.result, a = s.transaction(bi, "readwrite"), i = a.objectStore(bi), p = i.get(e), u; + return e = Lz(e), new Promise((t10, o) => { + let n = this.indexedDB.open(Zb, Jb); + n.onupgradeneeded = () => eC(n), n.onsuccess = () => { + let s = n.result, a = s.transaction(Ni, "readwrite"), i = a.objectStore(Ni), p = i.get(e), u; p.onsuccess = () => { if (p.result == null) return s.close(), o(new Error(`Cannot find model with path '${e}' in IndexedDB.`)); @@ -5021,7 +4988,7 @@ var zm = class { let c = i.delete(e), l = () => { u = s.transaction(nu, "readwrite"); let d = u.objectStore(nu).delete(e); - d.onsuccess = () => t6(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error); + d.onsuccess = () => t10(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error); }; c.onsuccess = l, c.onerror = (m) => (l(), s.close(), o(p.error)); } @@ -5032,48 +4999,48 @@ var zm = class { }); } }; -var Ta = "/"; -var Op = "tensorflowjs_models"; -var _0 = "info"; -var Cz = "model_topology"; -var Sz = "weight_specs"; -var wz = "weight_data"; -var Iz = "model_metadata"; -function E0(r) { - return { info: [Op, r, _0].join(Ta), topology: [Op, r, Cz].join(Ta), weightSpecs: [Op, r, Sz].join(Ta), weightData: [Op, r, wz].join(Ta), modelMetadata: [Op, r, Iz].join(Ta) }; +var $a = "/"; +var Pp = "tensorflowjs_models"; +var D0 = "info"; +var Bz = "model_topology"; +var Vz = "weight_specs"; +var zz = "weight_data"; +var Wz = "model_metadata"; +function F0(r) { + return { info: [Pp, r, D0].join($a), topology: [Pp, r, Bz].join($a), weightSpecs: [Pp, r, Vz].join($a), weightData: [Pp, r, zz].join($a), modelMetadata: [Pp, r, Wz].join($a) }; } -function $0(r) { +function O0(r) { for (let e of Object.values(r)) window.localStorage.removeItem(e); } -function vz(r) { - let e = r.split(Ta); +function Uz(r) { + let e = r.split($a); if (e.length < 3) throw new Error(`Invalid key format: ${r}`); - return e.slice(1, e.length - 1).join(Ta); + return e.slice(1, e.length - 1).join($a); } -function kz(r) { - return r.startsWith(Ls.URL_SCHEME) ? r.slice(Ls.URL_SCHEME.length) : r; +function Gz(r) { + return r.startsWith(Bs.URL_SCHEME) ? r.slice(Bs.URL_SCHEME.length) : r; } -var Ls = class { +var Bs = class { constructor(e) { if (!O().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined") throw new Error("The current environment does not support local storage."); if (this.LS = window.localStorage, e == null || !e) throw new Error("For local storage, modelPath must not be null, undefined or empty."); - this.modelPath = e, this.keys = E0(this.modelPath); + this.modelPath = e, this.keys = F0(this.modelPath); } async save(e) { if (e.modelTopology instanceof ArrayBuffer) throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); { - let t6 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = Ps(e); + let t10 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = Ms(e); try { - this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t6), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, C0(e.weightData)); + this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t10), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, k0(e.weightData)); let s = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 }; return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n }; } catch (s) { - throw $0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); + throw O0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); } } } @@ -5083,57 +5050,57 @@ var Ls = class { throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); if (e.modelTopologyType !== "JSON") throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); - let t6 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology)); + let t10 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology)); if (o == null) throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); - t6.modelTopology = o; + t10.modelTopology = o; let n = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); if (n == null) throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); - t6.weightSpecs = n; + t10.weightSpecs = n; let s = this.LS.getItem(this.keys.modelMetadata); if (s != null) { let i = JSON.parse(s); - t6.format = i.format, t6.generatedBy = i.generatedBy, t6.convertedBy = i.convertedBy, i.signature != null && (t6.signature = i.signature), i.userDefinedMetadata != null && (t6.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t6.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t6.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t6.trainingConfig = i.trainingConfig); + t10.format = i.format, t10.generatedBy = i.generatedBy, t10.convertedBy = i.convertedBy, i.signature != null && (t10.signature = i.signature), i.userDefinedMetadata != null && (t10.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t10.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t10.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t10.trainingConfig = i.trainingConfig); } let a = this.LS.getItem(this.keys.weightData); if (a == null) throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); - return t6.weightData = S0(a), t6; + return t10.weightData = N0(a), t10; } }; -Ls.URL_SCHEME = "localstorage://"; -var A0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ls.URL_SCHEME) ? Nz(r.slice(Ls.URL_SCHEME.length)) : null; -lt.registerSaveRouter(A0); -lt.registerLoadRouter(A0); -function Nz(r) { - return new Ls(r); +Bs.URL_SCHEME = "localstorage://"; +var P0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Bs.URL_SCHEME) ? Hz(r.slice(Bs.URL_SCHEME.length)) : null; +mt.registerSaveRouter(P0); +mt.registerLoadRouter(P0); +function Hz(r) { + return new Bs(r); } -var Wm = class { +var Um = class { constructor() { - E(O().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), E(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; + $(O().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; } async listModels() { - let e = {}, t6 = Op + Ta, o = Ta + _0; + let e = {}, t10 = Pp + $a, o = $a + D0; for (let n = 0; n < this.LS.length; ++n) { let s = this.LS.key(n); - if (s.startsWith(t6) && s.endsWith(o)) { - let a = vz(s); + if (s.startsWith(t10) && s.endsWith(o)) { + let a = Uz(s); e[a] = JSON.parse(this.LS.getItem(s)); } } return e; } async removeModel(e) { - e = kz(e); - let t6 = E0(e); - if (this.LS.getItem(t6.info) == null) + e = Gz(e); + let t10 = F0(e); + if (this.LS.getItem(t10.info) == null) throw new Error(`Cannot find model at path '${e}'`); - let o = JSON.parse(this.LS.getItem(t6.info)); - return $0(t6), o; + let o = JSON.parse(this.LS.getItem(t10.info)); + return O0(t10), o; } }; -var Pp = "://"; +var Mp = "://"; var Xt = class { constructor() { this.managers = {}; @@ -5141,84 +5108,84 @@ var Xt = class { static getInstance() { return Xt.instance == null && (Xt.instance = new Xt()), Xt.instance; } - static registerManager(e, t6) { - E(e != null, () => "scheme must not be undefined or null."), e.endsWith(Pp) && (e = e.slice(0, e.indexOf(Pp))), E(e.length > 0, () => "scheme must not be an empty string."); + static registerManager(e, t10) { + $(e != null, () => "scheme must not be undefined or null."), e.endsWith(Mp) && (e = e.slice(0, e.indexOf(Mp))), $(e.length > 0, () => "scheme must not be an empty string."); let o = Xt.getInstance(); - E(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t6; + $(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t10; } static getManager(e) { - let t6 = Xt.getInstance().managers[e]; - if (t6 == null) + let t10 = Xt.getInstance().managers[e]; + if (t10 == null) throw new Error(`Cannot find model manager for scheme '${e}'`); - return t6; + return t10; } static getSchemes() { return Object.keys(Xt.getInstance().managers); } }; -function Um(r) { - if (r.indexOf(Pp) === -1) +function Gm(r) { + if (r.indexOf(Mp) === -1) throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Xt.getSchemes().join(",")}`); - return { scheme: r.split(Pp)[0], path: r.split(Pp)[1] }; + return { scheme: r.split(Mp)[0], path: r.split(Mp)[1] }; } -async function R0(r, e, t6 = false) { - E(r !== e, () => `Old path and new path are the same: '${r}'`); - let o = lt.getLoadHandlers(r); - E(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), E(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`); - let n = o[0], s = lt.getSaveHandlers(e); - E(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${e}.`), E(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`); - let a = s[0], i = Um(r).scheme, p = Um(r).path, u = i === Um(r).scheme, c = await n.load(); - t6 && u && await Xt.getManager(i).removeModel(p); +async function M0(r, e, t10 = false) { + $(r !== e, () => `Old path and new path are the same: '${r}'`); + let o = mt.getLoadHandlers(r); + $(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`); + let n = o[0], s = mt.getSaveHandlers(e); + $(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${e}.`), $(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`); + let a = s[0], i = Gm(r).scheme, p = Gm(r).path, u = i === Gm(r).scheme, c = await n.load(); + t10 && u && await Xt.getManager(i).removeModel(p); let l = await a.save(c); - return t6 && !u && await Xt.getManager(i).removeModel(p), l.modelArtifactsInfo; + return t10 && !u && await Xt.getManager(i).removeModel(p), l.modelArtifactsInfo; } -async function F0() { +async function L0() { let r = Xt.getSchemes(), e = {}; - for (let t6 of r) { - let o = await Xt.getManager(t6).listModels(); + for (let t10 of r) { + let o = await Xt.getManager(t10).listModels(); for (let n in o) { - let s = t6 + Pp + n; + let s = t10 + Mp + n; e[s] = o[n]; } } return e; } -async function D0(r) { - let e = Um(r); +async function B0(r) { + let e = Gm(r); return Xt.getManager(e.scheme).removeModel(e.path); } -async function O0(r, e) { - return R0(r, e, false); +async function V0(r, e) { + return M0(r, e, false); } -async function P0(r, e) { - return R0(r, e, true); +async function z0(r, e) { + return M0(r, e, true); } -var Hb = class { +var tC = class { constructor() { this.messageName = "setTimeoutCustom", this.functionRefs = [], this.handledMessageCount = 0, this.hasEventListener = false; } - fetch(e, t6) { - return fetch(e, t6); + fetch(e, t10) { + return fetch(e, t10); } now() { return performance.now(); } - encode(e, t6) { - if (t6 !== "utf-8" && t6 !== "utf8") - throw new Error(`Browser's encoder only supports utf-8, but got ${t6}`); + encode(e, t10) { + if (t10 !== "utf-8" && t10 !== "utf8") + throw new Error(`Browser's encoder only supports utf-8, but got ${t10}`); return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e); } - decode(e, t6) { - return new TextDecoder(t6).decode(e); + decode(e, t10) { + return new TextDecoder(t10).decode(e); } - setTimeoutCustom(e, t6) { + setTimeoutCustom(e, t10) { if (typeof window == "undefined" || !O().getBool("USE_SETTIMEOUTCUSTOM")) { - setTimeout(e, t6); + setTimeout(e, t10); return; } this.functionRefs.push(e), setTimeout(() => { window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*"); - }, t6), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => { + }, t10), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => { if (o.source === window && o.data.name === this.messageName) { o.stopPropagation(); let n = this.functionRefs[o.data.index]; @@ -5226,1102 +5193,261 @@ var Hb = class { } }, true)); } + isTypedArray(e) { + return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray; + } }; if (O().get("IS_BROWSER")) { - O().setPlatform("browser", new Hb()); + O().setPlatform("browser", new tC()); + try { + Xt.registerManager(Bs.URL_SCHEME, new Um()); + } catch (r) { + } try { Xt.registerManager(Ls.URL_SCHEME, new Wm()); } catch (r) { } - try { - Xt.registerManager(Ms.URL_SCHEME, new zm()); - } catch (r) { - } } -var Tz = { importFetch: () => M0() }; -var qb; -var Kb = class { +var Kz = { importFetch: () => W0() }; +var rC; +var oC = class { constructor() { - this.util = L0(), this.textEncoder = new this.util.TextEncoder(); + this.util = U0(), this.textEncoder = new this.util.TextEncoder(); } - fetch(e, t6) { - return O().global.fetch != null ? O().global.fetch(e, t6) : (qb == null && (qb = Tz.importFetch()), qb(e, t6)); + fetch(e, t10) { + return O().global.fetch != null ? O().global.fetch(e, t10) : (rC == null && (rC = Kz.importFetch()), rC(e, t10)); } now() { let e = process.hrtime(); return e[0] * 1e3 + e[1] / 1e6; } - encode(e, t6) { - if (t6 !== "utf-8" && t6 !== "utf8") - throw new Error(`Node built-in encoder only supports utf-8, but got ${t6}`); + encode(e, t10) { + if (t10 !== "utf-8" && t10 !== "utf8") + throw new Error(`Node built-in encoder only supports utf-8, but got ${t10}`); return this.textEncoder.encode(e); } - decode(e, t6) { - return e.length === 0 ? "" : new this.util.TextDecoder(t6).decode(e); + decode(e, t10) { + return e.length === 0 ? "" : new this.util.TextDecoder(t10).decode(e); + } + isTypedArray(e) { + return this.util.types.isFloat32Array(e) || this.util.types.isInt32Array(e) || this.util.types.isUint8Array(e) || this.util.types.isUint8ClampedArray(e); } }; -O().get("IS_NODE") && !O().get("IS_BROWSER") && O().setPlatform("node", new Kb()); -function le(r, e = "float32", t6) { - return e = e || "float32", yt(r), new st(r, e, t6); +O().get("IS_NODE") && !O().get("IS_BROWSER") && O().setPlatform("node", new oC()); +function le(r, e = "float32", t10) { + return e = e || "float32", bt(r), new st(r, e, t10); } -function _z(r, e) { - let t6 = v(r, "x", "cast"); - if (!db(e)) +function qz(r, e) { + let t10 = v(r, "x", "cast"); + if (!Ib(e)) throw new Error(`Failed to cast to unknown dtype ${e}`); - if (e === "string" && t6.dtype !== "string" || e !== "string" && t6.dtype === "string") + if (e === "string" && t10.dtype !== "string" || e !== "string" && t10.dtype === "string") throw new Error("Only strings can be casted to strings"); - let o = { x: t6 }, n = { dtype: e }; + let o = { x: t10 }, n = { dtype: e }; return T.runKernel(co, o, n); } -var Ke = N({ cast_: _z }); -function Ez(r) { - let t6 = { x: v(r, "x", "clone", "string_or_numeric") }; - return T.runKernel(mo, t6); +var je = N({ cast_: qz }); +function jz(r) { + let t10 = { x: v(r, "x", "clone", "string_or_numeric") }; + return T.runKernel(mo, t10); } -var Br = N({ clone_: Ez }); -function Gm(r, e = false) { +var Lr = N({ clone_: jz }); +function Hm(r, e = false) { console.log(r.toString(e)); } -Ob(); -var $z = { buffer: le, cast: Ke, clone: Br, print: Gm }; -m0($z); -var Ea = {}; -Ue(Ea, { browserFiles: () => V0, browserHTTPRequest: () => U0, concatenateArrayBuffers: () => Fp, copyModel: () => O0, decodeWeights: () => Lm, encodeWeights: () => b0, fromMemory: () => G0, fromMemorySync: () => Jb, getLoadHandlers: () => k0, getModelArtifactsForJSON: () => Dp, getModelArtifactsForJSONSync: () => zb, getModelArtifactsInfoForJSON: () => Ps, getSaveHandlers: () => v0, getWeightSpecs: () => Vm, http: () => qm, isHTTPScheme: () => Hm, listModels: () => F0, loadWeights: () => z0, moveModel: () => P0, registerLoadRouter: () => I0, registerSaveRouter: () => w0, removeModel: () => D0, weightsLoaderFactory: () => Qb, withSaveHandler: () => H0, withSaveHandlerSync: () => q0 }); -var Az = "model"; -var Rz = ".json"; -var Fz = ".weights.bin"; -function B0(r) { - return new Promise((e) => setTimeout(e)).then(r); -} -var _a = class { - constructor(e) { - if (!O().getBool("IS_BROWSER")) - throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); - e.startsWith(_a.URL_SCHEME) && (e = e.slice(_a.URL_SCHEME.length)), (e == null || e.length === 0) && (e = Az), this.modelJsonFileName = e + Rz, this.weightDataFileName = e + Fz; - } - async save(e) { - if (typeof document == "undefined") - throw new Error("Browser downloads are not supported in this environment since `document` is not present"); - let t6 = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" })); - if (e.modelTopology instanceof ArrayBuffer) - throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); - { - let o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = Bm(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; - if (a.download = this.modelJsonFileName, a.href = s, await B0(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { - let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; - i.download = this.weightDataFileName, i.href = t6, await B0(() => i.dispatchEvent(new MouseEvent("click"))); - } - return { modelArtifactsInfo: Ps(e) }; - } - } -}; -_a.URL_SCHEME = "downloads://"; -var jb = class { - constructor(e) { - if (e == null || e.length < 1) - throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`); - this.jsonFile = e[0], this.weightsFiles = e.slice(1); - } - async load() { - return new Promise((e, t6) => { - let o = new FileReader(); - o.onload = (n) => { - let s = JSON.parse(n.target.result), a = s.modelTopology; - if (a == null) { - t6(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); - return; - } - if (s.weightsManifest == null) { - t6(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); - return; - } - if (this.weightsFiles.length === 0) { - e({ modelTopology: a }); - return; - } - let p = Dp(s, (u) => this.loadWeights(u)); - e(p); - }, o.onerror = (n) => t6(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile); - }); - } - loadWeights(e) { - let t6 = [], o = []; - for (let a of e) - t6.push(...a.weights), o.push(...a.paths); - let n = this.checkManifestAndWeightFiles(e), s = o.map((a) => this.loadWeightsFile(a, n[a])); - return Promise.all(s).then((a) => [t6, Fp(a)]); - } - loadWeightsFile(e, t6) { - return new Promise((o, n) => { - let s = new FileReader(); - s.onload = (a) => { - let i = a.target.result; - o(i); - }, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t6); - }); - } - checkManifestAndWeightFiles(e) { - let t6 = [], o = this.weightsFiles.map((s) => Vb(s.name)), n = {}; - for (let s of e) - s.paths.forEach((a) => { - let i = Vb(a); - if (t6.indexOf(i) !== -1) - throw new Error(`Duplicate file basename found in weights manifest: '${i}'`); - if (t6.push(i), o.indexOf(i) === -1) - throw new Error(`Weight file with basename '${i}' is not provided.`); - n[a] = this.weightsFiles[o.indexOf(i)]; - }); - if (t6.length !== this.weightsFiles.length) - throw new Error(`Mismatch in the number of files in weights manifest (${t6.length}) and the number of weight files provided (${this.weightsFiles.length}).`); - return n; - } -}; -var Dz = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(_a.URL_SCHEME) ? Oz(r.slice(_a.URL_SCHEME.length)) : null; -lt.registerSaveRouter(Dz); -function Oz(r = "model") { - return new _a(r); -} -function V0(r) { - return new jb(r); -} -function Xb(r, e, t6, o) { - a(r), t6 = t6 == null ? 0 : t6, o = o == null ? 1 : o, i(t6, o); - let n = 0, s = (p) => (p.then((u) => { - let c = t6 + ++n / r.length * (o - t6); - return e(c), u; - }), p); - function a(p) { - E(p != null && Array.isArray(p) && p.length > 0, () => "promises must be a none empty array"); - } - function i(p, u) { - E(p >= 0 && p <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${p}`), E(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${u}`), E(u >= p, () => `startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`); - } - return Promise.all(r.map(s)); -} -async function Yb(r, e) { - e == null && (e = {}); - let t6 = e.fetchFunc == null ? O().platform.fetch : e.fetchFunc, o = r.map((l) => t6(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await Xb(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; - return e.onProgress == null ? await Promise.all(i) : await Xb(i, e.onProgress, p, u); -} -async function z0(r, e = "", t6, o) { - return Qb((a) => Yb(a, { requestInit: o }))(r, e, t6); -} -function Qb(r) { - return async (e, t6 = "", o) => { - let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = []; - if (e.forEach((d, f) => { - let h = 0; - d.weights.forEach((g) => { - let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Zc[x] * ze(g.shape), C = () => { - n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b }); - }; - o != null ? o.forEach((w, k) => { - w === g.name && (C(), a[k] = true); - }) : C(), i.push(g.name), h += b; - }); - }), !a.every((d) => d)) { - let d = o.filter((f, h) => !a[h]); - throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}. -Manifest JSON has weights with names: ${i.join(", ")}.`); - } - let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = []; - p.forEach((d) => { - e[d].paths.forEach((f) => { - let h = t6 + (t6.endsWith("/") ? "" : "/") + f; - u.push(h); - }); - }); - let c = await r(u), l = {}, m = 0; - return p.forEach((d) => { - let f = e[d].paths.length, h = 0; - for (let w = 0; w < f; w++) - h += c[m + w].byteLength; - let g = new ArrayBuffer(h), x = new Uint8Array(g), b = 0; - for (let w = 0; w < f; w++) { - let k = new Uint8Array(c[m + w]); - x.set(k, b), b += k.byteLength; - } - s[d].forEach((w) => { - let k = g.slice(w.groupOffset, w.groupOffset + w.sizeBytes), _ = Lm(k, [w.manifestEntry]); - for (let $ in _) - l[$] = _[$]; - }), m += f; - }), l; - }; -} -var Pz = "application/octet-stream"; -var Mz = "application/json"; -var Jc = class { - constructor(e, t6) { - if (this.DEFAULT_METHOD = "POST", t6 == null && (t6 = {}), this.weightPathPrefix = t6.weightPathPrefix, this.onProgress = t6.onProgress, this.weightUrlConverter = t6.weightUrlConverter, t6.fetchFunc != null ? (E(typeof t6.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t6.fetchFunc) : this.fetch = O().platform.fetch, E(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && E(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t6.requestInit != null && t6.requestInit.body != null) - throw new Error("requestInit is expected to have no pre-existing body, but has one."); - this.requestInit = t6.requestInit || {}; - } - async save(e) { - if (e.modelTopology instanceof ArrayBuffer) - throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); - let t6 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); - t6.body = new FormData(); - let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = Bm(e, o); - t6.body.append("model.json", new Blob([JSON.stringify(n)], { type: Mz }), "model.json"), e.weightData != null && t6.body.append("model.weights.bin", new Blob([e.weightData], { type: Pz }), "model.weights.bin"); - let s = await this.fetch(this.path, t6); - if (s.ok) - return { modelArtifactsInfo: Ps(e), responses: [s] }; - throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`); - } - async load() { - let e = await this.fetch(this.path, this.requestInit); - if (!e.ok) - throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`); - let t6; - try { - t6 = await e.json(); - } catch (s) { - let a = `Failed to parse model JSON of response from ${this.path}.`; - throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a); - } - let o = t6.modelTopology, n = t6.weightsManifest; - if (o == null && n == null) - throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); - return Dp(t6, (s) => this.loadWeights(s)); - } - async loadWeights(e) { - let t6 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = Lz(t6), s = this.weightPathPrefix || o, a = Vm(e), i = [], p = []; - for (let c of e) - for (let l of c.paths) - this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n); - this.weightUrlConverter && i.push(...await Promise.all(p)); - let u = await Yb(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress }); - return [a, Fp(u)]; - } -}; -Jc.URL_SCHEME_REGEX = /^https?:\/\//; -function Lz(r) { - let e = r.lastIndexOf("/"), t6 = r.lastIndexOf("?"), o = r.substring(0, e), n = t6 > e ? r.substring(t6) : ""; - return [o + "/", n]; -} -function Hm(r) { - return r.match(Jc.URL_SCHEME_REGEX) != null; -} -var W0 = (r, e) => { - if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null)) - return null; - { - let t6 = true; - if (Array.isArray(r) ? t6 = r.every((o) => Hm(o)) : t6 = Hm(r), t6) - return qm(r, e); - } - return null; -}; -lt.registerSaveRouter(W0); -lt.registerLoadRouter(W0); -function qm(r, e) { - return new Jc(r, e); -} -function U0(r, e) { - return qm(r, e); -} -var el = class { - constructor(e) { - this.modelArtifacts = e; - } - load() { - return this.modelArtifacts; - } -}; -var Km = class { - constructor(e) { - this.saveHandler = e; - } - save(e) { - return this.saveHandler(e); - } -}; -var Zb = class { - constructor(e) { - e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t6) => Promise.resolve(e.save(t6))); - } -}; -function G0(r, e, t6, o) { - let n = arguments; - return new Zb(Jb(...n)); -} -function Jb(r, e, t6, o) { - return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new el(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new el({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. 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Ee(() => { - let a = $a(o), i = Si(o); - return a = T.runKernel(ro, { x: a }, s), i = T.runKernel(ro, { x: i }, s), t6 && (i = yr(i)), Tr(a, i); - }) : T.runKernel(ro, n, s); -} -var Mp = N({ transpose_: Gz }); -function Hz(r, e, t6) { - let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix"); - E(t6 == null || t6 > 0 && Number.isInteger(t6), () => `If provided, numClasses must be a positive integer, but got ${t6}`), E(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), E(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), E(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. 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[r.videoWidth, r.videoHeight] : [r.width, r.height], l; - if (a) - l = r.getContext("2d").getImageData(0, 0, u, c).data; - else if (o || t6) - l = r.data; - else if (s || n || i) { - if (su == null) - if (typeof document == "undefined") - if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined") - su = new OffscreenCanvas(1, 1).getContext("2d"); - else - throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); - else - su = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); - su.canvas.width = u, su.canvas.height = c, su.drawImage(r, 0, 0, u, c), l = su.getImageData(0, 0, u, c).data; - } - let m; - if (e === 4) - m = new Int32Array(l); - else { - let f = u * c; - m = new Int32Array(f * e); - for (let h = 0; h < f; h++) - for (let g = 0; g < e; ++g) - m[h * e + g] = l[h * 4 + g]; - } - return Xm(m, [c, u, e], "int32"); -} -function qz(r) { - return r != null && r.data instanceof Uint8Array; -} -function Kz() { - return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap"); -} -function jz(r) { - return r != null && r.width !== 0 && r.height !== 0; -} -function Xz(r) { - return Kz() && !(r instanceof ImageBitmap) && jz(r) && !qz(r); -} -async function Yz(r, e = 3) { - let t6 = null; - if (O().getBool("WRAP_TO_IMAGEBITMAP") && Xz(r)) { - let o; - try { - o = await createImageBitmap(r, { premultiplyAlpha: "none" }); - } catch (n) { - o = null; - } - o != null && o.width === r.width && o.height === r.height ? t6 = o : t6 = r; - } else - t6 = r; - return Y0(t6, e); -} -async function Qz(r, e) { - let t6 = v(r, "img", "toPixels"); - if (!(r instanceof it)) { - let u = t6; - t6 = Ke(u, "int32"), u.dispose(); - } - if (t6.rank !== 2 && t6.rank !== 3) - throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${t6.rank}.`); - let [o, n] = t6.shape.slice(0, 2), s = t6.rank === 2 ? 1 : t6.shape[2]; - if (s > 4 || s === 2) - throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`); - if (t6.dtype !== "float32" && t6.dtype !== "int32") - throw new Error(`Unsupported type for toPixels: ${t6.dtype}. Please use float32 or int32 tensors.`); - let a = await t6.data(), i = t6.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4); - for (let u = 0; u < o * n; ++u) { - let c = [0, 0, 0, 255]; - for (let m = 0; m < s; m++) { - let d = a[u * s + m]; - if (t6.dtype === "float32") { - if (d < 0 || d > 1) - throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`); - } else if (t6.dtype === "int32" && (d < 0 || d > 255)) - throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`); - s === 1 ? (c[0] = d * i, c[1] = d * i, c[2] = d * i) : c[m] = d * i; - } - let l = u * 4; - p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]); - } - if (e != null) { - e.width = n, e.height = o; - let u = e.getContext("2d"), c = new ImageData(p, n, o); - u.putImageData(c, 0, 0); - } - return t6 !== r && t6.dispose(), p; -} -var Zz = N({ fromPixels_: Y0 }); -var Ym = {}; -Ue(Ym, { prepareAndValidate: () => Z0 }); -function Z0(r, e) { - let t6 = r.shape.length, o = e.shape.length; - if (t6 < 1) - throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t6}.`); - if (o < 1) - throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`); - if (e.dtype !== "int32") - throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`); - if (e.shape[o - 1] > t6) - throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t6}`); - if (ze(r.shape) === 0) - throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`); - let n = e.shape, s = n[n.length - 1], a = 1; - for (let l = 0; l < n.length - 1; ++l) - a *= n[l]; - let i = r.shape, p = n.slice(); - p.pop(); - let u = 1; - for (let l = s; l < t6; ++l) - u *= i[l], p.push(i[l]); - let c = [...hs(r.shape).map((l) => l / u), 1].slice(0, s); - return [p, a, u, c]; -} -var rl = {}; -Ue(rl, { calculateShapes: () => J0, validateInput: () => Qm, validateUpdateShape: () => tC }); -function tC(r, e, t6) { - let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t6.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; - if (t6.rank < n) - throw new Error(s + ` update.rank < ${n}. `); - if (r.length < o + (t6.rank - n)) - throw new Error(s + ` Output shape length < ${o + (t6.rank - n)}`); - if (t6.rank !== n + r.length - o) - throw new Error(s + ` update.rank != ${n + r.length - o}`); - for (let a = 0; a < n; ++a) - if (t6.shape[a] !== e.shape[a]) - throw new Error(s + ` updates.shape[${a}] (${t6.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`); - for (let a = 0; a < t6.rank - n; ++a) - if (t6.shape[a + n] !== r[a + o]) - throw new Error(s + ` updates.shape[${a + n}] (${t6.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`); -} -function Qm(r, e, t6) { - if (e.rank < 1) - throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${e.rank}.`); - if (r.rank < 1) - throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`); - if (e.dtype !== "int32") - throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${e.dtype}`); - if (t6.length < 1) - throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t6}`); - if (t6.length === 0) { - if (e.size === 0) - throw new Error(`Indices specified for empty output. indices shape: ${e.shape}`); - if (r.size === 0) - throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`); - } - tC(t6, e, r); -} -function J0(r, e, t6) { - let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t6.length, a = 1; - for (let l = n; l < s; ++l) - a *= t6[l]; - let i = n < 1 ? 1 : n, p = ze(e.shape) / i, u = [...hs(t6.slice(0, n)), 1], c = ze(t6); - return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c }; -} -var ut = {}; -Ue(ut, { assertParamsValid: () => eW, computeFlatOffset: () => sW, computeOutShape: () => rW, getNormalizedAxes: () => oW, isSliceContinous: () => nW, maskToAxes: () => tW, parseSliceParams: () => aW, sliceInfo: () => iW, startForAxis: () => iv, startIndicesWithElidedDims: () => nv, stopForAxis: () => uv, stopIndicesWithElidedDims: () => sv, stridesForAxis: () => av, stridesWithElidedDims: () => tv }); -var rC = -2; -var Jz = -1; -function eW(r, e, t6) { - let o = r.shape.length; - E(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), E(o === t6.length, () => `Error in slice${o}D: Length of size ${t6} must match the rank of the array (${o}).`); - for (let n = 0; n < o; ++n) - E(e[n] + t6[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t6[n]}) would overflow input.shape[${n}] (${r.shape[n]})`); -} -function tW(r) { - let e = [], t6 = 0; - for (; r > 0; ) - r & 1 && e.push(t6), r /= 2, t6++; - return e; -} -function rW(r, e, t6) { - let o = []; - for (let n = 0; n < r.length; n++) - o[n] = Math.ceil((e[n] - r[n]) / t6[n]); - return o; -} -function tv(r, e, t6, o) { - let n = [...r]; - for (let s = n.length; s < o.length; s++) - n.push(1); - for (let s = 0; s < t6; s++) - s === 0 ? 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a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); - let p = o[n]; - return a < 0 && (a += p), a = op(0, a, p - 1), a; -} -function uv(r, e, t6, o, n, s) { - let a = e[n], i = t6[n] || 1; - (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); - let p = o[n]; - return a < 0 && (a += p), i > 0 ? a = op(0, a, p) : a = op(-1, a, p - 1), a; -} -function nW(r, e, t6) { - let o = t6.length; - for (let n = 0; n < t6.length; n++) - if (t6[n] > 1) { - o = n; - break; - } - for (let n = o + 1; n < t6.length; n++) - if (e[n] > 0 || t6[n] !== r[n]) - return false; - return true; -} -function sW(r, e) { - let t6 = r.length > 0 ? r[r.length - 1] : 1; - for (let o = 0; o < r.length - 1; o++) - t6 += r[o] * e[o]; - return t6; -} -function aW(r, e, t6) { - let o, n = r.shape.length; - typeof e == "number" ? o = [e, ...new Array(n - 1).fill(0)] : e.length < n ? o = e.concat(new Array(n - e.length).fill(0)) : o = e.slice(), o.forEach((a) => { - E(a !== -1, () => "slice() does not support negative begin indexing."); - }); - let s; - return t6 == null ? s = new Array(n).fill(-1) : typeof t6 == "number" ? s = [t6, ...new Array(n - 1).fill(-1)] : t6.length < n ? s = t6.concat(new Array(n - t6.length).fill(-1)) : s = t6, s = s.map((a, i) => a >= 0 ? a : (E(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s]; -} -function iW(r, e, t6, o, n, s, a, i, p) { - let u; - if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && (a & a - 1) !== 0) - throw new Error("Multiple ellipses in slice is not allowed."); - let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t6.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - for (let C = 0; C < l.dims; C++) - c && (1 << C & i) !== 0 && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); - c || (l.ellipsisMask |= 1 << l.dims, l.dims++); - let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; - uW(l, m); - let d = true, f = true, h = true, g = [], x = []; - for (let C = 0; C < r.length; ++C) { - if (m.strides[C] === 0) - throw Error(`strides[${C}] must be non-zero`); - let w = !!(m.shrinkAxisMask & 1 << C), k = r[C]; - if (k === -1) { - g.push(w ? 1 : -1); - continue; - } - let _ = [m.beginMask & 1 << C, m.endMask & 1 << C], $ = [m.strides[C] > 0 ? 0 : -1, m.strides[C] > 0 ? k : k - 1]; - if (w && m.strides[C] <= 0) - throw Error("only stride 1 allowed on non-range indexing."); - h = h && m.strides[C] === 1; - let A = !!(m.beginMask & 1 << C && m.endMask & 1 << C); - if (m.beginValid && m.endValid) { - if (w) { - let M = m.begin[C] < 0 ? k + m.begin[C] : m.begin[C]; - if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k) - throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`); - } else - m.begin[C] = ev(m.begin[C], 0, m.strides[C], k, _, $), m.end[C] = ev(m.end[C], 1, m.strides[C], k, _, $); - let P = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k; - d = d && P, f = f && (C === 0 && m.strides[C] === 1 || P); - } else - d = d && m.strides[C] === 1 && A, f = f && (C === 0 && m.strides[C] === 1 || A); - let R, D = false; - if (m.beginValid && m.endValid ? (R = m.end[C] - m.begin[C], D = true) : w ? (R = 1, D = true) : A && k >= 0 && (m.strides[C] < 0 ? R = -k : R = k, D = true), D) { - let P; - R === 0 || R < 0 != m.strides[C] < 0 ? P = 0 : P = Math.trunc(R / m.strides[C]) + (R % m.strides[C] !== 0 ? 1 : 0), g.push(P); - } else - g.push(-1); - } - for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) { - let w = m.finalShapeGatherIndices[C]; - w >= 0 ? x.push(g[w]) : w === rC && x.push(1); - } - return { finalShapeSparse: x.filter((C, w) => m.finalShapeGatherIndices[w] !== rC), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides }; -} -function uW(r, e) { - e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0; - let t6 = 0; - e.beginValid = r.begin != null, e.endValid = r.end != null, e.begin = new Array(e.dims), e.end = new Array(e.dims), e.strides = new Array(e.dims), e.finalShapeGatherIndices = [], e.finalShapeGatherIndicesSparse = [], e.inputShapeGatherIndicesSparse = new Array(e.dims); - for (let o = 0; o < r.dims; o++) - if (1 << o & r.ellipsisMask) { - let n = Math.min(e.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, e.dims); - for (; t6 < n; t6++) - e.begin[t6] = 0, e.end[t6] = 0, e.strides[t6] = 1, e.beginMask |= 1 << t6, e.endMask |= 1 << t6, e.finalShapeGatherIndices.push(t6), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t6] = o; - } else if (1 << o & r.newAxisMask) - e.finalShapeGatherIndices.push(rC), e.finalShapeGatherIndicesSparse.push(-1); - else { - if (t6 === e.begin.length) - throw Error(`Index out of range using input dim ${t6}; input has only ${e.dims} dims, ${e.begin.length}.`); - r.begin != null && (e.begin[t6] = r.begin[o]), r.end != null && (e.end[t6] = r.end[o]), e.strides[t6] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t6), r.endMask & 1 << o && (e.endMask |= 1 << t6), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(Jz), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t6) : (e.finalShapeGatherIndices.push(t6), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t6] = o, t6++; - } -} -function ev(r, e, t6, o, n, s) { - if (n[e]) - return t6 > 0 ? s[e] : s[e + 1 & 1]; - { - let a = r < 0 ? o + r : r; - return a < s[0] ? s[0] : a > s[1] ? s[1] : a; - } -} -var pv = {}; -Ue(pv, { Serializable: () => ol, SerializationMap: () => Bs, registerClass: () => Er }); -var ol = class { - getClassName() { - return this.constructor.className; - } - static fromConfig(e, t6) { - return new e(t6); - } -}; -var Bs = class { - constructor() { - this.classNameMap = {}; - } - static getMap() { - return Bs.instance == null && (Bs.instance = new Bs()), Bs.instance; - } - static register(e) { - Bs.getMap().classNameMap[e.className] = [e, e.fromConfig]; - } -}; -function Er(r) { - E(r.className != null, () => "Class being registered does not have the static className property defined."), E(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), E(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Bs.register(r); -} -var dv = {}; -Ue(dv, { TEST_EPSILON_FLOAT16: () => cv, createVideoElement: () => hW, encodeStrings: () => mv, expectArrayBuffersEqual: () => fW, expectArraysClose: () => cW, expectArraysEqual: () => mW, expectNumbersClose: () => lv, expectPromiseToFail: () => lW, expectValuesInRange: () => dW, play: () => gW, testEpsilon: () => Zm }); -var pW = 1e-3; -var cv = 0.1; -function cW(r, e, t6) { - return t6 == null && (t6 = Zm()), oC(r, e, (o, n) => nC(o, n, t6)); -} -function Zm() { - return T.backend.floatPrecision() === 32 ? pW : cv; -} -function oC(r, e, t6) { - let o = true; - if ((Wt(r) || Wt(e)) && (o = false), Wt(r) && Wt(e) && (o = true), o) { - let a = r.constructor.name, i = e.constructor.name; - if (a !== i) - throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`); - } - if (Array.isArray(r) && Array.isArray(e)) { - let a = or(r), i = or(e); - if (!Pr(a, i)) - throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`); - } - let n = Wt(r) ? r : Oo(r), s = Wt(e) ? e : Oo(e); - if (n.length !== s.length) - throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}. -Actual: ${n}. -Expected: ${s}.`); - for (let a = 0; a < s.length; ++a) { - let i = n[a], p = s[a]; - if (!t6(i, p)) - throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}. -Actual: ${n}. -Expected: ${s}.`); - } - typeof expect != "undefined" && expect().nothing(); -} -function lW(r, e) { - r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing(); -} -function mW(r, e) { - let t6 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e; - return Po(r) || Po(r[0]) || Po(e) || Po(e[0]) ? oC(r, t6, (o, n) => o == n) : oC(r, e, (o, n) => nC(o, n, 0)); -} -function lv(r, e, t6) { - if (t6 == null && (t6 = Zm()), !nC(r, e, t6)) - throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`); - typeof expect != "undefined" && expect().nothing(); -} -function nC(r, e, t6) { - return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t6); -} -function dW(r, e, t6) { - for (let o = 0; o < r.length; o++) - if (r[o] < e || r[o] > t6) - throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t6}`); -} -function fW(r, e) { - let t6 = new Float32Array(r), o = new Float32Array(e); - if (t6.length !== o.length) - throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t6.length}`); - for (let n = 0; n < o.length; n++) - if (t6[n] !== o[n]) - throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t6[n]} instead`); -} -function mv(r) { - for (let e = 0; e < r.length; e++) { - let t6 = r[e]; - Array.isArray(t6) ? mv(t6) : r[e] = gi(t6); - } - return r; -} -function hW(r) { - let e = document.createElement("video"); - return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t6) => { - e.addEventListener("loadeddata", (o) => t6(e)), e.load(); - }); -} -async function gW(r) { - await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => { - r.requestVideoFrameCallback(e); - }); -} -var xW = "4.1.0"; -function yW(r, e) { - let t6 = v(r, "a", "add"), o = v(e, "b", "add"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; +function Yz(r, e) { + let t10 = v(r, "a", "add"), o = v(e, "b", "add"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; return T.runKernel(eo, n); } -var xe = N({ add_: yW }); -function bW(r, e) { - let t6 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; - return T.runKernel(sn, n); +var be = N({ add_: Yz }); +function Qz(r, e) { + let t10 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; + return T.runKernel(nn, n); } -var Jm = N({ floorDiv_: bW }); -function CW(r, e) { - let t6 = v(r, "a", "div"), o = v(e, "b", "div"); - if ([t6, o] = Re(t6, o), t6.dtype === "int32" && o.dtype === "int32") - return Jm(t6, o); - let n = { a: t6, b: o }, s = {}; - return T.runKernel(Jo, n, s); +var Km = N({ floorDiv_: Qz }); +function Zz(r, e) { + let t10 = v(r, "a", "div"), o = v(e, "b", "div"); + if ([t10, o] = De(t10, o), t10.dtype === "int32" && o.dtype === "int32") + return Km(t10, o); + let n = { a: t10, b: o }, s = {}; + return T.runKernel(Zo, n, s); } -var Ge = N({ div_: CW }); -function SW(r, e) { - let t6 = v(r, "a", "mul"), o = v(e, "b", "mul"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; +var He = N({ div_: Zz }); +function Jz(r, e) { + let t10 = v(r, "a", "mul"), o = v(e, "b", "mul"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; return T.runKernel(kn, n); } -var ae = N({ mul_: SW }); -function wW(r) { +var ne = N({ mul_: Jz }); +function eW(r) { let e = v(r, "x", "abs"); if (e.dtype === "complex64") { - let t6 = { x: e }; - return T.runKernel(pp, t6); + let t10 = { x: e }; + return T.runKernel(hp, t10); } else { - let t6 = { x: e }; - return T.runKernel(gs, t6); + let t10 = { x: e }; + return T.runKernel(ys, t10); } } -var Yt = N({ abs_: wW }); -function IW(r) { - let t6 = { x: v(r, "x", "acos") }; - return T.runKernel(sa, t6); +var Yt = N({ abs_: eW }); +function tW(r) { + let t10 = { x: v(r, "x", "acos") }; + return T.runKernel(aa, t10); } -var fv = N({ acos_: IW }); -function vW(r) { - let t6 = { x: v(r, "x", "acosh") }; - return T.runKernel(aa, t6); +var G0 = N({ acos_: tW }); +function rW(r) { + let t10 = { x: v(r, "x", "acosh") }; + return T.runKernel(ia, t10); } -var hv = N({ acosh_: vW }); -function kW(r) { - E(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), E(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`); - let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t6 = e[0]; +var H0 = N({ acosh_: rW }); +function oW(r) { + $(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), $(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`); + let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t10 = e[0]; e.forEach((n) => { - if (n.dtype !== t6.dtype) + if (n.dtype !== t10.dtype) throw new Error("All tensors passed to tf.addN() must have the same dtype"); }), e.forEach((n) => { - if (!Pr(n.shape, t6.shape)) + if (!Or(n.shape, t10.shape)) throw new Error("All tensors passed to tf.addN() must have the same shape"); }); let o = e; - return T.runKernel(Mo, o); + return T.runKernel(Po, o); } -var gv = N({ addN_: kW }); -function NW(r, e = null, t6 = false) { - let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t6 }; +var K0 = N({ addN_: oW }); +function nW(r, e = null, t10 = false) { + let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t10 }; + return T.runKernel(Mo, n, s); +} +var q0 = N({ all_: nW }); +function sW(r, e = null, t10 = false) { + let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t10 }; return T.runKernel(Lo, n, s); } -var xv = N({ all_: NW }); -function TW(r, e = null, t6 = false) { - let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t6 }; - return T.runKernel(Bo, n, s); -} -var yv = N({ any_: TW }); -function _W(r, e = 0) { +var j0 = N({ any_: sW }); +function aW(r, e = 0) { let o = { x: v(r, "x", "argMax") }, n = { axis: e }; - return T.runKernel(Vo, o, n); + return T.runKernel(Bo, o, n); } -var bv = N({ argMax_: _W }); -function EW(r, e = 0) { +var X0 = N({ argMax_: aW }); +function iW(r, e = 0) { let o = { x: v(r, "x", "argMin") }, n = { axis: e }; - return T.runKernel(Za, o, n); + return T.runKernel(ei, o, n); } -var Cv = N({ argMin_: EW }); -function $W(r) { - let t6 = { x: v(r, "x", "asin") }; - return T.runKernel(ia, t6); +var Y0 = N({ argMin_: iW }); +function uW(r) { + let t10 = { x: v(r, "x", "asin") }; + return T.runKernel(ua, t10); } -var Sv = N({ asin_: $W }); -function AW(r) { - let t6 = { x: v(r, "x", "asinh") }; - return T.runKernel(ua, t6); +var Q0 = N({ asin_: uW }); +function pW(r) { + let t10 = { x: v(r, "x", "asinh") }; + return T.runKernel(pa, t10); } -var wv = N({ asinh_: AW }); -function RW(r) { - let t6 = { x: v(r, "x", "atan") }; - return T.runKernel(pa, t6); +var Z0 = N({ asinh_: pW }); +function cW(r) { + let t10 = { x: v(r, "x", "atan") }; + return T.runKernel(ca, t10); } -var Iv = N({ atan_: RW }); -function FW(r, e) { - let t6 = v(r, "a", "atan2"), o = v(e, "b", "atan2"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; - return T.runKernel(la, n); +var J0 = N({ atan_: cW }); +function lW(r, e) { + let t10 = v(r, "a", "atan2"), o = v(e, "b", "atan2"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; + return T.runKernel(ma, n); } -var vv = N({ atan2_: FW }); -function DW(r) { - let t6 = { x: v(r, "x", "atanh") }; - return T.runKernel(ca, t6); +var ev = N({ atan2_: lW }); +function mW(r) { + let t10 = { x: v(r, "x", "atanh") }; + return T.runKernel(la, t10); } -var kv = N({ atanh_: DW }); -function OW(r, e, t6, o, n = "NHWC", s) { - let a = r[3], i = [...e, a], p = Tv(n); - return uu(r, i, t6, s, o, null, null, p); +var tv = N({ atanh_: mW }); +function dW(r, e, t10, o, n = "NHWC", s) { + let a = r[3], i = [...e, a], p = ov(n); + return au(r, i, t10, s, o, null, null, p); } -function aC(r, e, t6, o, n, s, a = "channelsLast") { - let [i, p] = ed(e), u; +function aC(r, e, t10, o, n, s, a = "channelsLast") { + let [i, p] = Jc(e), u; if (a === "channelsLast") u = [i, p, r[3], r[3]]; else if (a === "channelsFirst") u = [i, p, r[1], r[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return uu(r, u, t6, o, n, s, false, a); + return au(r, u, t10, o, n, s, false, a); } -function PW(r, e, t6, o, n, s, a = "NDHWC") { +function fW(r, e, t10, o, n, s, a = "NDHWC") { let [i, p, u] = sC(e), c, l; if (a === "NDHWC") l = "channelsLast", c = [i, p, u, r[4], r[4]]; @@ -6329,9 +5455,9 @@ function PW(r, e, t6, o, n, s, a = "NDHWC") { l = "channelsFirst", c = [i, p, u, r[1], r[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return Nv(r, c, t6, o, n, false, l, s); + return rv(r, c, t10, o, n, false, l, s); } -function uu(r, e, t6, o, n, s, a = false, i = "channelsLast") { +function au(r, e, t10, o, n, s, a = false, i = "channelsLast") { let [p, u, c, l] = [-1, -1, -1, -1]; if (i === "channelsLast") [p, u, c, l] = r; @@ -6339,10 +5465,10 @@ function uu(r, e, t6, o, n, s, a = false, i = "channelsLast") { [p, l, u, c] = r; else throw new Error(`Unknown dataFormat ${i}`); - let [m, d, , f] = e, [h, g] = ed(t6), [x, b] = ed(o), C = Lp(m, x), w = Lp(d, b), { padInfo: k, outHeight: _, outWidth: $ } = BW(n, u, c, h, g, C, w, s, i), A = a ? f * l : f, R; - return i === "channelsFirst" ? R = [p, A, _, $] : i === "channelsLast" && (R = [p, _, $, A]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: $, outChannels: A, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: w, dilationHeight: x, dilationWidth: b, inShape: r, outShape: R, filterShape: e }; + let [m, d, , f] = e, [h, g] = Jc(t10), [x, b] = Jc(o), C = Lp(m, x), w = Lp(d, b), { padInfo: k, outHeight: _, outWidth: E } = xW(n, u, c, h, g, C, w, s, i), A = a ? f * l : f, R; + return i === "channelsFirst" ? R = [p, A, _, E] : i === "channelsLast" && (R = [p, _, E, A]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: A, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: w, dilationHeight: x, dilationWidth: b, inShape: r, outShape: R, filterShape: e }; } -function Nv(r, e, t6, o, n, s = false, a = "channelsLast", i) { +function rv(r, e, t10, o, n, s = false, a = "channelsLast", i) { let [p, u, c, l, m] = [-1, -1, -1, -1, -1]; if (a === "channelsLast") [p, u, c, l, m] = r; @@ -6350,24 +5476,26 @@ function Nv(r, e, t6, o, n, s = false, a = "channelsLast", i) { [p, m, u, c, l] = r; else throw new Error(`Unknown dataFormat ${a}`); - let [d, f, h, , g] = e, [x, b, C] = sC(t6), [w, k, _] = sC(o), $ = Lp(d, w), A = Lp(f, k), R = Lp(h, _), { padInfo: D, outDepth: P, outHeight: M, outWidth: L } = VW(n, u, c, l, x, b, C, $, A, R, i), W = s ? g * m : g, V; - return a === "channelsFirst" ? V = [p, W, P, M, L] : a === "channelsLast" && (V = [p, P, M, L, W]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: P, outHeight: M, outWidth: L, outChannels: W, padInfo: D, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: $, effectiveFilterHeight: A, effectiveFilterWidth: R, dilationDepth: w, dilationHeight: k, dilationWidth: _, inShape: r, outShape: V, filterShape: e }; + let [d, f, h, , g] = e, [x, b, C] = sC(t10), [w, k, _] = sC(o), E = Lp(d, w), A = Lp(f, k), R = Lp(h, _), { padInfo: D, outDepth: P, outHeight: M, outWidth: L } = yW(n, u, c, l, x, b, C, E, A, R, i), V = s ? g * m : g, z; + return a === "channelsFirst" ? z = [p, V, P, M, L] : a === "channelsLast" && (z = [p, P, M, L, V]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: P, outHeight: M, outWidth: L, outChannels: V, padInfo: D, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: A, effectiveFilterWidth: R, dilationDepth: w, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e }; } -function MW(r, e, t6, o, n) { - o == null && (o = iC(r, e, t6)); - let s = r[0], a = r[1], i = au((s - e + 2 * o) / t6 + 1, n), p = au((a - e + 2 * o) / t6 + 1, n); +function hW(r, e, t10, o, n) { + o == null && (o = iC(r, e, t10)); + let s = r[0], a = r[1], i = el((s - e + 2 * o) / t10 + 1, n), p = el((a - e + 2 * o) / t10 + 1, n); return [i, p]; } -function LW(r, e, t6, o, n, s) { - n == null && (n = iC(r, e, o)); - let a = r[0], i = r[1], p = r[2], u = au((a - e + 2 * n) / o + 1, s), c = au((i - e + 2 * n) / o + 1, s), l = au((p - e + 2 * n) / o + 1, s); - return [u, c, l, t6]; +function gW(r, e, t10, o, n, s) { + n == null && (n = iC(r, e[0], o[0])); + let a = [0, 0, 0, t10]; + for (let i = 0; i < 3; i++) + r[i] + 2 * n >= e[i] && (a[i] = el((r[i] - e[i] + 2 * n) / o[i] + 1, s)); + return a; } -function iC(r, e, t6, o = 1) { +function iC(r, e, t10, o = 1) { let n = Lp(e, o); - return Math.floor((r[0] * (t6 - 1) - t6 + n) / 2); + return Math.floor((r[0] * (t10 - 1) - t10 + n) / 2); } -function ed(r) { +function Jc(r) { return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r; } function sC(r) { @@ -6376,42 +5504,40 @@ function sC(r) { function Lp(r, e) { return e <= 1 ? r : r + (r - 1) * (e - 1); } -function BW(r, e, t6, o, n, s, a, i, p) { +function xW(r, e, t10, o, n, s, a, i, p) { let u, c, l; if (typeof r == "number") { u = { top: r, bottom: r, left: r, right: r, type: r === 0 ? "VALID" : "NUMBER" }; - let d = MW([e, t6], s, o, r, i); + let d = hW([e, t10], s, o, r, i); c = d[0], l = d[1]; } else if (r === "same") { - c = Math.ceil(e / o), l = Math.ceil(t6 / n); - let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t6), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g; + c = Math.ceil(e / o), l = Math.ceil(t10 / n); + let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t10), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g; u = { top: f, bottom: h, left: g, right: x, type: "SAME" }; } else if (r === "valid") - u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t6 - a + 1) / n); + u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t10 - a + 1) / n); else if (typeof r == "object") { let m = p === "channelsLast" ? r[1][0] : r[2][0], d = p === "channelsLast" ? r[1][1] : r[2][1], f = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1]; - u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = au((e - s + m + d) / o + 1, i), l = au((t6 - a + f + h) / n + 1, i); + u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = el((e - s + m + d) / o + 1, i), l = el((t10 - a + f + h) / n + 1, i); } else throw Error(`Unknown padding parameter: ${r}`); return { padInfo: u, outHeight: c, outWidth: l }; } -function VW(r, e, t6, o, n, s, a, i, p, u, c) { +function yW(r, e, t10, o, n, s, a, i, p, u, c) { let l, m, d, f; - if (typeof r == "number") { + if (r === "valid" && (r = 0), typeof r == "number") { l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" }; - let g = LW([e, t6, o, 1], i, 1, n, r, c); + let g = gW([e, t10, o, 1], [i, p, u], 1, [n, s, a], r, c); m = g[0], d = g[1], f = g[2]; } else if (r === "same") { - m = Math.ceil(e / n), d = Math.ceil(t6 / s), f = Math.ceil(o / a); - let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t6, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, w = Math.floor(g / 2), k = g - w, _ = Math.floor(x / 2), $ = x - _; - l = { top: w, bottom: k, left: _, right: $, front: b, back: C, type: "SAME" }; - } else if (r === "valid") - l = { top: 0, bottom: 0, left: 0, right: 0, front: 0, back: 0, type: "VALID" }, m = Math.ceil((e - i + 1) / n), d = Math.ceil((t6 - p + 1) / s), f = Math.ceil((o - u + 1) / a); - else + m = Math.ceil(e / n), d = Math.ceil(t10 / s), f = Math.ceil(o / a); + let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t10, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, w = Math.floor(g / 2), k = g - w, _ = Math.floor(x / 2), E = x - _; + l = { top: w, bottom: k, left: _, right: E, front: b, back: C, type: "SAME" }; + } else throw Error(`Unknown padding parameter: ${r}`); return { padInfo: l, outDepth: m, outHeight: d, outWidth: f }; } -function au(r, e) { +function el(r, e) { if (!e) return Math.trunc(r); switch (e) { @@ -6425,868 +5551,924 @@ function au(r, e) { throw new Error(`Unknown roundingMode ${e}`); } } -function iu(r) { - let [e, t6, o] = ed(r); - return e === 1 && t6 === 1 && o === 1; +function su(r) { + let [e, t10, o] = Jc(r); + return e === 1 && t10 === 1 && o === 1; } -function lr(r, e) { - return iu(r) || iu(e); +function mr(r, e) { + return su(r) || su(e); } -function Tv(r) { +function Vs(r) { + return Jc(r).every((e) => e > 0); +} +function ov(r) { if (r === "NHWC") return "channelsLast"; if (r === "NCHW") return "channelsFirst"; throw new Error(`Unknown dataFormat ${r}`); } -function Pt(r, e, t6) { - if (t6 != null) { +function Mt(r, e, t10) { + if (t10 != null) { if (typeof e == "string") - throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t6} but got pad ${e}.`); + throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`); if (typeof e == "number") - E(na(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t6} but got pad ${e}.`); + $(sa(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`); else if (typeof e == "object") e.forEach((o) => { o.forEach((n) => { - E(na(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t6} but got pad ${n}.`); + $(sa(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${n}.`); }); }); else throw Error(`Error in ${r}: Unknown padding parameter: ${e}`); } } -function zW(r, e) { +function bW(r, e) { let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e }; - return T.runKernel(Ns, o, n); + return T.runKernel(_s, o, n); } -var z = N({ reshape_: zW }); -function WW(r, e, t6, o, n) { +var W = N({ reshape_: bW }); +function CW(r, e, t10, o, n) { let s = v(r, "x", "avgPool", "float32"), a = 1; - E(lr(t6, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t6} and dilations '${a}'`); + $(mr(t10, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`); let i = s, p = false; - s.rank === 3 && (p = true, i = z(s, [1, s.shape[0], s.shape[1], s.shape[2]])), E(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Pt("avgPool", o, n); - let u = { x: i }, c = { filterSize: e, strides: t6, pad: o, dimRoundingMode: n }, l = T.runKernel(zo, u, c); - return l = Ke(l, s.dtype), p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; + s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Mt("avgPool", o, n); + let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Vo, u, c); + return l = je(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var td = N({ avgPool_: WW }); -function UW(r, e, t6, o, n, s = "NDHWC") { +var qm = N({ avgPool_: CW }); +function SW(r, e, t10, o, n, s = "NDHWC") { let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false; - a.rank === 4 && (p = true, i = z(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), E(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), E(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Pt("avgPool3d", o, n); - let u = { x: i }, c = { filterSize: e, strides: t6, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(ip, u, c); - return l = Ke(l, i.dtype), p ? z(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; + a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof t10 == "number" && t10 > 0 || Array.isArray(t10) && t10[0] > 0 && t10[1] > 0 && t10[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t10}'`), Mt("avgPool3d", o, n); + let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(dp, u, c); + return l = je(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var _v = N({ avgPool3d_: UW }); -function GW(r, e = 0) { - E(r.length >= 1, () => "Pass at least one tensor to concat"); - let t6 = Na(r, "tensors", "concat", "string_or_numeric"); - if (t6[0].dtype === "complex64" && t6.forEach((s) => { +var nv = N({ avgPool3d_: SW }); +function wW(r, e = 0) { + $(r.length >= 1, () => "Pass at least one tensor to concat"); + let t10 = _a(r, "tensors", "concat", "string_or_numeric"); + if (t10[0].dtype === "complex64" && t10.forEach((s) => { if (s.dtype !== "complex64") throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `); - }), t6.length === 1) - return Br(t6[0]); - let o = t6, n = { axis: e }; - return T.runKernel(ys, o, n); + }), t10.length === 1) + return Lr(t10[0]); + let o = t10, n = { axis: e }; + return T.runKernel(Cs, o, n); } -var gt = N({ concat_: GW }); -function HW(r) { - let t6 = { x: v(r, "x", "sigmoid", "float32") }; - return T.runKernel(Un, t6); +var xt = N({ concat_: wW }); +function IW(r, e, t10 = false, o = false) { + let n = v(r, "a", "matMul"), s = v(e, "b", "matMul"); + [n, s] = De(n, s); + let a = { a: n, b: s }, i = { transposeA: t10, transposeB: o }; + return T.runKernel(zo, a, i); } -var zs = N({ sigmoid_: HW }); -function qW(r, e, t6) { +var Xe = N({ matMul_: IW }); +function vW(r) { + let t10 = { x: v(r, "x", "sigmoid", "float32") }; + return T.runKernel(Gn, t10); +} +var Ws = N({ sigmoid_: vW }); +function kW(r, e, t10) { let o = v(r, "x", "slice", "string_or_numeric"); if (o.rank === 0) throw new Error("Slicing scalar is not possible"); - let n = { x: o }, s = { begin: e, size: t6 }; - return T.runKernel(_s, n, s); + let n = { x: o }, s = { begin: e, size: t10 }; + return T.runKernel(Es, n, s); } -var He = N({ slice_: qW }); -function KW(r) { - let t6 = { x: v(r, "x", "tanh", "float32") }; - return T.runKernel(Qn, t6); +var Ke = N({ slice_: kW }); +function NW(r) { + let t10 = { x: v(r, "x", "tanh", "float32") }; + return T.runKernel(Zn, t10); } -var nl = N({ tanh_: KW }); -function jW(r, e, t6, o, n, s) { - let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t6, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = gt([u, l], 1), d = Xe(m, i), f = xe(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = He(f, [0, 0], x), C = He(f, [0, g], x), w = He(f, [0, g * 2], x), k = He(f, [0, g * 3], x), _ = xe(ae(zs(b), nl(C)), ae(c, zs(xe(a, w)))), $ = ae(nl(_), zs(k)); - return [_, $]; +var tl = N({ tanh_: NW }); +function TW(r, e, t10, o, n, s) { + let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t10, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = xt([u, l], 1), d = Xe(m, i), f = be(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Ke(f, [0, 0], x), C = Ke(f, [0, g], x), w = Ke(f, [0, g * 2], x), k = Ke(f, [0, g * 3], x), _ = be(ne(Ws(b), tl(C)), ne(c, Ws(be(a, w)))), E = ne(tl(_), Ws(k)); + return [_, E]; } -var Ev = N({ basicLSTMCell_: jW }); -function XW(r, e, t6) { +var sv = N({ basicLSTMCell_: TW }); +function _W(r, e, t10) { let o = v(r, "x", "batchToSpaceND"), n = e.reduce((i, p) => i * p); - E(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), E(t6.length === e.length, () => `crops.length is ${t6.length} but should be equal to blockShape.length ${e.length}`), E(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`); - let s = { x: o }, a = { blockShape: e, crops: t6 }; - return T.runKernel(xs, s, a); + $(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), $(t10.length === e.length, () => `crops.length is ${t10.length} but should be equal to blockShape.length ${e.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`); + let s = { x: o }, a = { blockShape: e, crops: t10 }; + return T.runKernel(bs, s, a); } -var rd = N({ batchToSpaceND_: XW }); -function $v(r) { +var jm = N({ batchToSpaceND_: _W }); +function av(r) { let e; - return r.rank === 0 || r.rank === 1 ? e = z(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = z(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = z(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e; + return r.rank === 0 || r.rank === 1 ? e = W(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = W(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e; } -function YW(r, e, t6, o, n, s) { +function $W(r, e, t10, o, n, s) { s == null && (s = 1e-3); - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - o != null && (c = v(o, "offset", "batchNorm")), E(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), E(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), E(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let m = { x: $v(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(an, m, d); - return z(f, a.shape); + o != null && (c = v(o, "offset", "batchNorm")), $(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), $(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), $(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + let m = { x: av(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(sn, m, d); + return W(f, a.shape); } -var wi = N({ batchNorm_: YW }); -function QW(r, e, t6, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; +var _i = N({ batchNorm_: $W }); +function EW(r, e, t10, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), E(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), E(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s); } -var Av = N({ batchNorm2d_: QW }); -function ZW(r, e, t6, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; +var iv = N({ batchNorm2d_: EW }); +function AW(r, e, t10, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), E(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), E(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s); } -var Rv = N({ batchNorm3d_: ZW }); -function JW(r, e, t6, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; +var uv = N({ batchNorm3d_: AW }); +function RW(r, e, t10, o, n, s) { + let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), E(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), E(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s); } -var Fv = N({ batchNorm4d_: JW }); -function eU(r, e, t6) { +var pv = N({ batchNorm4d_: RW }); +function DW(r, e, t10) { let o = v(r, "x", "bincount"), n = v(e, "weights", "bincount"); - E(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), E(t6 >= 0, () => `size must be non-negative, but got ${t6}.`), E(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); - let s = { x: o, weights: n }, a = { size: t6 }; - return T.runKernel(Ja, s, a); + $(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); + let s = { x: o, weights: n }, a = { size: t10 }; + return T.runKernel(ti, s, a); } -var od = N({ bincount_: eU }); -function tU(r, e) { - let t6 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32"); - if (t6.rank !== 1) - throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t6.rank}`); +var Xm = N({ bincount_: DW }); +function FW(r, e) { + let t10 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32"); + if (t10.rank !== 1) + throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t10.rank}`); if (o.rank !== 1) throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`); - let n = { s0: t6, s1: o }; - return T.runKernel(up, n); + let n = { s0: t10, s1: o }; + return T.runKernel(fp, n); } -var Dv = N({ broadcastArgs_: tU }); -function rU(r, e) { - let t6 = v(r, "broadcastTo", "x"), o = t6.shape; - if (yt(e), e.length < t6.rank) - throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t6.rank}.`); - if (e.length > t6.rank) { - let u = t6.shape.slice(); +var cv = N({ broadcastArgs_: FW }); +function OW(r, e) { + let t10 = v(r, "broadcastTo", "x"), o = t10.shape; + if (bt(e), e.length < t10.rank) + throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t10.rank}.`); + if (e.length > t10.rank) { + let u = t10.shape.slice(); for (; u.length < e.length; ) u.unshift(1); - t6 = z(t6, u); + t10 = W(t10, u); } - let n = t6.shape, s = Array.from(e); + let n = t10.shape, s = Array.from(e); for (let u = e.length - 1; u >= 0; u--) if (n[u] === e[u]) s[u] = 1; - else if (t6.shape[u] !== 1) + else if (t10.shape[u] !== 1) throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`); if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0) - return Br(t6); - let i = { x: t6 }, p = { reps: s }; + return Lr(t10); + let i = { x: t10 }, p = { reps: s }; return T.runKernel(to, i, p); } -var Ii = N({ broadcastTo_: rU }); -function oU(r) { - let t6 = { x: v(r, "x", "ceil", "float32") }; - return T.runKernel(Uo, t6); +var $i = N({ broadcastTo_: OW }); +function PW(r) { + let t10 = { x: v(r, "x", "ceil", "float32") }; + return T.runKernel(Wo, t10); } -var Ov = N({ ceil_: oU }); -function Ws(r, e, t6) { - yt(r); - let o = { shape: r, value: e, dtype: t6 }; - return T.runKernel(Cs, {}, o); +var lv = N({ ceil_: PW }); +function Us(r, e, t10) { + bt(r); + let o = { shape: r, value: e, dtype: t10 }; + return T.runKernel(ws, {}, o); } -function nU(r, e, t6) { +function MW(r, e, t10) { let o = v(r, "x", "clipByValue"); - if (E(e <= t6, () => `Error in clip: min (${e}) must be less than or equal to max (${t6}).`), e === t6) - return Ws(o.shape, e, o.dtype); - let n = { x: o }, s = { clipValueMin: e, clipValueMax: t6 }; + if ($(e <= t10, () => `Error in clip: min (${e}) must be less than or equal to max (${t10}).`), e === t10) + return Us(o.shape, e, o.dtype); + let n = { x: o }, s = { clipValueMin: e, clipValueMax: t10 }; return T.runKernel(lo, n, s); } -var Pv = N({ clipByValue_: nU }); -function sU(r) { - return gt(r, 0); +var mv = N({ clipByValue_: MW }); +function LW(r) { + return xt(r, 0); } -var Mv = N({ concat1d_: sU }); -function aU(r, e) { - return gt(r, e); +var dv = N({ concat1d_: LW }); +function BW(r, e) { + return xt(r, e); } -var Lv = N({ concat2d_: aU }); -function iU(r, e) { - return gt(r, e); +var fv = N({ concat2d_: BW }); +function VW(r, e) { + return xt(r, e); } -var Bv = N({ concat3d_: iU }); -function uU(r, e) { - return gt(r, e); +var hv = N({ concat3d_: VW }); +function zW(r, e) { + return xt(r, e); } -var Vv = N({ concat4d_: uU }); -function pU(r, e, t6, o, n = "NHWC", s = [1, 1], a) { +var gv = N({ concat4d_: zW }); +function WW(r, e, t10, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "conv2d", "float32"), p = v(e, "filter", "conv2d", "float32"), u = i, c = false; - i.rank === 3 && (c = true, u = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), E(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), E(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Pt("conv2d", o, a); + i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Mt("conv2d", o, a); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; - E(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), E(lr(t6, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t6} and dilations '${s}'`); - let m = { x: u, filter: p }, d = { strides: t6, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Go, m, d); - return c ? z(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; + $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(mr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(Vs(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv2D: Strides should be larger than 0."); + let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Uo, m, d); + return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var vi = N({ conv2d_: pU }); -function cU(r, e, t6, o, n = "NWC", s = 1, a) { +var Ei = N({ conv2d_: WW }); +function UW(r, e, t10, o, n = "NWC", s = 1, a) { let i = v(r, "x", "conv1d"), p = v(e, "filter", "conv1d"), u = i, c = false; - i.rank === 2 && (c = true, u = z(i, [1, i.shape[0], i.shape[1]])), E(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), E(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Pt("conv1d", o, a), E(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), E(lr(t6, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t6} and dilation '${s}'`), E(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); - let l = z(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = z(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = vi(m, l, [1, t6], o, "NHWC", [1, s], a); - return c ? z(g, [g.shape[2], g.shape[3]]) : z(g, [g.shape[0], g.shape[2], g.shape[3]]); + i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Mt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(mr(t10, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t10} and dilation '${s}'`), $(Vs(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); + let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = Ei(m, l, [1, t10], o, "NHWC", [1, s], a); + return c ? W(g, [g.shape[2], g.shape[3]]) : W(g, [g.shape[0], g.shape[2], g.shape[3]]); } -var zv = N({ conv1d_: cU }); -function lU(r, e, t6, o, n, s = "NHWC", a) { - E(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); +var xv = N({ conv1d_: UW }); +function GW(r, e, t10, o, n, s = "NHWC", a) { + $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let i = r, p = e, u = false; - e.rank === 3 && (u = true, p = z(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), E(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), E(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), E(t6.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t6.rank}`); + e.rank === 3 && (u = true, p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(t10.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t10.rank}`); let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1]; - E(c === t6.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t6.shape[2]}.`), E(l === t6.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t6.shape[3]}.`), Pt("conv2dDerInput", n, a); - let m = { dy: p, filter: t6 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(Ho, m, d); - return u ? z(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; + $(c === t10.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t10.shape[2]}.`), $(l === t10.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t10.shape[3]}.`), Mt("conv2dDerInput", n, a); + let m = { dy: p, filter: t10 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(Go, m, d); + return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var nd = N({ conv2DBackpropInput_: lU }); -function mU(r, e, t6, o, n, s) { +var Ym = N({ conv2DBackpropInput_: GW }); +function HW(r, e, t10, o, n, s) { let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose"); - return nd(t6, a, i, o, n, "NHWC", s); + return Ym(t10, a, i, o, n, "NHWC", s); } -var Wv = N({ conv2dTranspose_: mU }); -function dU(r, e, t6, o, n = "NDHWC", s = [1, 1, 1]) { +var yv = N({ conv2dTranspose_: HW }); +function KW(r, e, t10, o, n = "NDHWC", s = [1, 1, 1]) { let a = v(r, "x", "conv3d"), i = v(e, "filter", "conv3d"), p = a, u = false; - a.rank === 4 && (u = true, p = z(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), E(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), E(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), E(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), E(lr(t6, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t6} and dilations '${s}'`), E(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`); - let c = { x: p, filter: i }, l = { strides: t6, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(lp, c, l); - return u ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; + a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(mr(t10, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(Vs(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv3D: Strides should be larger than 0."); + let c = { x: p, filter: i }, l = { strides: t10, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(gp, c, l); + return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var Uv = N({ conv3d_: dU }); -function fU(r, e, t6, o, n) { - E(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); +var bv = N({ conv3d_: KW }); +function qW(r, e, t10, o, n) { + $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let s = r, a = e, i = false; - e.rank === 4 && (i = true, a = z(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]), s = [1, r[0], r[1], r[2], r[3]]); + e.rank === 4 && (i = true, a = W(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]), s = [1, r[0], r[1], r[2], r[3]]); let p = s[4], u = a.shape[4]; - E(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), E(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), E(t6.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t6.rank}`), E(p === t6.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t6.shape[3]}.`), E(u === t6.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t6.shape[4]}.`); - let c = { dy: a, filter: t6 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(mp, c, l); - return i ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; + $(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(t10.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t10.rank}`), $(p === t10.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t10.shape[3]}.`), $(u === t10.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t10.shape[4]}.`); + let c = { dy: a, filter: t10 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(xp, c, l); + return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var Gv = N({ conv3DBackpropInput_: fU }); -function hU(r, e, t6, o, n) { +var Cv = N({ conv3DBackpropInput_: qW }); +function jW(r, e, t10, o, n) { let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose"); - return Gv(t6, s, a, o, n); + return Cv(t10, s, a, o, n); } -var Hv = N({ conv3dTranspose_: hU }); -function gU(r) { - let t6 = { x: v(r, "x", "cos", "float32") }; - return T.runKernel(qo, t6); +var Sv = N({ conv3dTranspose_: jW }); +function XW(r) { + let t10 = { x: v(r, "x", "cos", "float32") }; + return T.runKernel(Ho, t10); } -var qv = N({ cos_: gU }); -function xU(r) { - let t6 = { x: v(r, "x", "cosh", "float32") }; - return T.runKernel(Ko, t6); +var wv = N({ cos_: XW }); +function YW(r) { + let t10 = { x: v(r, "x", "cosh", "float32") }; + return T.runKernel(Ko, t10); } -var Kv = N({ cosh_: xU }); -function yU(r, e = 0, t6 = false, o = false) { - let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t6, reverse: o }; +var Iv = N({ cosh_: YW }); +function QW(r, e = 0, t10 = false, o = false) { + let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t10, reverse: o }; + return T.runKernel(qo, s, a); +} +var vv = N({ cumprod_: QW }); +function ZW(r, e = 0, t10 = false, o = false) { + let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t10, reverse: o }; return T.runKernel(jo, s, a); } -var jv = N({ cumprod_: yU }); -function bU(r, e = 0, t6 = false, o = false) { - let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t6, reverse: o }; - return T.runKernel(Xo, s, a); -} -var Xv = N({ cumsum_: bU }); -function CU(r, e, t6, o = false) { +var kv = N({ cumsum_: ZW }); +function JW(r, e, t10, o = false) { let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount"); - E(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), E(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), E(t6 >= 0, () => `size must be non-negative, but got ${t6}.`), E(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); - let a = { x: n, weights: s }, i = { size: t6, binaryOutput: o }; - return T.runKernel(ti, a, i); + $(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); + let a = { x: n, weights: s }, i = { size: t10, binaryOutput: o }; + return T.runKernel(ni, a, i); } -var Yv = N({ denseBincount_: CU }); -function SU(r, e, t6 = "NHWC") { - let o = v(r, "x", "depthToSpace", "float32"), n = t6 === "NHWC" ? o.shape[1] : o.shape[2], s = t6 === "NHWC" ? o.shape[2] : o.shape[3], a = t6 === "NHWC" ? o.shape[3] : o.shape[1]; - E(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), E(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying +var Nv = N({ denseBincount_: JW }); +function eU(r, e, t10 = "NHWC") { + let o = v(r, "x", "depthToSpace", "float32"), n = t10 === "NHWC" ? o.shape[1] : o.shape[2], s = t10 === "NHWC" ? o.shape[2] : o.shape[3], a = t10 === "NHWC" ? o.shape[3] : o.shape[1]; + $(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), $(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying ${n} and ${e} for depthToSpace with input shape - ${o.shape}`), E(s * e >= 0, () => `Negative dimension size caused by overflow when multiplying + ${o.shape}`), $(s * e >= 0, () => `Negative dimension size caused by overflow when multiplying ${s} and ${e} for depthToSpace with input shape - ${o.shape}`), E(a % (e * e) === 0, () => `Dimension size must be evenly divisible by ${e * e} but is ${a} for depthToSpace with input shape ${o.shape}`); - let i = { x: o }, p = { blockSize: e, dataFormat: t6 }; - return T.runKernel(Qo, i, p); + ${o.shape}`), $(a % (e * e) === 0, () => `Dimension size must be evenly divisible by ${e * e} but is ${a} for depthToSpace with input shape ${o.shape}`); + let i = { x: o }, p = { blockSize: e, dataFormat: t10 }; + return T.runKernel(Yo, i, p); } -var Qv = N({ depthToSpace_: SU }); -function wU(r, e, t6, o, n = "NHWC", s = [1, 1], a) { +var Tv = N({ depthToSpace_: eU }); +function tU(r, e, t10, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(e, "filter", "depthwiseConv2d", "float32"), u = i, c = false; - i.rank === 3 && (c = true, u = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), E(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), E(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`); + i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; - E(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Pt("depthwiseConv2d", o, a); - let m = { x: u, filter: p }, d = { strides: t6, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Zo, m, d); - return c ? z(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; + $(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Mt("depthwiseConv2d", o, a); + let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Qo, m, d); + return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var Bp = N({ depthwiseConv2d_: wU }); -function IU(r) { - let t6 = { x: v(r, "x", "diag") }; - return T.runKernel(hp, t6); +var Bp = N({ depthwiseConv2d_: tU }); +function rU(r) { + let t10 = { x: v(r, "x", "diag") }; + return T.runKernel(si, t10); } -var Zv = N({ diag_: IU }); -function vU(r, e, t6, o, n = [1, 1], s = "NHWC") { +var _v = N({ diag_: rU }); +function oU(r, e, t10, o, n = [1, 1], s = "NHWC") { let a = v(r, "x", "dilation2d"), i = v(e, "filter", "dilation2d"); - E(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), E(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), E(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`); + $(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), $(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), $(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`); let p = a, u = false; - a.rank === 3 && (p = z(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true); - let c = { x: p, filter: i }, l = { strides: t6, pad: o, dilations: n }, m = T.runKernel(gp, c, l); - return u ? z(m, [m.shape[1], m.shape[2], m.shape[3]]) : m; + a.rank === 3 && (p = W(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true), $(p.shape[3] === i.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`); + let c = { x: p, filter: i }, l = { strides: t10, pad: o, dilations: n }, m = T.runKernel(ai, c, l); + return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m; } -var Jv = N({ dilation2d_: vU }); -function kU(r, e) { - let t6 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; - return T.runKernel(tn, n); +var $v = N({ dilation2d_: oU }); +var yr = {}; +Ge(yr, { assertAndGetBroadcastShape: () => Je, getBroadcastDims: () => Ev, getReductionAxes: () => Qm }); +function Ev(r, e) { + let t10 = r.length, o = []; + for (let n = 0; n < t10; n++) { + let s = t10 - 1 - n, a = r[s] || 1; + (e[e.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s); + } + return o; } -var sd = N({ equal_: kU }); -function NU(r, e, t6) { - let o = v(e, "a", "where"), n = v(t6, "b", "where"), s = v(r, "condition", "where", "bool"), a = Je(Je(s.shape, o.shape), n.shape), i = Ii(s, a), p = Ii(o, a), u = Ii(n, a), c = { condition: i, t: p, e: u }; - return T.runKernel(Ts, c); +function Qm(r, e) { + let t10 = []; + for (let o = 0; o < e.length; o++) { + let n = r[r.length - o - 1], s = e.length - o - 1, a = e[s]; + (n == null || n === 1 && a > 1) && t10.unshift(s); + } + return t10; } -var os = N({ where_: NU }); -function TU(r) { - let t6 = { x: v(r, "x", "zerosLike") }; - return T.runKernel(Fs, t6); +function Je(r, e) { + let t10 = [], o = Math.max(r.length, e.length); + for (let n = 0; n < o; n++) { + let s = r[r.length - n - 1]; + s == null && (s = 1); + let a = e[e.length - n - 1]; + if (a == null && (a = 1), s === 1) + t10.unshift(a); + else if (a === 1) + t10.unshift(s); + else if (s !== a) { + let i = `Operands could not be broadcast together with shapes ${r} and ${e}.`; + throw Error(i); + } else + t10.unshift(s); + } + return t10; } -var Ut = N({ zerosLike_: TU }); -function _U(r, e) { - let t6 = v(r, "a", "div"), o = v(e, "b", "div"); - [t6, o] = Re(t6, o); - let n = Ge(t6, o), s = Ut(n), a = sd(o, s); - return os(a, s, n); +function nU(r, e) { + let t10 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; + return T.runKernel(en, n); } -var ek = N({ divNoNan_: _U }); -function EU(r, e) { - let t6 = v(r, "t1", "dot"), o = v(e, "t2", "dot"); - E((t6.rank === 1 || t6.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t6.rank} and ${o.rank}.`); - let n = t6.rank === 1 ? t6.size : t6.shape[1], s = o.rank === 1 ? o.size : o.shape[0]; - if (E(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t6.rank === 1 && o.rank === 1) { - let a = z(t6, [1, -1]), i = z(o, [-1, 1]), p = Xe(a, i); - return z(p, []); - } else if (t6.rank === 1 && o.rank === 2) { - let a = z(t6, [1, -1]), i = z(o, [o.shape[0], o.shape[1]]), p = Xe(a, i); - return z(p, [p.size]); - } else if (t6.rank === 2 && o.rank === 1) { - let a = z(o, [-1, 1]), i = Xe(t6, a); - return z(i, [i.size]); +var Zm = N({ equal_: nU }); +function sU(r, e, t10) { + let o = v(e, "a", "where"), n = v(t10, "b", "where"), s = v(r, "condition", "where", "bool"), a = Je(Je(s.shape, o.shape), n.shape), i = $i(s, a), p = $i(o, a), u = $i(n, a), c = { condition: i, t: p, e: u }; + return T.runKernel($s, c); +} +var ss = N({ where_: sU }); +function aU(r) { + let t10 = { x: v(r, "x", "zerosLike") }; + return T.runKernel(Os, t10); +} +var Ut = N({ zerosLike_: aU }); +function iU(r, e) { + let t10 = v(r, "a", "div"), o = v(e, "b", "div"); + [t10, o] = De(t10, o); + let n = He(t10, o), s = Ut(n), a = Zm(o, s); + return ss(a, s, n); +} +var Av = N({ divNoNan_: iU }); +function uU(r, e) { + let t10 = v(r, "t1", "dot"), o = v(e, "t2", "dot"); + $((t10.rank === 1 || t10.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t10.rank} and ${o.rank}.`); + let n = t10.rank === 1 ? t10.size : t10.shape[1], s = o.rank === 1 ? o.size : o.shape[0]; + if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t10.rank === 1 && o.rank === 1) { + let a = W(t10, [1, -1]), i = W(o, [-1, 1]), p = Xe(a, i); + return W(p, []); + } else if (t10.rank === 1 && o.rank === 2) { + let a = W(t10, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Xe(a, i); + return W(p, [p.size]); + } else if (t10.rank === 2 && o.rank === 1) { + let a = W(o, [-1, 1]), i = Xe(t10, a); + return W(i, [i.size]); } else { - let a = z(o, [o.shape[0], o.shape[1]]); - return Xe(t6, a); + let a = W(o, [o.shape[0], o.shape[1]]); + return Xe(t10, a); } } -var tk = N({ dot_: EU }); -function $U(r, ...e) { - let t6 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r }; - return T.runKernel(ri, t6, o); +var Rv = N({ dot_: uU }); +function pU(r, ...e) { + let t10 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r }; + return T.runKernel(ii, t10, o); } -var rk = N({ einsum_: $U }); -function AU(r) { - let t6 = { x: v(r, "x", "elu", "float32") }; - return T.runKernel(en, t6); +var Dv = N({ einsum_: pU }); +function cU(r) { + let t10 = { x: v(r, "x", "elu", "float32") }; + return T.runKernel(Jo, t10); } -var ad = N({ elu_: AU }); -function RU(r) { +var Jm = N({ elu_: cU }); +function lU(r) { let e = v(r, "x", "erf"); - E(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = Ke(e, "float32")); - let t6 = { x: e }; - return T.runKernel(ma, t6); + $(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = je(e, "float32")); + let t10 = { x: e }; + return T.runKernel(da, t10); } -var ok = N({ erf_: RU }); +var Fv = N({ erf_: lU }); function uC(r, e) { - for (let t6 = 0; t6 < r.length; ++t6) - if (r[r.length - t6 - 1] !== e - 1 - t6) + for (let t10 = 0; t10 < r.length; ++t10) + if (r[r.length - t10 - 1] !== e - 1 - t10) return false; return true; } -function nk(r, e, t6) { +function Ov(r, e, t10) { let o = r.length + e.length, n = [], s = 0, a = 0; for (let i = 0; i < o; i++) - t6.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]); + t10.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]); return n; } -function FU(r, e) { - let t6 = [], o = r.length; +function mU(r, e) { + let t10 = [], o = r.length; for (let s = 0; s < o; s++) - e.indexOf(s) === -1 && t6.push(r[s]); + e.indexOf(s) === -1 && t10.push(r[s]); let n = e.map((s) => r[s]); - return [t6, n]; + return [t10, n]; } -function Aa(r, e) { - let t6 = e.map((o) => 1); - return nk(r, t6, e); +function Ea(r, e) { + let t10 = e.map((o) => 1); + return Ov(r, t10, e); } -function DU(r, e, t6) { - E(uC(e, t6), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t6} input.`); +function dU(r, e, t10) { + $(uC(e, t10), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t10} input.`); } -function OU(r, e) { +function fU(r, e) { if (uC(r, e)) return null; - let t6 = []; + let t10 = []; for (let o = 0; o < e; ++o) - r.indexOf(o) === -1 && t6.push(o); - return r.forEach((o) => t6.push(o)), t6; + r.indexOf(o) === -1 && t10.push(o); + return r.forEach((o) => t10.push(o)), t10; } -function PU(r) { - return r.map((e, t6) => [t6, e]).sort((e, t6) => e[1] - t6[1]).map((e) => e[0]); +function hU(r) { + return r.map((e, t10) => [t10, e]).sort((e, t10) => e[1] - t10[1]).map((e) => e[0]); } -function MU(r, e) { - let t6 = []; +function gU(r, e) { + let t10 = []; for (let o = e - r; o < e; ++o) - t6.push(o); - return t6; + t10.push(o); + return t10; } -function BU(r, e = null, t6 = false) { - let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t6 }; +function yU(r, e = null, t10 = false) { + let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t10 }; return T.runKernel(yn, n, s); } -var Us = N({ max_: BU }); -function VU(r, e = null, t6 = false) { - let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t6 }; +var Gs = N({ max_: yU }); +function bU(r, e = null, t10 = false) { + let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t10 }; return T.runKernel(wn, n, s); } -var sl = N({ min_: VU }); -function zU(r, e) { - let t6 = v(r, "base", "pow"), o = v(e, "exp", "pow"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; +var rl = N({ min_: bU }); +function CU(r, e) { + let t10 = v(r, "base", "pow"), o = v(e, "exp", "pow"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; return T.runKernel(An, n); } -var Ra = N({ pow_: zU }); -function be(r, e) { - if ((Wt(r) && e !== "string" || Array.isArray(r)) && e !== "complex64") +var Aa = N({ pow_: CU }); +function Ce(r, e) { + if ((Bt(r) && e !== "string" || Array.isArray(r)) && e !== "complex64") throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)"); - if (e === "string" && Wt(r) && !(r instanceof Uint8Array)) + if (e === "string" && Bt(r) && !(r instanceof Uint8Array)) throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`."); return xr(r, [], [], e); } -function WU(r) { - let t6 = { x: v(r, "x", "sqrt", "float32") }; - return T.runKernel(Gn, t6); +function SU(r) { + let t10 = { x: v(r, "x", "sqrt", "float32") }; + return T.runKernel(Hn, t10); } -var $r = N({ sqrt_: WU }); -function UU(r) { - let e = v(r, "x", "square"), t6 = {}; - return T.runKernel("Square", { x: e }, t6); +var $r = N({ sqrt_: SU }); +function wU(r) { + let e = v(r, "x", "square"), t10 = {}; + return T.runKernel("Square", { x: e }, t10); } -var Qt = N({ square_: UU }); -function GU(r, e = null, t6 = false) { +var Qt = N({ square_: wU }); +function IU(r, e = null, t10 = false) { let o = v(r, "x", "sum"); - o.dtype === "bool" && (o = Ke(o, "int32")); - let n = { x: o }, s = { axis: e, keepDims: t6 }; - return T.runKernel(Hn, n, s); + o.dtype === "bool" && (o = je(o, "int32")); + let n = { x: o }, s = { axis: e, keepDims: t10 }; + return T.runKernel(Kn, n, s); } -var et = N({ sum_: GU }); -function HU(r, e = "euclidean", t6 = null, o = false) { +var et = N({ sum_: IU }); +function vU(r, e = "euclidean", t10 = null, o = false) { r = v(r, "x", "norm"); - let n = sk(r, e, t6), s = n.shape; + let n = Pv(r, e, t10), s = n.shape; if (o) { - let a = Qa(t6, r.shape); - s = Aa(n.shape, a); + let a = Ja(t10, r.shape); + s = Ea(n.shape, a); } - return z(n, s); + return W(n, s); } -function sk(r, e, t6 = null) { +function Pv(r, e, t10 = null) { if (r.rank === 0) return Yt(r); - if (r.rank !== 1 && t6 === null) - return sk(z(r, [-1]), e, t6); - if (r.rank === 1 || typeof t6 == "number" || Array.isArray(t6) && t6.length === 1) { + if (r.rank !== 1 && t10 === null) + return Pv(W(r, [-1]), e, t10); + if (r.rank === 1 || typeof t10 == "number" || Array.isArray(t10) && t10.length === 1) { if (e === 1) - return et(Yt(r), t6); + return et(Yt(r), t10); if (e === 1 / 0) - return Us(Yt(r), t6); + return Gs(Yt(r), t10); if (e === -1 / 0) - return sl(Yt(r), t6); + return rl(Yt(r), t10); if (e === "euclidean" || e === 2) - return $r(et(Ra(Yt(r), be(2, "int32")), t6)); + return $r(et(Aa(Yt(r), Ce(2, "int32")), t10)); throw new Error(`Error in norm: invalid ord value: ${e}`); } - if (Array.isArray(t6) && t6.length === 2) { + if (Array.isArray(t10) && t10.length === 2) { if (e === 1) - return Us(et(Yt(r), t6[0]), t6[1] - 1); + return Gs(et(Yt(r), t10[0]), t10[1] - 1); if (e === 1 / 0) - return Us(et(Yt(r), t6[1]), t6[0]); + return Gs(et(Yt(r), t10[1]), t10[0]); if (e === -1 / 0) - return sl(et(Yt(r), t6[1]), t6[0]); + return rl(et(Yt(r), t10[1]), t10[0]); if (e === "fro" || e === "euclidean") - return $r(et(Qt(r), t6)); + return $r(et(Qt(r), t10)); throw new Error(`Error in norm: invalid ord value: ${e}`); } - throw new Error(`Error in norm: invalid axis: ${t6}`); + throw new Error(`Error in norm: invalid axis: ${t10}`); } -var pu = N({ norm_: HU }); -function qU(r, e = null, t6 = false) { - return pu(r, "euclidean", e, t6); +var iu = N({ norm_: vU }); +function kU(r, e = null, t10 = false) { + return iu(r, "euclidean", e, t10); } -var ak = N({ euclideanNorm_: qU }); -function KU(r) { - let t6 = { x: v(r, "x", "exp") }; - return T.runKernel(rn, t6); +var Mv = N({ euclideanNorm_: kU }); +function NU(r) { + let t10 = { x: v(r, "x", "exp") }; + return T.runKernel(tn, t10); } -var Co = N({ exp_: KU }); -function jU(r, e = 0) { - let t6 = v(r, "x", "expandDims", "string_or_numeric"); - E(e <= t6.rank, () => "Axis must be <= rank of the tensor"); - let o = { input: t6 }, n = { dim: e }; - return T.runKernel(bs, o, n); +var So = N({ exp_: NU }); +function TU(r, e = 0) { + let t10 = v(r, "x", "expandDims", "string_or_numeric"); + $(e <= t10.rank, () => "Axis must be <= rank of the tensor"); + let o = { input: t10 }, n = { dim: e }; + return T.runKernel(Ss, o, n); } -var Fa = N({ expandDims_: jU }); -function XU(r) { - let t6 = { x: v(r, "x", "expm1") }; - return T.runKernel(da, t6); +var Ra = N({ expandDims_: TU }); +function _U(r) { + let t10 = { x: v(r, "x", "expm1") }; + return T.runKernel(fa, t10); } -var ik = N({ expm1_: XU }); -function YU(r, e) { - let t6 = v(r, "x", "tile", "string_or_numeric"); - E(t6.rank === e.length, () => `Error in transpose: rank of input ${t6.rank} must match length of reps ${e}.`); - let o = { x: t6 }, n = { reps: e }; +var Lv = N({ expm1_: _U }); +function $U(r, e) { + let t10 = v(r, "x", "tile", "string_or_numeric"); + $(t10.rank === e.length, () => `Error in transpose: rank of input ${t10.rank} must match length of reps ${e}.`); + let o = { x: t10 }, n = { reps: e }; return T.runKernel(to, o, n); } -var ki = N({ tile_: YU }); -function QU(r, e, t6, o = "float32") { +var Ai = N({ tile_: $U }); +function EU(r, e, t10, o = "float32") { e == null && (e = r); let n = le([r, e], o), s = r <= e ? r : e; for (let i = 0; i < s; ++i) n.set(1, i, i); - let a = z(n.toTensor(), [r, e]); - if (t6 == null) + let a = W(n.toTensor(), [r, e]); + if (t10 == null) return a; - if (t6.length === 1) - return ki(Fa(a, 0), [t6[0], 1, 1]); - if (t6.length === 2) - return ki(Fa(Fa(a, 0), 0), [t6[0], t6[1], 1, 1]); - if (t6.length === 3) - return ki(Fa(Fa(Fa(a, 0), 0), 0), [t6[0], t6[1], t6[2], 1, 1]); - throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t6.length}D.`); + if (t10.length === 1) + return Ai(Ra(a, 0), [t10[0], 1, 1]); + if (t10.length === 2) + return Ai(Ra(Ra(a, 0), 0), [t10[0], t10[1], 1, 1]); + if (t10.length === 3) + return Ai(Ra(Ra(Ra(a, 0), 0), 0), [t10[0], t10[1], t10[2], 1, 1]); + throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t10.length}D.`); } -var id = N({ eye_: QU }); -function ZU(r) { - let t6 = { x: v(r, "x", "floor", "float32") }; - return T.runKernel(nn, t6); +var ed = N({ eye_: EU }); +function AU(r) { + let t10 = { x: v(r, "x", "floor", "float32") }; + return T.runKernel(on, t10); } -var ud = N({ floor_: ZU }); -function JU(r, e, t6 = 0, o = 0) { - let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t6, batchDims: o }; - return T.runKernel(Ss, a, i); +var td = N({ floor_: AU }); +function RU(r, e, t10 = 0, o = 0) { + let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t10, batchDims: o }; + return T.runKernel(Is, a, i); } -var pd = N({ gather_: JU }); -function e4(r, e) { - let t6 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; +var rd = N({ gather_: RU }); +function DU(r, e) { + let t10 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; + return T.runKernel(un, n); +} +var uu = N({ greater_: DU }); +function FU(r, e) { + let t10 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; return T.runKernel(pn, n); } -var cu = N({ greater_: e4 }); -function t4(r, e) { - let t6 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; - return T.runKernel(cn, n); +var od = N({ greaterEqual_: FU }); +function OU(r) { + let t10 = { input: v(r, "input", "imag") }; + return T.runKernel(ci, t10); } -var cd = N({ greaterEqual_: t4 }); -function r4(r) { - let t6 = { x: v(r, "x", "isFinite") }; - return T.runKernel(fa, t6); +var Ri = N({ imag_: OU }); +function PU(r) { + let t10 = { x: v(r, "x", "isFinite") }; + return T.runKernel(ha, t10); } -var uk = N({ isFinite_: r4 }); -function o4(r) { - let t6 = { x: v(r, "x", "isInf") }; - return T.runKernel(ha, t6); +var Bv = N({ isFinite_: PU }); +function MU(r) { + let t10 = { x: v(r, "x", "isInf") }; + return T.runKernel(ga, t10); } -var pk = N({ isInf_: o4 }); -function n4(r) { - let t6 = { x: v(r, "x", "isNaN") }; - return T.runKernel(ln, t6); +var Vv = N({ isInf_: MU }); +function LU(r) { + let t10 = { x: v(r, "x", "isNaN") }; + return T.runKernel(cn, t10); } -var ck = N({ isNaN_: n4 }); -function s4(r, e = 0.2) { +var zv = N({ isNaN_: LU }); +function BU(r, e = 0.2) { let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e }; - return T.runKernel(mn, o, n); + return T.runKernel(ln, o, n); } -var ld = N({ leakyRelu_: s4 }); -function a4(r, e) { - let t6 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; +var nd = N({ leakyRelu_: BU }); +function VU(r, e) { + let t10 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; + return T.runKernel(mn, n); +} +var Wv = N({ less_: VU }); +function zU(r, e) { + let t10 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; return T.runKernel(dn, n); } -var lk = N({ less_: a4 }); -function i4(r, e) { - let t6 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; - return T.runKernel(fn, n); -} -var Vp = N({ lessEqual_: i4 }); -function mk(r, e, t6) { - if (t6 <= 0) +var Vp = N({ lessEqual_: zU }); +function Uv(r, e, t10) { + if (t10 <= 0) throw new Error("The number of values should be positive."); - let o = { start: r, stop: e, num: t6 }; - return T.runKernel(xp, {}, o); + let o = { start: r, stop: e, num: t10 }; + return T.runKernel(li, {}, o); } -function u4(r, e = 5, t6 = 1, o = 1, n = 0.5) { +function WU(r, e = 5, t10 = 1, o = 1, n = 0.5) { let s = v(r, "x", "localResponseNormalization"); - E(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got - rank ${s.rank}.`), E(na(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`); + $(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got + rank ${s.rank}.`), $(sa(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`); let a = s, i = false; - s.rank === 3 && (i = true, a = z(s, [1, s.shape[0], s.shape[1], s.shape[2]])); - let p = { x: a }, u = { depthRadius: e, bias: t6, alpha: o, beta: n }, c = T.runKernel(yp, p, u); - return i ? z(c, [c.shape[1], c.shape[2], c.shape[3]]) : c; + s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])); + let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel(mi, p, u); + return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c; } -var dk = N({ localResponseNormalization_: u4 }); -function p4(r) { - let t6 = { x: v(r, "x", "log", "float32") }; - return T.runKernel(hn, t6); +var Gv = N({ localResponseNormalization_: WU }); +function UU(r) { + let t10 = { x: v(r, "x", "log", "float32") }; + return T.runKernel(fn, t10); } -var Da = N({ log_: p4 }); -function c4(r) { - let t6 = { x: v(r, "x", "log1p") }; - return T.runKernel(ga, t6); +var Da = N({ log_: UU }); +function GU(r) { + let t10 = { x: v(r, "x", "log1p") }; + return T.runKernel(xa, t10); } -var md = N({ log1p_: c4 }); -function l4(r) { - return E(fs(r), () => "The f passed in grad(f) must be a function"), (e, t6) => { - let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t6 != null ? v(t6, "dy", "tf.grad") : null; +var sd = N({ log1p_: GU }); +function HU(r) { + return $(gs(r), () => "The f passed in grad(f) must be a function"), (e, t10) => { + let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grad") : null; return T.tidy(() => { let { value: s, grads: a } = T.gradients(() => r(o), [o], n); - return n != null && ht(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), dd(a), a[0]; + return n != null && gt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), ad(a), a[0]; }); }; } -function m4(r) { - return E(fs(r), () => "The f passed in grads(f) must be a function"), (e, t6) => { - E(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); - let o = Na(e, "args", "tf.grads", "string_or_numeric"), n = t6 != null ? v(t6, "dy", "tf.grads") : null; +function KU(r) { + return $(gs(r), () => "The f passed in grads(f) must be a function"), (e, t10) => { + $(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); + let o = _a(e, "args", "tf.grads", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grads") : null; return T.tidy(() => { let { value: s, grads: a } = T.gradients(() => r(...o), o, n); - return n != null && ht(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), dd(a), a; + return n != null && gt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), ad(a), a; }); }; } -function d4(r) { - return E(fs(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t6) => { - E(e instanceof it, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), E(t6 == null || t6 instanceof it, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); - let { grads: o, value: n } = T.gradients(() => r(e), [e], t6); - return dd(o), { grad: o[0], value: n }; +function qU(r) { + return $(gs(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t10) => { + $(e instanceof it, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t10 == null || t10 instanceof it, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); + let { grads: o, value: n } = T.gradients(() => r(e), [e], t10); + return ad(o), { grad: o[0], value: n }; }; } -function f4(r) { - return E(fs(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t6) => { - E(Array.isArray(e) && e.every((n) => n instanceof it), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), E(t6 == null || t6 instanceof it, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); - let o = T.gradients(() => r(...e), e, t6); - return t6 != null && ht(o.value.shape, t6.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), dd(o.grads), o; +function jU(r) { + return $(gs(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t10) => { + $(Array.isArray(e) && e.every((n) => n instanceof it), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t10 == null || t10 instanceof it, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); + let o = T.gradients(() => r(...e), e, t10); + return t10 != null && gt(o.value.shape, t10.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), ad(o.grads), o; }; } function pC(r, e) { - E(fs(r), () => "The f passed in variableGrads(f) must be a function"), E(e == null || Array.isArray(e) && e.every((u) => u instanceof va), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); - let t6 = e != null; - if (!t6) { + $(gs(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof Na), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); + let t10 = e != null; + if (!t10) { e = []; for (let u in T.registeredVariables) e.push(T.registeredVariables[u]); } - let o = t6 ? e.filter((u) => !u.trainable) : null, n = e.length; - e = e.filter((u) => u.trainable), E(e.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`); + let o = t10 ? e.filter((u) => !u.trainable) : null, n = e.length; + e = e.filter((u) => u.trainable), $(e.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`); let s = true, { value: a, grads: i } = T.gradients(r, e, null, s); - E(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), E(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`); + $(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), $(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`); let p = {}; return e.forEach((u, c) => { i[c] != null && (p[u.name] = i[c]); }), o != null && o.forEach((u) => p[u.name] = null), { value: a, grads: p }; } -function Cr(r) { +function br(r) { return T.customGrad(r); } -function dd(r) { - if (r.filter((t6) => t6 == null).length > 0) +function ad(r) { + if (r.filter((t10) => t10 == null).length > 0) throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.`); } -function h4(r) { - let t6 = { x: v(r, "x", "softplus") }; - return T.runKernel(Qi, t6); +function XU(r) { + let t10 = { x: v(r, "x", "neg") }; + return T.runKernel(vs, t10); } -var fd = N({ softplus_: h4 }); -function g4(r) { +var Cr = N({ neg_: XU }); +function YU(r) { + let t10 = { x: v(r, "x", "softplus") }; + return T.runKernel(Ia, t10); +} +var id = N({ softplus_: YU }); +function QU(r) { let e = v(r, "x", "logSigmoid"); - return Cr((o) => ({ value: yr(fd(yr(o))), gradFunc: (a) => ae(a, zs(yr(o))) }))(e); + return br((o) => ({ value: Cr(id(Cr(o))), gradFunc: (a) => ne(a, Ws(Cr(o))) }))(e); } -var fk = N({ logSigmoid_: g4 }); -function x4(r, e) { - let t6 = v(r, "a", "sub"), o = v(e, "b", "sub"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; - return T.runKernel(Xn, n); +var Hv = N({ logSigmoid_: QU }); +function ZU(r, e) { + let t10 = v(r, "a", "sub"), o = v(e, "b", "sub"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; + return T.runKernel(Yn, n); } -var Ne = N({ sub_: x4 }); -function y4(r, e = -1) { - let t6 = v(r, "logits", "logSoftmax"); - if (e === -1 && (e = t6.rank - 1), e !== t6.rank - 1) - throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t6.rank} and axis was ${e}`); - return Cr((n, s) => { - let i = Us(n, e, true), p = Ne(n, i), u = Ne(Ke(p, "float32"), Da(et(Co(p), e, true))); +var ke = N({ sub_: ZU }); +function JU(r, e = -1) { + let t10 = v(r, "logits", "logSoftmax"); + if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1) + throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and axis was ${e}`); + return br((n, s) => { + let i = Gs(n, e, true), p = ke(n, i), u = ke(je(p, "float32"), Da(et(So(p), e, true))); return s([u]), { value: u, gradFunc: (l, m) => { - let [d] = m, f = true, h = Co(d); - return Ne(l, ae(et(l, e, f), h)); + let [d] = m, f = true, h = So(d); + return ke(l, ne(et(l, e, f), h)); } }; - })(t6); + })(t10); } -var hk = N({ logSoftmax_: y4 }); -function b4(r, e = null, t6 = false) { - let o = v(r, "x", "logSumExp"), n = Qa(e, o.shape), s = Us(o, n, true), a = Ne(o, s), i = Co(a), p = et(i, n), u = Da(p), c = xe(z(s, u.shape), u); - if (t6) { - let l = Aa(c.shape, n); - return z(c, l); +var Kv = N({ logSoftmax_: JU }); +function e4(r, e = null, t10 = false) { + let o = v(r, "x", "logSumExp"), n = Ja(e, o.shape), s = Gs(o, n, true), a = ke(o, s), i = So(a), p = et(i, n), u = Da(p), c = be(W(s, u.shape), u); + if (t10) { + let l = Ea(c.shape, n); + return W(c, l); } return c; } -var hd = N({ logSumExp_: b4 }); -function C4(r, e) { - let t6 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool"); - Je(t6.shape, o.shape); - let n = { a: t6, b: o }; - return T.runKernel(gn, n); +var ud = N({ logSumExp_: e4 }); +function t4(r, e) { + let t10 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool"); + Je(t10.shape, o.shape); + let n = { a: t10, b: o }; + return T.runKernel(hn, n); } -var lu = N({ logicalAnd_: C4 }); -function S4(r) { - let t6 = { x: v(r, "x", "logicalNot", "bool") }; - return T.runKernel(xn, t6); +var pu = N({ logicalAnd_: t4 }); +function r4(r) { + let t10 = { x: v(r, "x", "logicalNot", "bool") }; + return T.runKernel(gn, t10); } -var gd = N({ logicalNot_: S4 }); -function w4(r, e) { - let t6 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool"); - Je(t6.shape, o.shape); - let n = { a: t6, b: o }; - return T.runKernel(xa, n); +var pd = N({ logicalNot_: r4 }); +function o4(r, e) { + let t10 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool"); + Je(t10.shape, o.shape); + let n = { a: t10, b: o }; + return T.runKernel(xn, n); } -var xd = N({ logicalOr_: w4 }); -function I4(r, e) { - let t6 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool"); - return Je(t6.shape, o.shape), lu(xd(r, e), gd(lu(r, e))); +var cd = N({ logicalOr_: o4 }); +function n4(r, e) { + let t10 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool"); + return Je(t10.shape, o.shape), pu(cd(r, e), pd(pu(r, e))); } -var gk = N({ logicalXor_: I4 }); -var yd = 2147483648; -function v4(r, e, t6 = "left") { - let o = v(r, "sortedSequence", "searchSorted"), n = v(e, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = z(o, [-1, s]), p = z(n, [-1, a]); +var qv = N({ logicalXor_: n4 }); +var ld = 2147483648; +function s4(r, e, t10 = "left") { + let o = v(r, "sortedSequence", "searchSorted"), n = v(e, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]); if (i.rank < 2) throw new Error("Sorted input argument must be at least 2-dimensional"); if (i.shape[0] !== p.shape[0]) throw new Error("Leading dimension of 'sortedSequence' and 'values' must match."); - if (ze(p.shape) >= yd) - throw new Error(`values tensor size must less than ${yd}`); - if (i.shape[1] >= yd) - throw new Error(`trailing dim_size must less than ${yd} for int32 output type, was ${i.shape[1]}`); - let u = { sortedSequence: i, values: p }, c = { side: t6 }; - return T.runKernel(ii, u, c); + if (We(p.shape) >= ld) + throw new Error(`values tensor size must less than ${ld}`); + if (i.shape[1] >= ld) + throw new Error(`trailing dim_size must less than ${ld} for int32 output type, was ${i.shape[1]}`); + let u = { sortedSequence: i, values: p }, c = { side: t10 }; + return T.runKernel(fi, u, c); } -var al = N({ searchSorted_: v4 }); -function xk(r, e) { - return al(r, e, "left"); +var ol = N({ searchSorted_: s4 }); +function jv(r, e) { + return ol(r, e, "left"); } -function k4(r, e, t6, o, n) { +function a4(r, e, t10, o, n) { let s = v(r, "x", "maxPool"), a = 1, i = s, p = false; - s.rank === 3 && (p = true, i = z(s, [1, s.shape[0], s.shape[1], s.shape[2]])), E(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), E(lr(t6, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t6} and dilations '${a}'`), Pt("maxPool", o, n); - let u = { x: i }, c = { filterSize: e, strides: t6, pad: o, dimRoundingMode: n }, l = T.runKernel(Cn, u, c); - return p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; + s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(mr(t10, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`), Mt("maxPool", o, n); + let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Cn, u, c); + return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var bd = N({ maxPool_: k4 }); -function N4(r, e = [1, 1, 1], t6, o, n, s = "NDHWC") { +var md = N({ maxPool_: a4 }); +function i4(r, e = [1, 1, 1], t10, o, n, s = "NDHWC") { let a = v(r, "x", "maxPool3d"), i = a, p = false; - a.rank === 4 && (p = true, i = z(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), E(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), E(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Pt("maxPool3d", o, n); - let u = { x: i }, c = { filterSize: e, strides: t6, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(bp, u, c); - return p ? z(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; + a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Mt("maxPool3d", o, n); + let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(Cp, u, c); + return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var yk = N({ maxPool3d_: N4 }); -function T4(r, e, t6, o, n = false) { - let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t6, pad: o, includeBatchInIndex: n }, p = T.runKernel(Cp, a, i); +var Xv = N({ maxPool3d_: i4 }); +function u4(r, e, t10, o, n = false) { + let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t10, pad: o, includeBatchInIndex: n }, p = T.runKernel(Sp, a, i); return { result: p[0], indexes: p[1] }; } -var bk = N({ maxPoolWithArgmax_: T4 }); -function _4(r, e) { - let t6 = v(r, "a", "maximum"), o = v(e, "b", "maximum"); - [t6, o] = Re(t6, o), t6.dtype === "bool" && (t6 = Ke(t6, "int32"), o = Ke(o, "int32")), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; +var Yv = N({ maxPoolWithArgmax_: u4 }); +function p4(r, e) { + let t10 = v(r, "a", "maximum"), o = v(e, "b", "maximum"); + [t10, o] = De(t10, o), t10.dtype === "bool" && (t10 = je(t10, "int32"), o = je(o, "int32")), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; return T.runKernel(bn, n); } -var Cd = N({ maximum_: _4 }); -function E4(r, e = null, t6 = false) { - let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t6 }; +var dd = N({ maximum_: p4 }); +function c4(r, e = null, t10 = false) { + let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t10 }; return T.runKernel(Sn, n, s); } -var mu = N({ mean_: E4 }); -function Vr(r, e = "float32") { - if (yt(r), e === "complex64") { - let o = Vr(r, "float32"), n = Vr(r, "float32"); +var cu = N({ mean_: c4 }); +function Br(r, e = "float32") { + if (bt(r), e === "complex64") { + let o = Br(r, "float32"), n = Br(r, "float32"); return Tr(o, n); } - let t6 = ap(ze(r), e); - return T.makeTensor(t6, r, e); + let t10 = lp(We(r), e); + return T.makeTensor(t10, r, e); } -function Gs(r, e = "float32") { - if (yt(r), e === "complex64") { - let o = Gs(r, "float32"), n = Vr(r, "float32"); +function Hs(r, e = "float32") { + if (bt(r), e === "complex64") { + let o = Hs(r, "float32"), n = Br(r, "float32"); return Tr(o, n); } - let t6 = zc(ze(r), e); - return T.makeTensor(t6, r, e); + let t10 = Wc(We(r), e); + return T.makeTensor(t10, r, e); } -function Ck(r, e, { indexing: t6 = "xy" } = {}) { - if (t6 !== "xy" && t6 !== "ij") - throw new TypeError(`${t6} is not a valid third argument to meshgrid`); +function Qv(r, e, { indexing: t10 = "xy" } = {}) { + if (t10 !== "xy" && t10 !== "ij") + throw new TypeError(`${t10} is not a valid third argument to meshgrid`); if (r === void 0) return []; let o = v(r, "x", "meshgrid", r instanceof it ? r.dtype : "float32"); if (e === void 0) return [o]; - let n = v(e, "y", "meshgrid", e instanceof it ? e.dtype : "float32"), s = ze(o.shape), a = ze(n.shape); - return t6 === "xy" ? (o = z(o, [1, -1]), n = z(n, [-1, 1]), [Xe(Gs([a, 1], o.dtype), o), Xe(n, Gs([1, s], n.dtype))]) : (o = z(o, [-1, 1]), n = z(n, [1, -1]), [Xe(o, Gs([1, a], o.dtype)), Xe(Gs([s, 1], n.dtype), n)]); + let n = v(e, "y", "meshgrid", e instanceof it ? e.dtype : "float32"), s = We(o.shape), a = We(n.shape); + return t10 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Xe(Hs([a, 1], o.dtype), o), Xe(n, Hs([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Xe(o, Hs([1, a], o.dtype)), Xe(Hs([s, 1], n.dtype), n)]); } -function $4(r, e) { - let t6 = v(r, "a", "minimum"), o = v(e, "b", "minimum"); - [t6, o] = Re(t6, o), t6.dtype === "bool" && (t6 = Ke(t6, "int32"), o = Ke(o, "int32")), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; +function l4(r, e) { + let t10 = v(r, "a", "minimum"), o = v(e, "b", "minimum"); + [t10, o] = De(t10, o), t10.dtype === "bool" && (t10 = je(t10, "int32"), o = je(o, "int32")), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; return T.runKernel(In, n); } -var Sd = N({ minimum_: $4 }); -function A4(r, e, t6) { - E(t6 === "reflect" || t6 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t6}.`); +var fd = N({ minimum_: l4 }); +function m4(r, e, t10) { + $(t10 === "reflect" || t10 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t10}.`); let o = v(r, "x", "mirrorPad"); if (o.rank === 0) throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad"); - E(e.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`); - let n = t6 === "reflect" ? 1 : 0; + $(e.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`); + let n = t10 === "reflect" ? 1 : 0; for (let i = 0; i < o.rank; i++) - E(e[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), E(e[i][0] >= 0 && e[i][0] <= o.shape[i] - n && e[i][1] >= 0 && e[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`); - let s = { paddings: e, mode: t6 }, a = { x: o }; + $(e[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), $(e[i][0] >= 0 && e[i][0] <= o.shape[i] - n && e[i][1] >= 0 && e[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`); + let s = { paddings: e, mode: t10 }, a = { x: o }; return T.runKernel(vn, a, s); } -var Sk = N({ mirrorPad_: A4 }); -function R4(r, e) { - let t6 = v(r, "a", "mod"), o = v(e, "b", "mod"); - [t6, o] = Re(t6, o); - let n = { a: t6, b: o }; +var Zv = N({ mirrorPad_: m4 }); +function d4(r, e) { + let t10 = v(r, "a", "mod"), o = v(e, "b", "mod"); + [t10, o] = De(t10, o); + let n = { a: t10, b: o }; return T.runKernel(ya, n); } -var wk = N({ mod_: R4 }); -function F4(r, e = null, t6 = false) { +var Jv = N({ mod_: d4 }); +function f4(r, e = null, t10 = false) { r = v(r, "x", "moments"); - let o = Qa(e, r.shape), n = mu(r, o, t6), s = n.shape; - t6 || (s = Aa(n.shape, o)); - let a = Qt(Ne(Ke(r, "float32"), z(n, s))), i = mu(a, o, t6); + let o = Ja(e, r.shape), n = cu(r, o, t10), s = n.shape; + t10 || (s = Ea(n.shape, o)); + let a = Qt(ke(je(r, "float32"), W(n, s))), i = cu(a, o, t10); return { mean: n, variance: i }; } -var Ik = N({ moments_: F4 }); -function D4(r, e, t6, o) { - let n = v(e, "data", "multiRNNCell"), s = Na(t6, "c", "multiRNNCell"), a = Na(o, "h", "multiRNNCell"), i = n, p = []; +var ek = N({ moments_: f4 }); +function h4(r, e, t10, o) { + let n = v(e, "data", "multiRNNCell"), s = _a(t10, "c", "multiRNNCell"), a = _a(o, "h", "multiRNNCell"), i = n, p = []; for (let l = 0; l < r.length; l++) { let m = r[l](i, s[l], a[l]); p.push(m[0]), p.push(m[1]), i = m[1]; @@ -7296,151 +6478,240 @@ function D4(r, e, t6, o) { u.push(p[l]), c.push(p[l + 1]); return [u, c]; } -var vk = N({ multiRNNCell_: D4 }); -function O4(r, e, t6, o = false) { +var tk = N({ multiRNNCell_: h4 }); +function g4(r, e, t10, o = false) { let n = v(r, "logits", "multinomial"), s = n.size, a = n.rank; if (s < 2) throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`); if (a > 2) throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`); - t6 = t6 || Math.random(); - let p = { logits: a === 1 ? z(n, [1, -1]) : n }, u = { numSamples: e, seed: t6, normalized: o }, c = T.runKernel(Sp, p, u); - return a === 1 ? z(c, [c.size]) : c; + t10 = t10 || Math.random(); + let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t10, normalized: o }, c = T.runKernel(wp, p, u); + return a === 1 ? W(c, [c.size]) : c; } -var kk = N({ multinomial_: O4 }); -function P4(r, e) { - let t6 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }; +var rk = N({ multinomial_: g4 }); +function x4(r, e) { + let t10 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }; return T.runKernel(Nn, n); } -var wd = N({ notEqual_: P4 }); -function M4(r) { - let t6 = { x: v(r, "x", "onesLike") }; - return T.runKernel(Is, t6); +var hd = N({ notEqual_: x4 }); +function y4(r, e, t10 = 1, o = 0, n = "int32") { + if (e < 2) + throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`); + let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: e, onValue: t10, offValue: o }; + return T.runKernel($n, a, i); } -var Nk = N({ onesLike_: M4 }); -function L4(r, e) { - let t6 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct"); - E(t6.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t6.rank} and ${o.rank}.`); - let n = z(t6, [-1, 1]), s = z(o, [1, -1]); +var nl = N({ oneHot_: y4 }); +function b4(r) { + let t10 = { x: v(r, "x", "onesLike") }; + return T.runKernel(ks, t10); +} +var ok = N({ onesLike_: b4 }); +function C4(r, e) { + let t10 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct"); + $(t10.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t10.rank} and ${o.rank}.`); + let n = W(t10, [-1, 1]), s = W(o, [1, -1]); return Xe(n, s); } -var Tk = N({ outerProduct_: L4 }); -function B4(r, e, t6 = 0) { +var nk = N({ outerProduct_: C4 }); +function S4(r, e, t10 = 0) { let o = v(r, "x", "pad"); if (o.rank === 0) throw new Error("pad(scalar) is not defined. Pass non-scalar to pad"); - let n = { paddings: e, constantValue: t6 }, s = { x: o }; - return T.runKernel($n, s, n); + let n = { paddings: e, constantValue: t10 }, s = { x: o }; + return T.runKernel(En, s, n); } -var Hs = N({ pad_: B4 }); -function V4(r, e, t6 = 0) { - return E(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Hs(r, [e], t6); +var Ks = N({ pad_: S4 }); +function w4(r, e, t10 = 0) { + return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Ks(r, [e], t10); } -var _k = N({ pad1d_: V4 }); -function z4(r, e, t6 = 0) { - return E(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Hs(r, e, t6); +var sk = N({ pad1d_: w4 }); +function I4(r, e, t10 = 0) { + return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10); } -var Ek = N({ pad2d_: z4 }); -function W4(r, e, t6 = 0) { - return E(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Hs(r, e, t6); +var ak = N({ pad2d_: I4 }); +function v4(r, e, t10 = 0) { + return $(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10); } -var $k = N({ pad3d_: W4 }); -function U4(r, e, t6 = 0) { - return E(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Hs(r, e, t6); +var ik = N({ pad3d_: v4 }); +function k4(r, e, t10 = 0) { + return $(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10); } -var Ak = N({ pad4d_: U4 }); -function G4(r, e, t6) { +var uk = N({ pad4d_: k4 }); +function N4(r, e, t10) { let o = v(r, "x", "spaceToBatchND"); - E(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), E(t6.length === e.length, () => `paddings.shape[0] ${t6.length} must be equal to [blockShape] ${e.length}`), E(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t6[p - 1][0] + t6[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t6.toString()} must be divisible by blockShapes ${e.toString()}`); - let n = { x: o }, s = { blockShape: e, paddings: t6 }; - return T.runKernel(Es, n, s); + $(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t10.length === e.length, () => `paddings.shape[0] ${t10.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t10[p - 1][0] + t10[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t10.toString()} must be divisible by blockShapes ${e.toString()}`); + let n = { x: o }, s = { blockShape: e, paddings: t10 }; + return T.runKernel(As, n, s); } -var Id = N({ spaceToBatchND_: G4 }); -function H4(r, e, t6, o, n, s, a) { +var gd = N({ spaceToBatchND_: N4 }); +function T4(r, e, t10, o, n, s, a) { n == null && (n = [1, 1]), s == null && (s = 1), o === 0 && (o = "valid"); let i = v(r, "x", "maxPool"), p = i, u = false; - i.rank === 3 && (u = true, p = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), E(lr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`); + i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(mr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`); let c = aC(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m; - o === "same" ? m = K4([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]]; - let d = l[0] === 1 && l[1] === 1, [f, h] = q4([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Id(p, l, f), C = (t6 === "avg" ? () => td(x, e, s, g, a) : () => bd(x, e, s, g, a))(), w = d ? C : rd(C, l, h); - return u ? z(w, [w.shape[1], w.shape[2], w.shape[3]]) : w; + o === "same" ? m = $4([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]]; + let d = l[0] === 1 && l[1] === 1, [f, h] = _4([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : gd(p, l, f), C = (t10 === "avg" ? () => qm(x, e, s, g, a) : () => md(x, e, s, g, a))(), w = d ? C : jm(C, l, h); + return u ? W(w, [w.shape[1], w.shape[2], w.shape[3]]) : w; } -function q4(r, e, t6) { - let o = t6.map((c) => c[0]), n = t6.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]); +function _4(r, e, t10) { + let o = t10.map((c) => c[0]), n = t10.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]); return [p, u]; } -function K4(r, e) { +function $4(r, e) { let o = r.map((a, i) => a + (a - 1) * (e[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]); return o.map((a, i) => [n[i], s[i]]); } -var Rk = N({ pool_: H4 }); -function j4(r, e) { - let t6 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t6, alpha: o }; +var pk = N({ pool_: T4 }); +function E4(r, e) { + let t10 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t10, alpha: o }; return T.runKernel(Rn, n); } -var vd = N({ prelu_: j4 }); -function X4(r, e = null, t6 = false) { +var xd = N({ prelu_: E4 }); +function A4(r, e = null, t10 = false) { let o = v(r, "x", "prod"); - o.dtype === "bool" && (o = Ke(o, "int32")); - let n = { x: o }, s = { axis: e, keepDims: t6 }; - return T.runKernel(Fn, n, s); + o.dtype === "bool" && (o = je(o, "int32")); + let n = { x: o }, s = { axis: e, keepDims: t10 }; + return T.runKernel(Dn, n, s); } -var Fk = N({ prod_: X4 }); -function Y4(r, e, t6, o) { - let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t6, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(wp, i, p); +var ck = N({ prod_: A4 }); +function R4(r, e, t10, o) { + let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t10, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Ip, i, p); return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] }; } -var Dk = N({ raggedGather_: Y4 }); -function Q4(r, e, t6) { - let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t6, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(Ip, a); +var lk = N({ raggedGather_: R4 }); +function D4(r, e, t10) { + let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t10, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(vp, a); return { rtNestedSplits: i[0], rtDenseValues: i[1] }; } -var Ok = N({ raggedRange_: Q4 }); -function Z4(r, e, t6, o, n) { - let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t6, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; - return T.runKernel(vp, u, c); +var mk = N({ raggedRange_: D4 }); +function F4(r, e, t10, o, n) { + let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t10, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; + return T.runKernel(kp, u, c); } -var Pk = N({ raggedTensorToTensor_: Z4 }); -function J4(r, e, t6) { - yt(r); - let o = ze(r), n = null; - if (t6 == null || t6 === "float32") +var dk = N({ raggedTensorToTensor_: F4 }); +function O4(r, e, t10) { + bt(r); + let o = We(r), n = null; + if (t10 == null || t10 === "float32") n = new Float32Array(o); - else if (t6 === "int32") + else if (t10 === "int32") n = new Int32Array(o); - else if (t6 === "bool") + else if (t10 === "bool") n = new Uint8Array(o); else - throw new Error(`Unknown data type ${t6}`); + throw new Error(`Unknown data type ${t10}`); for (let s = 0; s < o; s++) n[s] = e(); - return T.makeTensor(n, r, t6); + return T.makeTensor(n, r, t10); } -var Mk = N({ rand_: J4 }); -var _d = rp(gC()); -var fu = class { - constructor(e, t6, o, n, s) { - this.mean = e, this.stdDev = t6, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2); +var fk = N({ rand_: O4 }); +var wd = ap(gC()); +var Fk = {}; +Ge(Fk, { TEST_EPSILON_FLOAT16: () => Ak, createVideoElement: () => j4, encodeStrings: () => Dk, expectArrayBuffersEqual: () => q4, expectArraysClose: () => U4, expectArraysEqual: () => H4, expectNumbersClose: () => Rk, expectPromiseToFail: () => G4, expectValuesInRange: () => K4, play: () => X4, testEpsilon: () => bd }); +var W4 = 1e-3; +var Ak = 0.1; +function U4(r, e, t10) { + return t10 == null && (t10 = bd()), xC(r, e, (o, n) => yC(o, n, t10)); +} +function bd() { + return T.backend.floatPrecision() === 32 ? W4 : Ak; +} +function xC(r, e, t10) { + let o = true; + if ((Bt(r) || Bt(e)) && (o = false), Bt(r) && Bt(e) && (o = true), o) { + let a = r.constructor.name, i = e.constructor.name; + if (a !== i) + throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`); + } + if (Array.isArray(r) && Array.isArray(e)) { + let a = or(r), i = or(e); + if (!Or(a, i)) + throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`); + } + let n = Bt(r) ? r : os(r), s = Bt(e) ? e : os(e); + if (n.length !== s.length) + throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}. +Actual: ${n}. +Expected: ${s}.`); + for (let a = 0; a < s.length; ++a) { + let i = n[a], p = s[a]; + if (!t10(i, p)) + throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}. +Actual: ${n}. +Expected: ${s}.`); + } + typeof expect != "undefined" && expect().nothing(); +} +function G4(r, e) { + r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing(); +} +function H4(r, e) { + let t10 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e; + return Oo(r) || Oo(r[0]) || Oo(e) || Oo(e[0]) ? xC(r, t10, (o, n) => o == n) : xC(r, e, (o, n) => yC(o, n, 0)); +} +function Rk(r, e, t10) { + if (t10 == null && (t10 = bd()), !yC(r, e, t10)) + throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`); + typeof expect != "undefined" && expect().nothing(); +} +function yC(r, e, t10) { + return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t10); +} +function K4(r, e, t10) { + for (let o = 0; o < r.length; o++) + if (r[o] < e || r[o] > t10) + throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t10}`); +} +function q4(r, e) { + let t10 = new Float32Array(r), o = new Float32Array(e); + if (t10.length !== o.length) + throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t10.length}`); + for (let n = 0; n < o.length; n++) + if (t10[n] !== o[n]) + throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t10[n]} instead`); +} +function Dk(r) { + for (let e = 0; e < r.length; e++) { + let t10 = r[e]; + Array.isArray(t10) ? Dk(t10) : r[e] = Ii(t10); + } + return r; +} +function j4(r) { + let e = document.createElement("video"); + return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t10) => { + e.addEventListener("loadeddata", (o) => t10(e)), e.load(); + }); +} +async function X4(r) { + await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => { + r.requestVideoFrameCallback(e); + }); +} +var mu = class { + constructor(e, t10, o, n, s) { + this.mean = e, this.stdDev = t10, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2); let a = s || Math.random(); - this.random = _d.alea(a.toString()); + this.random = wd.alea(a.toString()); } nextValue() { if (!isNaN(this.nextVal)) { let n = this.nextVal; return this.nextVal = NaN, n; } - let e, t6, o = false; + let e, t10, o = false; for (; !o; ) { let n, s, a; do n = 2 * this.random() - 1, s = 2 * this.random() - 1, a = n * n + s * s; while (a >= 1 || a === 0); let i = Math.sqrt(-2 * Math.log(a) / a); - e = this.mean + this.stdDev * n * i, t6 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true); + e = this.mean + this.stdDev * n * i, t10 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true); } - return (!this.truncated || this.isValidTruncated(t6)) && (this.nextVal = this.convertValue(t6)), this.convertValue(e); + return (!this.truncated || this.isValidTruncated(t10)) && (this.nextVal = this.convertValue(t10)), this.convertValue(e); } convertValue(e) { return this.dtype == null || this.dtype === "float32" ? e : Math.round(e); @@ -7449,19 +6720,19 @@ var fu = class { return e <= this.upper && e >= this.lower; } }; -var Nd = class { - constructor(e, t6, o, n) { - this.alpha = e, this.beta = 1 / t6, this.dtype = o; +var Cd = class { + constructor(e, t10, o, n) { + this.alpha = e, this.beta = 1 / t10, this.dtype = o; let s = n || Math.random(); - this.randu = _d.alea(s.toString()), this.randn = new fu(0, 1, o, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); + this.randu = wd.alea(s.toString()), this.randn = new mu(0, 1, o, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); } nextValue() { - let e, t6, o, n, s, a; + let e, t10, o, n, s, a; for (; ; ) { do n = this.randn.nextValue(), a = 1 + this.c * n; while (a <= 0); - if (a *= a * a, e = n * n, t6 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t6 || Math.log(s) < o) + if (a *= a * a, e = n * n, t10 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t10 || Math.log(s) < o) break; } return a = 1 / this.beta * this.d * a, this.alpha < 1 && (a *= Math.pow(this.randu(), 1 / this.alpha)), this.convertValue(a); @@ -7470,11 +6741,11 @@ var Nd = class { return this.dtype === "float32" ? e : Math.round(e); } }; -var Td = class { - constructor(e = 0, t6 = 1, o, n) { - if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t6 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1) - throw new Error(`The difference between ${e} - ${t6} <= 1 and dtype is not float`); - this.random = _d.alea(n); +var Sd = class { + constructor(e = 0, t10 = 1, o, n) { + if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t10 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1) + throw new Error(`The difference between ${e} - ${t10} <= 1 and dtype is not float`); + this.random = wd.alea(n); } convertValue(e) { return this.canReturnFloat() ? e : Math.round(e); @@ -7483,295 +6754,310 @@ var Td = class { return this.convertValue(this.min + this.range * this.random()); } }; -function aG(r, e, t6 = 1, o = "float32", n) { - if (yt(r), t6 == null && (t6 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") +function Y4(r, e, t10 = 1, o = "float32", n) { + if (bt(r), t10 == null && (t10 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") throw new Error(`Unsupported data type ${o}`); - let s = new Nd(e, t6, o, n), a = le(r, o); + let s = new Cd(e, t10, o, n), a = le(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var e1 = N({ randomGamma_: aG }); -function iG(r, e = 0, t6 = 1, o, n) { - if (yt(r), o != null && o === "bool") +var Ok = N({ randomGamma_: Y4 }); +function Q4(r, e = 0, t10 = 1, o, n) { + if (bt(r), o != null && o === "bool") throw new Error(`Unsupported data type ${o}`); - let s = new fu(e, t6, o, false, n), a = le(r, o); + let s = new mu(e, t10, o, false, n), a = le(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var Ed = N({ randomNormal_: iG }); -function uG(r, e, t6) { +var Id = N({ randomNormal_: Q4 }); +function Z4(r, e, t10) { if (e != null && e === "bool") throw new Error(`Unsupported data type ${e}`); - return Ed(r, 0, 1, e, t6); + return Id(r, 0, 1, e, t10); } -var t1 = N({ randomStandardNormal_: uG }); -function pG(r, e = 0, t6 = 1, o = "float32", n) { - yt(r); - let s = le(r, o), a = new Td(e, t6, null, n); +var Pk = N({ randomStandardNormal_: Z4 }); +function J4(r, e = 0, t10 = 1, o = "float32", n) { + bt(r); + let s = le(r, o), a = new Sd(e, t10, null, n); for (let i = 0; i < s.values.length; i++) s.values[i] = a.nextValue(); return s.toTensor(); } -var $d = N({ randomUniform_: pG }); -function Ni(r, e, t6 = 1, o = "float32") { - if (t6 === 0) +var vd = N({ randomUniform_: J4 }); +function Di(r, e, t10 = 1, o = "float32") { + if (t10 === 0) throw new Error("Cannot have a step of zero"); - let n = { start: r, stop: e, step: t6, dtype: o }; - return T.runKernel(ks, {}, n); + let n = { start: r, stop: e, step: t10, dtype: o }; + return T.runKernel(Ts, {}, n); } -function cG(r) { - let t6 = { x: v(r, "x", "reciprocal") }; - return T.runKernel(Dn, t6); +function eG(r) { + let t10 = { input: v(r, "input", "real") }; + return T.runKernel(di, t10); } -var r1 = N({ reciprocal_: cG }); -function lG(r) { - let t6 = { x: v(r, "x", "relu") }; - return T.runKernel(On, t6); +var Fa = N({ real_: eG }); +function tG(r) { + let t10 = { x: v(r, "x", "reciprocal") }; + return T.runKernel(Fn, t10); } -var Ti = N({ relu_: lG }); -function mG(r) { - let t6 = { x: v(r, "x", "relu6") }; - return T.runKernel(Ln, t6); +var Mk = N({ reciprocal_: tG }); +function rG(r) { + let t10 = { x: v(r, "x", "relu") }; + return T.runKernel(On, t10); } -var Ad = N({ relu6_: mG }); -function dG(r, e) { +var Fi = N({ relu_: rG }); +function oG(r) { + let t10 = { x: v(r, "x", "relu6") }; + return T.runKernel(Ln, t10); +} +var kd = N({ relu6_: oG }); +function nG(r, e) { let o = { x: v(r, "x", "reverse") }, n = { dims: e }; return T.runKernel(Bn, o, n); } -var no = N({ reverse_: dG }); -function fG(r) { +var no = N({ reverse_: nG }); +function sG(r) { let e = v(r, "x", "reverse"); - return E(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), no(e, 0); + return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), no(e, 0); } -var o1 = N({ reverse1d_: fG }); -function hG(r, e) { - let t6 = v(r, "x", "reverse"); - return E(t6.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t6.rank}.`), no(t6, e); +var Lk = N({ reverse1d_: sG }); +function aG(r, e) { + let t10 = v(r, "x", "reverse"); + return $(t10.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t10.rank}.`), no(t10, e); } -var n1 = N({ reverse2d_: hG }); -function gG(r, e) { - let t6 = v(r, "x", "reverse"); - return E(t6.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t6.rank}.`), no(t6, e); +var Bk = N({ reverse2d_: aG }); +function iG(r, e) { + let t10 = v(r, "x", "reverse"); + return $(t10.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t10.rank}.`), no(t10, e); } -var s1 = N({ reverse3d_: gG }); -function xG(r, e) { - let t6 = v(r, "x", "reverse"); - return E(t6.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t6.rank}.`), no(t6, e); +var Vk = N({ reverse3d_: iG }); +function uG(r, e) { + let t10 = v(r, "x", "reverse"); + return $(t10.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t10.rank}.`), no(t10, e); } -var a1 = N({ reverse4d_: xG }); -function yG(r) { - let t6 = { x: v(r, "x", "round") }; - return T.runKernel(Ca, t6); +var zk = N({ reverse4d_: uG }); +function pG(r) { + let t10 = { x: v(r, "x", "round") }; + return T.runKernel(Vn, t10); } -var Rd = N({ round_: yG }); -function bG(r) { - let t6 = { x: v(r, "x", "rsqrt", "float32") }; - return T.runKernel(Vn, t6); +var Nd = N({ round_: pG }); +function cG(r) { + let t10 = { x: v(r, "x", "rsqrt", "float32") }; + return T.runKernel(zn, t10); } -var i1 = N({ rsqrt_: bG }); -function CG(r) { - let t6 = { x: v(r, "x", "selu") }; - return T.runKernel(Xi, t6); +var Wk = N({ rsqrt_: cG }); +function lG(r) { + let t10 = { x: v(r, "x", "selu") }; + return T.runKernel(Ca, t10); } -var u1 = N({ selu_: CG }); -function SG(r, e, t6, o, n, s = [1, 1], a = "NHWC") { - let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t6, "pointwiseFilter", "separableConv2d"), c = i, l = false; - if (i.rank === 3 && (l = true, c = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW") +var Uk = N({ selu_: lG }); +function mG(r, e, t10, o, n, s = [1, 1], a = "NHWC") { + let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t10, "pointwiseFilter", "separableConv2d"), c = i, l = false; + if (i.rank === 3 && (l = true, c = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW") throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported"); - E(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), E(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), E(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), E(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`), E(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`); + $(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), $(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), $(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), $(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`), $(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`); let m = p.shape[2], d = p.shape[3]; - E(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`); - let f = Bp(c, p, o, n, a, s), g = vi(f, u, 1, "valid", a); - return l ? z(g, [g.shape[1], g.shape[2], g.shape[3]]) : g; + $(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`); + let f = Bp(c, p, o, n, a, s), g = Ei(f, u, 1, "valid", a); + return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g; } -var p1 = N({ separableConv2d_: SG }); -async function wG(r, e) { - let t6 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d"); - E(t6.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t6.dtype}) and y (${o.dtype}).`), E(t6.rank === 1, () => `x should be 1D tensor, but got x (${t6.shape}).`), E(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`); - let n = await t6.data(), s = await o.data(), a = new Set(s), i = 0; +var Gk = N({ separableConv2d_: mG }); +async function dG(r, e) { + let t10 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d"); + $(t10.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t10.dtype}) and y (${o.dtype}).`), $(t10.rank === 1, () => `x should be 1D tensor, but got x (${t10.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`); + let n = await t10.data(), s = await o.data(), a = new Set(s), i = 0; for (let c = 0; c < n.length; c++) a.has(n[c]) || i++; - let p = new st([i], t6.dtype), u = new st([i], "int32"); + let p = new st([i], t10.dtype), u = new st([i], "int32"); for (let c = 0, l = 0; c < n.length; c++) a.has(n[c]) || (p.values[l] = n[c], u.values[l] = c, l++); return [p.toTensor(), u.toTensor()]; } -var c1 = wG; -function IG(r) { - let t6 = { x: v(r, "x", "sign") }; - return T.runKernel(Yi, t6); +var Hk = dG; +function fG(r) { + let t10 = { x: v(r, "x", "sign") }; + return T.runKernel(wa, t10); } -var l1 = N({ sign_: IG }); -function vG(r) { - let t6 = { x: v(r, "x", "sin", "float32") }; - return T.runKernel(Wn, t6); +var Kk = N({ sign_: fG }); +function hG(r) { + let t10 = { x: v(r, "x", "sin", "float32") }; + return T.runKernel(Un, t10); } -var m1 = N({ sin_: vG }); -function kG(r) { - let t6 = { x: v(r, "x", "sinh") }; - return T.runKernel(Sa, t6); +var qk = N({ sin_: hG }); +function gG(r) { + let t10 = { x: v(r, "x", "sinh") }; + return T.runKernel(Sa, t10); } -var d1 = N({ sinh_: kG }); -function NG(r, e, t6) { +var jk = N({ sinh_: gG }); +function xG(r, e, t10) { let o = v(r, "x", "slice1d"); - return E(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), He(o, [e], [t6]); + return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), Ke(o, [e], [t10]); } -var f1 = N({ slice1d_: NG }); -function TG(r, e, t6) { +var Xk = N({ slice1d_: xG }); +function yG(r, e, t10) { let o = v(r, "x", "slice2d"); - return E(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), He(o, e, t6); + return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10); } -var h1 = N({ slice2d_: TG }); -function _G(r, e, t6) { +var Yk = N({ slice2d_: yG }); +function bG(r, e, t10) { let o = v(r, "x", "slice3d"); - return E(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), He(o, e, t6); + return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10); } -var g1 = N({ slice3d_: _G }); -function EG(r, e, t6) { +var Qk = N({ slice3d_: bG }); +function CG(r, e, t10) { let o = v(r, "x", "slice4d"); - return E(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), He(o, e, t6); + return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10); } -var x1 = N({ slice4d_: EG }); -function $G(r, e = -1) { - let t6 = v(r, "logits", "softmax", "float32"); - if (e === -1 && (e = t6.rank - 1), e !== t6.rank - 1) - throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t6.rank} and dim was ${e}`); - let o = { logits: t6 }, n = { dim: e }; +var Zk = N({ slice4d_: CG }); +function SG(r, e = -1) { + let t10 = v(r, "logits", "softmax", "float32"); + if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1) + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and dim was ${e}`); + let o = { logits: t10 }, n = { dim: e }; return T.runKernel(qn, o, n); } -var y1 = N({ softmax_: $G }); -function AG(r) { - E(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`); +var Jk = N({ softmax_: SG }); +function wG(r) { + $(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`); let e = { input: r }; - return T.runKernel(oi, e); + return T.runKernel(ui, e); } -var zp = N({ fft_: AG }); -function RG(r) { - E(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`); +var zp = N({ fft_: wG }); +function IG(r) { + $(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`); let e = { input: r }; - return T.runKernel(ni, e); + return T.runKernel(pi, e); } -var hu = N({ ifft_: RG }); -function FG(r) { - let e = r.shape[r.shape.length - 1], t6 = r.size / e, o; +var du = N({ ifft_: IG }); +function vG(r) { + let e = r.shape[r.shape.length - 1], t10 = r.size / e, o; if (e <= 2) { - let n = z(r, [t6, e]); - o = hu(n); + let n = W(r, [t10, e]); + o = du(n); } else { - let n = [t6, 2 * (e - 1)], s = z($a(r), [t6, e]), a = z(Si(r), [t6, e]), i = no(He(s, [0, 1], [t6, e - 2]), 1), p = ae(no(He(a, [0, 1], [t6, e - 2]), 1), be(-1)), u = gt([s, i], 1), c = gt([a, p], 1), l = z(Tr(u, c), [n[0], n[1]]); - o = hu(l); + let n = [t10, 2 * (e - 1)], s = W(Fa(r), [t10, e]), a = W(Ri(r), [t10, e]), i = no(Ke(s, [0, 1], [t10, e - 2]), 1), p = ne(no(Ke(a, [0, 1], [t10, e - 2]), 1), Ce(-1)), u = xt([s, i], 1), c = xt([a, p], 1), l = W(Tr(u, c), [n[0], n[1]]); + o = du(l); } - if (o = $a(o), r.rank === 3 && r.shape[0] !== 0) { + if (o = Fa(o), r.rank === 3 && r.shape[0] !== 0) { let n = o, s = r.shape[0]; - o = z(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose(); + o = W(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose(); } return o; } -var Fd = N({ irfft_: FG }); -function DG(r, e, t6 = 0) { - let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t6 }; - return T.runKernel($s, n, s); +var Td = N({ irfft_: vG }); +function kG(r, e, t10 = 0) { + let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t10 }; + return T.runKernel(Rs, n, s); } -var Oa = N({ split_: DG }); -function OG(r, e) { - E(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`); - let t6 = r.shape[r.shape.length - 1], o = r.size / t6, n; - if (e != null && e < t6) { +var Oa = N({ split_: kG }); +function NG(r, e) { + $(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`); + let t10 = r.shape[r.shape.length - 1], o = r.size / t10, n; + if (e != null && e < t10) { let f = r.shape.map((g) => 0), h = r.shape.map((g) => g); - h[r.shape.length - 1] = e, n = He(r, f, h), t6 = e; - } else if (e != null && e > t6) { + h[r.shape.length - 1] = e, n = Ke(r, f, h), t10 = e; + } else if (e != null && e > t10) { let f = r.shape.map((h) => h); - f[r.shape.length - 1] = e - t6, n = gt([r, Vr(f)], r.shape.length - 1), t6 = e; + f[r.shape.length - 1] = e - t10, n = xt([r, Br(f)], r.shape.length - 1), t10 = e; } else n = r; - let s = Ut(n), a = z(Tr(n, s), [o, t6]), i = zp(a), p = Math.floor(t6 / 2) + 1, u = $a(i), c = Si(i), l = Oa(u, [p, t6 - p], u.shape.length - 1), m = Oa(c, [p, t6 - p], c.shape.length - 1), d = n.shape.slice(); - return d[n.shape.length - 1] = p, z(Tr(l[0], m[0]), d); + let s = Ut(n), a = W(Tr(n, s), [o, t10]), i = zp(a), p = Math.floor(t10 / 2) + 1, u = Fa(i), c = Ri(i), l = Oa(u, [p, t10 - p], u.shape.length - 1), m = Oa(c, [p, t10 - p], c.shape.length - 1), d = n.shape.slice(); + return d[n.shape.length - 1] = p, W(Tr(l[0], m[0]), d); } -var Wp = N({ rfft_: OG }); -function PG(r, e) { - let t6 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference"); - [t6, o] = Re(t6, o), Je(t6.shape, o.shape); - let n = { a: t6, b: o }, s = {}; - return T.runKernel(Kn, n, s); +var Wp = N({ rfft_: NG }); +function TG(r, e) { + let t10 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference"); + [t10, o] = De(t10, o), Je(t10.shape, o.shape); + let n = { a: t10, b: o }, s = {}; + return T.runKernel(jn, n, s); } -var Dd = N({ squaredDifference_: PG }); -function MG(r, e) { - let t6 = v(r, "x", "squeeze", "string_or_numeric"); - return z(t6, pb(t6.shape, e).newShape); +var _d = N({ squaredDifference_: TG }); +function _G(r, e) { + let t10 = v(r, "x", "squeeze", "string_or_numeric"); + return W(t10, bb(t10.shape, e).newShape); } -var Up = N({ squeeze_: MG }); -function LG(r, e = 0) { - let t6 = Na(r, "tensors", "stack", "string_or_numeric"); - E(t6.length >= 1, () => "Pass at least one tensor to tf.stack"), t6.length > 0 && E(e <= t6[0].rank, () => "Axis must be <= rank of the tensor"); - let o = t6, n = { axis: e }; - return T.runKernel(vs, o, n); +var Up = N({ squeeze_: _G }); +function $G(r, e = 0) { + let t10 = _a(r, "tensors", "stack", "string_or_numeric"); + $(t10.length >= 1, () => "Pass at least one tensor to tf.stack"), t10.length > 0 && $(e <= t10[0].rank, () => "Axis must be <= rank of the tensor"); + let o = t10, n = { axis: e }; + return T.runKernel(Ns, o, n); } -var Sr = N({ stack_: LG }); -function BG(r, e = 0) { +var Sr = N({ stack_: $G }); +function EG(r, e = 0) { let o = { x: v(r, "x", "step") }, n = { alpha: e }; - return T.runKernel(Ds, o, n); + return T.runKernel(fo, o, n); } -var Od = N({ step_: BG }); -function VG(r, e, t6, o, n = 0, s = 0, a = 0, i = 0, p = 0) { - let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t6, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - return T.runKernel(jn, c, l); +var $d = N({ step_: EG }); +function AG(r, e, t10, o, n = 0, s = 0, a = 0, i = 0, p = 0) { + let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t10, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; + return T.runKernel(Xn, c, l); } -var b1 = N({ stridedSlice_: VG }); -function zG(r) { - let t6 = { x: v(r, "x", "tan", "float32") }; - return T.runKernel(Yn, t6); +var e1 = N({ stridedSlice_: AG }); +function RG(r) { + let t10 = { x: v(r, "x", "tan", "float32") }; + return T.runKernel(Qn, t10); } -var C1 = N({ tan_: zG }); -function mr(r, e) { +var t1 = N({ tan_: RG }); +function dr(r, e) { Jr(r); - let t6 = or(r, e); - if (t6.length !== 1) + let t10 = or(r, e); + if (t10.length !== 1) throw new Error("tensor1d() requires values to be a flat/TypedArray"); - return xr(r, null, t6, e); + return xr(r, null, t10, e); } -function _i(r, e, t6) { +function Oi(r, e, t10) { if (Jr(r), e != null && e.length !== 2) throw new Error("tensor2d() requires shape to have two numbers"); - let o = or(r, t6); + let o = or(r, t10); if (o.length !== 2 && o.length !== 1) throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray"); - return xr(r, e, o, t6); + return xr(r, e, o, t10); } -function S1(r, e, t6) { +function Ed(r, e, t10) { + if (Jr(r), e != null && e.length !== 3) + throw new Error("tensor3d() requires shape to have three numbers"); + let o = or(r, t10); + if (o.length !== 3 && o.length !== 1) + throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); + if (o.length === 1 && e == null) + throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); + return xr(r, e, o, t10); +} +function r1(r, e, t10) { if (Jr(r), e != null && e.length !== 4) throw new Error("tensor4d() requires shape to have four numbers"); - let o = or(r, t6); + let o = or(r, t10); if (o.length !== 4 && o.length !== 1) throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor4d() requires shape to be provided when `values` are a flat array"); - return xr(r, e, o, t6); + return xr(r, e, o, t10); } -function w1(r, e, t6) { +function o1(r, e, t10) { if (Jr(r), e != null && e.length !== 5) throw new Error("tensor5d() requires shape to have five numbers"); - let o = or(r, t6); + let o = or(r, t10); if (o.length !== 5 && o.length !== 1) throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor5d() requires shape to be provided when `values` are a flat array"); - return xr(r, e, o, t6); + return xr(r, e, o, t10); } -function I1(r, e, t6) { +function n1(r, e, t10) { if (Jr(r), e != null && e.length !== 6) throw new Error("tensor6d() requires shape to have six numbers"); - let o = or(r, t6); + let o = or(r, t10); if (o.length !== 6 && o.length !== 1) throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray"); if (o.length === 1 && e == null) throw new Error("tensor6d() requires shape to be provided when `values` are a flat array"); - return e = e || o, xr(r, e, o, t6); + return e = e || o, xr(r, e, o, t10); } -function WG(r, e = 1, t6 = true) { +function DG(r, e = 1, t10 = true) { let o = v(r, "x", "topk"); if (o.rank === 0) throw new Error("topk() expects the input to be of rank 1 or higher"); @@ -7780,163 +7066,217 @@ function WG(r, e = 1, t6 = true) { throw new Error(`'k' passed to topk() must be >= 0 but got ${e}`); if (e > n) throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`); - let s = { x: o }, a = { k: e, sorted: t6 }, [i, p] = T.runKernel(Zn, s, a); + let s = { x: o }, a = { k: e, sorted: t10 }, [i, p] = T.runKernel(Jn, s, a); return { values: i, indices: p }; } -var v1 = N({ topk_: WG }); -function UG(r, e = 0, t6 = 1, o, n) { - if (yt(r), o != null && o === "bool") +var s1 = N({ topk_: DG }); +function FG(r, e = 0, t10 = 1, o, n) { + if (bt(r), o != null && o === "bool") throw new Error("Unsupported data type $ { dtype }"); - let s = new fu(e, t6, o, true, n), a = le(r, o); + let s = new mu(e, t10, o, true, n), a = le(r, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var k1 = N({ truncatedNormal_: UG }); -function GG(r, e = 0) { - let t6 = v(r, "x", "unique", "string_or_numeric"); - E(t6.rank > 0, () => "The input tensor must be at least 1D"); - let o = { x: t6 }, n = { axis: e }, [s, a] = T.runKernel(kp, o, n); +var a1 = N({ truncatedNormal_: FG }); +function OG(r, e = 0) { + let t10 = v(r, "x", "unique", "string_or_numeric"); + $(t10.rank > 0, () => "The input tensor must be at least 1D"); + let o = { x: t10 }, n = { axis: e }, [s, a] = T.runKernel(Np, o, n); return { values: s, indices: a }; } -var N1 = N({ unique_: GG }); -function HG(r, e, t6) { +var i1 = N({ unique_: OG }); +function PG(r, e, t10) { let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32"); - E(na(t6), () => "numSegments must be of dtype int"); - let s = { x: o, segmentIds: n }, a = { numSegments: t6 }; - return T.runKernel(Np, s, a); + $(sa(t10), () => "numSegments must be of dtype int"); + let s = { x: o, segmentIds: n }, a = { numSegments: t10 }; + return T.runKernel(Tp, s, a); } -var T1 = N({ unsortedSegmentSum_: HG }); -function qG(r, e = 0) { - let t6 = v(r, "x", "unstack", "string_or_numeric"); - E(e >= -t6.shape.length && e < t6.shape.length, () => `Axis = ${e} is not in [-${t6.shape.length}, ${t6.shape.length})`); - let o = { value: t6 }, n = { axis: e }; - return T.runKernel(Rs, o, n); +var u1 = N({ unsortedSegmentSum_: PG }); +function MG(r, e = 0) { + let t10 = v(r, "x", "unstack", "string_or_numeric"); + $(e >= -t10.shape.length && e < t10.shape.length, () => `Axis = ${e} is not in [-${t10.shape.length}, ${t10.shape.length})`); + let o = { value: t10 }, n = { axis: e }; + return T.runKernel(Fs, o, n); } -var so = N({ unstack_: qG }); -function _1(r, e) { - return al(r, e, "right"); +var so = N({ unstack_: MG }); +function p1(r, e) { + return ol(r, e, "right"); } -function E1(r, e = true, t6, o) { - return T.makeVariable(r, e, t6, o); +function c1(r, e = true, t10, o) { + return T.makeVariable(r, e, t10, o); } -function Pd(r, e) { - let t6 = []; +function Ad(r, e) { + let t10 = []; for (let s = 0; s < e.length; s++) - e[s] && t6.push(s); - let o = le(r, "int32"), n = le([t6.length, r.length], "int32"); - for (let s = 0; s < t6.length; s++) { - let a = o.indexToLoc(t6[s]), i = s * r.length; + e[s] && t10.push(s); + let o = le(r, "int32"), n = le([t10.length, r.length], "int32"); + for (let s = 0; s < t10.length; s++) { + let a = o.indexToLoc(t10[s]), i = s * r.length; n.values.set(a, i); } return n.toTensor(); } -async function KG(r) { - let e = v(r, "condition", "whereAsync", "bool"), t6 = await e.data(), o = Pd(e.shape, t6); +async function LG(r) { + let e = v(r, "condition", "whereAsync", "bool"), t10 = await e.data(), o = Ad(e.shape, t10); return r !== e && e.dispose(), o; } -var Md = KG; -async function jG(r, e, t6) { - let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t6 == null ? 0 : t6, a = n.rank, i = o.shape; - E(a > 0, () => "mask cannot be scalar"), ht(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); +var Rd = LG; +async function BG(r, e, t10) { + let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t10 == null ? 0 : t10, a = n.rank, i = o.shape; + $(a > 0, () => "mask cannot be scalar"), gt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); let p = 1; for (let h = s; h < s + a; h++) p *= i[h]; - let u = i.slice(0, s).concat([p], i.slice(s + a)), c = z(o, u), l = z(n, [-1]), m = await Md(l), d = Up(m, [1]), f = pd(c, d, s); + let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await Rd(l), d = Up(m, [1]), f = rd(c, d, s); return r !== o && o.dispose(), e !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f; } -var XG = jG; -function YG(r, e, t6, o, n = true) { - let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t6, "decay", "movingAverage"); - Fb(s, a), E(Pr(s.shape, a.shape), () => "Shape mismatch in v and x"); - let p = be(1), u = Ne(p, i), c = ae(Ne(a, s), u); +var VG = BG; +function zG(r, e, t10) { + let o = v(r, "x", "transpose"); + if (e == null && (e = o.shape.map((a, i) => i).reverse()), $(o.rank === e.length, () => `Error in transpose: rank of input ${o.rank} must match length of perm ${e}.`), e.forEach((a) => { + $(a >= 0 && a < o.rank, () => `All entries in 'perm' must be between 0 and ${o.rank - 1} but got ${e}`); + }), o.rank <= 1) + return o.clone(); + let n = { x: o }, s = { perm: e }; + return o.dtype === "complex64" ? Ee(() => { + let a = Fa(o), i = Ri(o); + return a = T.runKernel(ro, { x: a }, s), i = T.runKernel(ro, { x: i }, s), t10 && (i = Cr(i)), Tr(a, i); + }) : T.runKernel(ro, n, s); +} +var Gp = N({ transpose_: zG }); +function WG(r, e, t10, o, n = true) { + let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t10, "decay", "movingAverage"); + Ub(s, a), $(Or(s.shape, a.shape), () => "Shape mismatch in v and x"); + let p = Ce(1), u = ke(p, i), c = ne(ke(a, s), u); if (n) { - E(o != null, () => "When using zeroDebias: true, step is required."); + $(o != null, () => "When using zeroDebias: true, step is required."); let l = v(o, "step", "movingAverage"); - c = Ge(c, Ne(p, Ra(i, l))); + c = He(c, ke(p, Aa(i, l))); } - return xe(s, c); + return be(s, c); } -var QG = N({ movingAverage_: YG }); -function ZG(r, e, t6) { - yt(t6); +var UG = N({ movingAverage_: WG }); +var sl = {}; +Ge(sl, { calculateShapes: () => l1, validateInput: () => Dd, validateUpdateShape: () => bC }); +function bC(r, e, t10) { + let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t10.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; + if (t10.rank < n) + throw new Error(s + ` update.rank < ${n}. `); + if (r.length < o + (t10.rank - n)) + throw new Error(s + ` Output shape length < ${o + (t10.rank - n)}`); + if (t10.rank !== n + r.length - o) + throw new Error(s + ` update.rank != ${n + r.length - o}`); + for (let a = 0; a < n; ++a) + if (t10.shape[a] !== e.shape[a]) + throw new Error(s + ` updates.shape[${a}] (${t10.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`); + for (let a = 0; a < t10.rank - n; ++a) + if (t10.shape[a + n] !== r[a + o]) + throw new Error(s + ` updates.shape[${a + n}] (${t10.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`); +} +function Dd(r, e, t10) { + if (e.rank < 1) + throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${e.rank}.`); + if (r.rank < 1) + throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`); + if (e.dtype !== "int32") + throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${e.dtype}`); + if (t10.length < 1) + throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t10}`); + if (t10.length === 0) { + if (e.size === 0) + throw new Error(`Indices specified for empty output. indices shape: ${e.shape}`); + if (r.size === 0) + throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`); + } + bC(t10, e, r); +} +function l1(r, e, t10) { + let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t10.length, a = 1; + for (let l = n; l < s; ++l) + a *= t10[l]; + let i = n < 1 ? 1 : n, p = We(e.shape) / i, u = [...xs(t10.slice(0, n)), 1], c = We(t10); + return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c }; +} +function GG(r, e, t10) { + bt(t10); let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND"); - Qm(n, o, t6); - let s = { indices: o, updates: n }, a = { shape: t6 }; - return T.runKernel(zn, s, a); + Dd(n, o, t10); + let s = { indices: o, updates: n }, a = { shape: t10 }; + return T.runKernel(Wn, s, a); } -var JG = N({ scatterND_: ZG }); -function $1(r, e, t6, o) { +var HG = N({ scatterND_: GG }); +function m1(r, e, t10, o) { if (r.dtype !== "int32") throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`); if (r.rank > 2) throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`); let n = r.rank > 0 ? r.shape[0] : 1, s = r.rank > 1 ? r.shape[1] : 1; - if (t6.length !== s) - throw new Error(`outputShape has incorrect number of elements:, ${t6.length}, should be: ${s}.`); + if (t10.length !== s) + throw new Error(`outputShape has incorrect number of elements:, ${t10.length}, should be: ${s}.`); let a = e.size; if (!(e.rank === 0 || e.rank === 1 && a === n)) throw new Error(`sparseValues has incorrect shape ${e.shape}, should be [] or [${n}]`); if (e.dtype !== o.dtype) throw new Error("sparseValues.dtype must match defaultValues.dtype"); } -function tH(r, e, t6, o = 0) { - yt(t6); +function qG(r, e, t10, o = 0) { + bt(t10); let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype); - $1(n, s, t6, a); - let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t6 }; - return T.runKernel(li, i, p); + m1(n, s, t10, a); + let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t10 }; + return T.runKernel(yi, i, p); } -var rH = N({ sparseToDense_: tH }); -function oH(r, e) { - let t6 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t6 }; - return T.runKernel(un, n); +var jG = N({ sparseToDense_: qG }); +function XG(r, e) { + let t10 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t10 }; + return T.runKernel(an, n); } -var nH = N({ gatherND_: oH }); -function A1(r, e) { +var YG = N({ gatherND_: XG }); +function d1(r, e) { if (e == null) return r.shape.slice(); - if (Pr(r.shape, e)) + if (Or(r.shape, e)) return e; if (r.shape.length === e.length) { - let t6 = []; + let t10 = []; for (let o = 0; o < r.shape.length; o++) - e[o] == null && r.shape[o] != null ? t6.push(r.shape[o]) : t6.push(e[o]); - return t6; + e[o] == null && r.shape[o] != null ? t10.push(r.shape[o]) : t10.push(e[o]); + return t10; } return e; } -function sH(r, e, t6, o) { +function QG(r, e, t10, o) { let n = v(r, "x", "dropout"); - if (E(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), E(e >= 0 && e < 1, () => `rate must be a float in the range [0, 1), but got ${e}.`), e === 0) + if ($(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), $(e >= 0 && e < 1, () => `rate must be a float in the range [0, 1), but got ${e}.`), e === 0) return r instanceof it ? n.clone() : n; - let s = A1(n, t6), a = 1 - e, i = Ge(ud(xe($d(s, 0, 1, "float32", o), a)), a); - return ae(n, i); + let s = d1(n, t10), a = 1 - e, i = He(td(be(vd(s, 0, 1, "float32", o), a)), a); + return ne(n, i); } -var aH = N({ dropout_: sH }); -function xC(r) { +var ZG = N({ dropout_: QG }); +function CC(r) { return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2)))); } -function il(r, e, t6) { +function al(r, e, t10) { let o = 1 - r % 2, n = new Float32Array(r); for (let s = 0; s < r; ++s) { let a = 2 * Math.PI * s / (r + o - 1); - n[s] = e - t6 * Math.cos(a); + n[s] = e - t10 * Math.cos(a); } - return mr(n, "float32"); + return dr(n, "float32"); } -async function iH(r, e, t6 = 1) { +async function JG(r, e, t10 = 1) { let o = v(r, "predictions", "inTopK"), n = v(e, "targets", "inTopK"); - E(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), E(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), ht(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); + $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), gt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); let s = o.shape[o.shape.length - 1]; - E(t6 > 0 && t6 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t6}`); - let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = cb("bool", p); + $(t10 > 0 && t10 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t10}`); + let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = Cb("bool", p); for (let l = 0; l < p; l++) { let m = l * u, d = a.subarray(m, m + u), f = []; for (let h = 0; h < d.length; h++) f.push({ value: d[h], index: h }); f.sort((h, g) => g.value - h.value), c[l] = 0; - for (let h = 0; h < t6; h++) + for (let h = 0; h < t10; h++) if (f[h].index === i[l]) { c[l] = 1; break; @@ -7944,589 +7284,589 @@ async function iH(r, e, t6 = 1) { } return r !== o && o.dispose(), e !== n && n.dispose(), nr(c, n.shape, "bool"); } -var uH = iH; -var yC = {}; -Ue(yC, { conv2d: () => F1, depthwiseConv2d: () => P1, matMul: () => M1 }); -function pH(r, e, t6, o, n, s = "NHWC", a) { +var eH = JG; +var SC = {}; +Ge(SC, { conv2d: () => h1, depthwiseConv2d: () => y1, matMul: () => b1 }); +function tH(r, e, t10, o, n, s = "NHWC", a) { let i = r; - r.rank === 3 && (i = z(r, [1, r.shape[0], r.shape[1], r.shape[2]])); + r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); let p = e; - p.rank === 3 && (p = z(e, [1, e.shape[0], e.shape[1], e.shape[2]])), E(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), E(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), E(t6.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t6}.`); + p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(t10.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t10}.`); let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1]; - E(u === t6[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t6[2]}.`), E(c === t6[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t6[3]}).`), Pt("conv2dDerFilter", n, a); - let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t6 }; - return T.runKernel(cp, l, m); + $(u === t10[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t10[2]}.`), $(c === t10[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t10[3]}).`), Mt("conv2dDerFilter", n, a); + let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t10 }; + return T.runKernel(oi, l, m); } -var R1 = N({ conv2DBackpropFilter_: pH }); -function gu(r, e, t6) { - if (t6 == null || t6 === "linear") +var f1 = N({ conv2DBackpropFilter_: tH }); +function fu(r, e, t10) { + if (t10 == null || t10 === "linear") return r; - if (t6 === "relu") - return ae(r, Od(e)); - throw new Error(`Cannot compute gradient for fused activation ${t6}.`); + if (t10 === "relu") + return ne(r, $d(e)); + throw new Error(`Cannot compute gradient for fused activation ${t10}.`); } -function xu(r, e) { - let t6 = e, o = jm(r.shape, e.shape); - return o.length > 0 && (t6 = et(t6, o)), z(t6, r.shape); +function hu(r, e) { + let t10 = e, o = Qm(r.shape, e.shape); + return o.length > 0 && (t10 = et(t10, o)), W(t10, r.shape); } -function yu(r, e, t6, o) { +function gu(r, e, t10, o) { if (e === "linear") return r; if (e === "relu") - return Ti(r); + return Fi(r); if (e === "elu") - return ad(r); + return Jm(r); if (e === "relu6") - return Ad(r); + return kd(r); if (e === "prelu") - return vd(r, t6); + return xd(r, t10); if (e === "leakyrelu") - return ld(r, o); + return nd(r, o); if (e === "sigmoid") - return zs(r); + return Ws(r); throw new Error(`Unknown fused activation ${e}.`); } -var bu = (r, e) => !(r > 0) || e === "linear"; -function cH({ x: r, filter: e, strides: t6, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (p = p || "linear", bu(T.state.gradientDepth, p) === false) { - E(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); - let _ = vi(r, e, t6, o, n, s, a); - return i != null && (_ = xe(_, i)), yu(_, p, u, c); +var xu = (r, e) => !(r > 0) || e === "linear"; +function rH({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (p = p || "linear", xu(T.state.gradientDepth, p) === false) { + $(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); + let _ = Ei(r, e, t10, o, n, s, a); + return i != null && (_ = be(_, i)), gu(_, p, u, c); } let l = v(r, "x", "conv2d", "float32"), m = v(e, "filter", "conv2d", "float32"), d = l, f = false; - l.rank === 3 && (f = true, d = z(l, [1, l.shape[0], l.shape[1], l.shape[2]])), E(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), E(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Pt("fused conv2d", o, a); + l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Mt("fused conv2d", o, a); let h = n === "NHWC" ? d.shape[3] : d.shape[1]; - E(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), E(lr(t6, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t6} and dilations '${s}'`); - let g = uu(d.shape, m.shape, t6, s, o, a), x; - i != null && (x = v(i, "bias", "fused conv2d"), [x] = Re(x, l), n === "NHWC" ? Je(g.outShape, x.shape) : (E(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), E(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`))); + $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(mr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`); + let g = au(d.shape, m.shape, t10, s, o, a), x; + i != null && (x = v(i, "bias", "fused conv2d"), [x] = De(x, l), n === "NHWC" ? Je(g.outShape, x.shape) : ($(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), $(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`))); let b; if (u != null) { let _ = u.shape; - if (E(_.length <= 1 || _.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${_.length}.`), _.length === 1) - E(_[0] === 1 || _[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the number of output channels (${g.outChannels}).`); + if ($(_.length <= 1 || _.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${_.length}.`), _.length === 1) + $(_[0] === 1 || _[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the number of output channels (${g.outChannels}).`); else if (_.length === 3) try { Je(_, g.outShape); - } catch ($) { + } catch (E) { let A = `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the output shape of the conv2d (${g.outShape}).`; throw Error(A); } b = v(u, "prelu weights", "fused conv2d"); } - let C = (_, $) => { - E(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`); - let [A, R, D, P] = $, M = gu(_, D, p); - E(iu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); - let L = nd(R.shape, M, A, t6, o), W = R1(R, M, A.shape, t6, o), V = [L, W]; + let C = (_, E) => { + $(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`); + let [A, R, D, P] = E, M = fu(_, D, p); + $(su(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); + let L = Ym(R.shape, M, A, t10, o), V = f1(R, M, A.shape, t10, o), z = [L, V]; if (P != null) { - let U = xu(P, M); - V.push(U); + let U = hu(P, M); + z.push(U); } - return V; - }, w = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t6, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? Cr(($, A, R) => { - let D = T.runKernel(ho, w, k); - return R([A, $, D]), f && (D = z(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: C }; - })(d, m) : Cr(($, A, R, D) => { - let P = T.runKernel(ho, w, k); - return D([A, $, P, R]), f && (P = z(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C }; + return z; + }, w = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; + return i == null ? br((E, A, R) => { + let D = T.runKernel(go, w, k); + return R([A, E, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: C }; + })(d, m) : br((E, A, R, D) => { + let P = T.runKernel(go, w, k); + return D([A, E, P, R]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C }; })(d, m, x); } -var F1 = N({ fusedConv2d_: cH }); -function lH(r, e, t6, o, n, s = [1, 1], a) { +var h1 = N({ fusedConv2d_: rH }); +function oH(r, e, t10, o, n, s = [1, 1], a) { let i = r; - r.rank === 3 && (i = z(r, [1, r.shape[0], r.shape[1], r.shape[2]])); + r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); let p = e; - p.rank === 3 && (p = z(e, [1, e.shape[0], e.shape[1], e.shape[2]])); - let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t6 }; - return T.runKernel(dp, u, c); + p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])); + let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t10 }; + return T.runKernel(yp, u, c); } -var D1 = N({ depthwiseConv2dNativeBackpropFilter_: lH }); -function mH(r, e, t6, o, n, s = [1, 1], a) { +var g1 = N({ depthwiseConv2dNativeBackpropFilter_: oH }); +function nH(r, e, t10, o, n, s = [1, 1], a) { let i = e, p = false; - e.rank === 3 && (p = true, i = z(e, [1, e.shape[0], e.shape[1], e.shape[2]])); - let u = { dy: i, filter: t6 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(fp, u, c); - return p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; + e.rank === 3 && (p = true, i = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])); + let u = { dy: i, filter: t10 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(bp, u, c); + return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var O1 = N({ depthwiseConv2dNativeBackpropInput_: mH }); -function dH({ x: r, filter: e, strides: t6, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (bu(T.state.gradientDepth, p) === false) { - let k = Bp(r, e, t6, o, n, s, a); - return i != null && (k = xe(k, i)), yu(k, p, u, c); +var x1 = N({ depthwiseConv2dNativeBackpropInput_: nH }); +function sH({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (xu(T.state.gradientDepth, p) === false) { + let k = Bp(r, e, t10, o, n, s, a); + return i != null && (k = be(k, i)), gu(k, p, u, c); } let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(e, "filter", "depthwiseConv2d", "float32"), d = l, f = false; - l.rank === 3 && (f = true, d = z(l, [1, l.shape[0], l.shape[1], l.shape[2]])), E(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), E(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), E(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), E(lr(t6, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t6} and dilations '${s}'`), Pt("fused depthwiseConv2d", o, a); - let h = uu(d.shape, m.shape, t6, s, o, a, true), g; - i != null && (g = v(i, "bias", "fused conv2d"), [g] = Re(g, l), Je(h.outShape, g.shape)); + l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(mr(t10, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), Mt("fused depthwiseConv2d", o, a); + let h = au(d.shape, m.shape, t10, s, o, a, true), g; + i != null && (g = v(i, "bias", "fused conv2d"), [g] = De(g, l), Je(h.outShape, g.shape)); let x; u != null && (x = v(u, "prelu weights", "fused depthwiseConv2d")); let b = (k, _) => { - E(iu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); - let [$, A, R, D] = _, P = gu(k, R, p), M = O1(A.shape, P, $, t6, o, s, a), L = D1(A, P, $.shape, t6, o, s, a); + $(su(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); + let [E, A, R, D] = _, P = fu(k, R, p), M = x1(A.shape, P, E, t10, o, s, a), L = g1(A, P, E.shape, t10, o, s, a); if (D != null) { - let W = xu(g, P); - return [M, L, W]; + let V = hu(g, P); + return [M, L, V]; } return [M, L]; - }, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, w = { strides: t6, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? Cr((_, $, A) => { - let R = T.runKernel(go, C, w); - return A([$, _, R]), f && (R = z(R, [R.shape[1], R.shape[2], R.shape[3]])), { value: R, gradFunc: b }; - })(d, m) : Cr((_, $, A, R) => { - let D = T.runKernel(go, C, w); - return R([$, _, D, A]), f && (D = z(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b }; + }, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, w = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; + return i == null ? br((_, E, A) => { + let R = T.runKernel(xo, C, w); + return A([E, _, R]), f && (R = W(R, [R.shape[1], R.shape[2], R.shape[3]])), { value: R, gradFunc: b }; + })(d, m) : br((_, E, A, R) => { + let D = T.runKernel(xo, C, w); + return R([E, _, D, A]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b }; })(d, m, g); } -var P1 = N({ fusedDepthwiseConv2d_: dH }); -function fH({ a: r, b: e, transposeA: t6 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { - if (bu(T.state.gradientDepth, s) === false) { - let P = Xe(r, e, t6, o); - return n != null && (P = xe(P, n)), yu(P, s, a, i); +var y1 = N({ fusedDepthwiseConv2d_: sH }); +function aH({ a: r, b: e, transposeA: t10 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { + if (xu(T.state.gradientDepth, s) === false) { + let P = Xe(r, e, t10, o); + return n != null && (P = be(P, n)), gu(P, s, a, i); } let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul"); - [p, u] = Re(p, u); - let c = t6 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t6 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = ze(f), x = ze(h); - E(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t6} and transposeB=${o} must match.`); - let C = Je(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), w = t6 ? z(p, [g, c, m]) : z(p, [g, m, c]), k = o ? z(u, [x, d, l]) : z(u, [x, l, d]), _; - n != null && (_ = v(n, "bias", "fused matMul"), [_] = Re(_, p), Je(C, _.shape)); - let $; - a != null && ($ = v(a, "prelu weights", "fused matMul")); + [p, u] = De(p, u); + let c = t10 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t10 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = We(f), x = We(h); + $(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t10} and transposeB=${o} must match.`); + let C = Je(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), w = t10 ? W(p, [g, c, m]) : W(p, [g, m, c]), k = o ? W(u, [x, d, l]) : W(u, [x, l, d]), _; + n != null && (_ = v(n, "bias", "fused matMul"), [_] = De(_, p), Je(C, _.shape)); + let E; + a != null && (E = v(a, "prelu weights", "fused matMul")); let A = (P, M) => { - let [L, W, V, U] = M, q = gu(z(P, V.shape), V, s), H, j; - if (!t6 && !o ? (H = Xe(q, W, false, true), j = Xe(L, q, true, false)) : !t6 && o ? (H = Xe(q, W, false, false), j = Xe(q, L, true, false)) : t6 && !o ? (H = Xe(W, q, false, true), j = Xe(L, q, false, false)) : (H = Xe(W, q, true, true), j = Xe(q, L, true, true)), n != null) { - let X = xu(U, q); - return [H, j, X]; + let [L, V, z, U] = M, K = fu(W(P, z.shape), z, s), H, q; + if (!t10 && !o ? (H = Xe(K, V, false, true), q = Xe(L, K, true, false)) : !t10 && o ? (H = Xe(K, V, false, false), q = Xe(K, L, true, false)) : t10 && !o ? (H = Xe(V, K, false, true), q = Xe(L, K, false, false)) : (H = Xe(V, K, true, true), q = Xe(K, L, true, true)), n != null) { + let Z = hu(U, K); + return [H, q, Z]; } else - return [H, j]; - }, R = { a: w, b: k, bias: _, preluActivationWeights: $ }, D = { transposeA: t6, transposeB: o, activation: s, leakyreluAlpha: i }; - return n == null ? Cr((M, L, W) => { - let V = T.runKernel(fo, R, D); - return W([M, L, V]), { value: z(V, C), gradFunc: A }; - })(w, k) : Cr((M, L, W, V) => { - let U = T.runKernel(fo, R, D); - return V([M, L, U, W]), { value: z(U, C), gradFunc: A }; + return [H, q]; + }, R = { a: w, b: k, bias: _, preluActivationWeights: E }, D = { transposeA: t10, transposeB: o, activation: s, leakyreluAlpha: i }; + return n == null ? br((M, L, V) => { + let z = T.runKernel(ho, R, D); + return V([M, L, z]), { value: W(z, C), gradFunc: A }; + })(w, k) : br((M, L, V, z) => { + let U = T.runKernel(ho, R, D); + return z([M, L, U, V]), { value: W(U, C), gradFunc: A }; })(w, k, _); } -var M1 = N({ fusedMatMul_: fH }); -function hH(r) { - return il(r, 0.54, 0.46); +var b1 = N({ fusedMatMul_: aH }); +function iH(r) { + return al(r, 0.54, 0.46); } -var L1 = N({ hammingWindow_: hH }); -function gH(r) { - return il(r, 0.5, 0.5); +var C1 = N({ hammingWindow_: iH }); +function uH(r) { + return al(r, 0.5, 0.5); } -var Ld = N({ hannWindow_: gH }); -function xH(r, e, t6, o = false, n = 0) { +var Fd = N({ hannWindow_: uH }); +function pH(r, e, t10, o = false, n = 0) { let s = 0, a = []; for (; s + e <= r.size; ) - a.push(He(r, s, e)), s += t6; + a.push(Ke(r, s, e)), s += t10; if (o) for (; s < r.size; ) { - let i = s + e - r.size, p = gt([He(r, s, e - i), Ws([i], n)]); - a.push(p), s += t6; + let i = s + e - r.size, p = xt([Ke(r, s, e - i), Us([i], n)]); + a.push(p), s += t10; } - return a.length === 0 ? _i([], [0, e]) : z(gt(a), [a.length, e]); + return a.length === 0 ? Oi([], [0, e]) : W(xt(a), [a.length, e]); } -var Bd = N({ frame_: xH }); -function yH(r, e, t6, o, n = Ld) { - o == null && (o = xC(e)); - let s = Bd(r, e, t6), a = ae(s, n(e)); +var Od = N({ frame_: pH }); +function cH(r, e, t10, o, n = Fd) { + o == null && (o = CC(e)); + let s = Od(r, e, t10), a = ne(s, n(e)); return Wp(a, o); } -var B1 = N({ stft_: yH }); -function bH(r, e, t6, o, n = "bilinear", s = 0) { - let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t6, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; - E(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), E(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), E(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), E(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), E(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), E(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`); +var S1 = N({ stft_: cH }); +function lH(r, e, t10, o, n = "bilinear", s = 0) { + let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t10, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; + $(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), $(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), $(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), $(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), $(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`); let c = { image: a, boxes: i, boxInd: p }, l = { method: n, extrapolationValue: s, cropSize: o }; - return T.runKernel(Yo, c, l); + return T.runKernel(Xo, c, l); } -var V1 = N({ cropAndResize_: bH }); -function CH(r) { +var w1 = N({ cropAndResize_: lH }); +function mH(r) { let e = v(r, "image", "flipLeftRight", "float32"); - E(e.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`); - let t6 = { image: e }; - return T.runKernel(on, t6, {}); + $(e.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`); + let t10 = { image: e }; + return T.runKernel(rn, t10, {}); } -var z1 = N({ flipLeftRight_: CH }); -function SH(r) { - let e = v(r, "image", "grayscaleToRGB"), t6 = e.rank - 1, o = e.shape[t6]; - E(e.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`), E(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`); +var I1 = N({ flipLeftRight_: mH }); +function dH(r) { + let e = v(r, "image", "grayscaleToRGB"), t10 = e.rank - 1, o = e.shape[t10]; + $(e.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`), $(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`); let n = new Array(e.rank); - return n.fill(1, 0, t6), n[t6] = 3, ki(e, n); + return n.fill(1, 0, t10), n[t10] = 3, Ai(e, n); } -var W1 = N({ grayscaleToRGB_: SH }); -function wH(r, e, t6 = 0, o = 0.5) { +var v1 = N({ grayscaleToRGB_: dH }); +function fH(r, e, t10 = 0, o = 0.5) { let n = v(r, "image", "rotateWithOffset", "float32"); - E(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`); - let s = { image: n }, a = { radians: e, fillValue: t6, center: o }; - return T.runKernel(es, s, a); + $(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`); + let s = { image: n }, a = { radians: e, fillValue: t10, center: o }; + return T.runKernel(ts, s, a); } -var U1 = N({ rotateWithOffset_: wH }); -function So(r, e, t6, o, n, s) { +var k1 = N({ rotateWithOffset_: fH }); +function wo(r, e, t10, o, n, s) { o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0); let a = r.shape[0]; - return t6 = Math.min(t6, a), E(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), E(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), E(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), E(e.rank === 1, () => "scores must be a 1D tensor"), E(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), E(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t6, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; + return t10 = Math.min(t10, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(e.rank === 1, () => "scores must be a 1D tensor"), $(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; } -function IH(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = So(s, a, t6, o, n); - t6 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; - let p = { maxOutputSize: t6, iouThreshold: o, scoreThreshold: n }; +function hH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = wo(s, a, t10, o, n); + t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; + let p = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n }; return T.runKernel(Tn, { boxes: s, scores: a }, p); } -var G1 = N({ nonMaxSuppression_: IH }); -function H1(r, e, t6) { - let o = vH(r, e, t6), n = o < 0 ? -(o + 1) : o; +var N1 = N({ nonMaxSuppression_: hH }); +function T1(r, e, t10) { + let o = gH(r, e, t10), n = o < 0 ? -(o + 1) : o; r.splice(n, 0, e); } -function vH(r, e, t6) { - return NH(r, e, t6 || kH); +function gH(r, e, t10) { + return yH(r, e, t10 || xH); } -function kH(r, e) { +function xH(r, e) { return r > e ? 1 : r < e ? -1 : 0; } -function NH(r, e, t6) { +function yH(r, e, t10) { let o = 0, n = r.length, s = 0, a = false; for (; o < n; ) { s = o + (n - o >>> 1); - let i = t6(e, r[s]); + let i = t10(e, r[s]); i > 0 ? o = s + 1 : (n = s, a = !i); } return a ? o : -o - 1; } -function Vd(r, e, t6, o, n) { - return bC(r, e, t6, o, n, 0); +function Pd(r, e, t10, o, n) { + return wC(r, e, t10, o, n, 0); } -function zd(r, e, t6, o, n, s) { - return bC(r, e, t6, o, n, 0, false, s, true); +function Md(r, e, t10, o, n, s) { + return wC(r, e, t10, o, n, 0, false, s, true); } -function Wd(r, e, t6, o, n, s) { - return bC(r, e, t6, o, n, s, true); +function Ld(r, e, t10, o, n, s) { + return wC(r, e, t10, o, n, s, true); } -function bC(r, e, t6, o, n, s, a = false, i = false, p = false) { +function wC(r, e, t10, o, n, s, a = false, i = false, p = false) { let u = []; for (let g = 0; g < e.length; g++) e[g] > n && u.push({ score: e[g], boxIndex: g, suppressBeginIndex: 0 }); - u.sort(q1); + u.sort(_1); let c = s > 0 ? -0.5 / s : 0, l = [], m = []; - for (; l.length < t6 && u.length > 0; ) { + for (; l.length < t10 && u.length > 0; ) { let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: C } = g; if (x < n) break; let w = false; for (let k = l.length - 1; k >= C; --k) { - let _ = TH(r, b, l[k]); + let _ = bH(r, b, l[k]); if (_ >= o) { w = true; break; } - if (g.score = g.score * _H(o, c, _), g.score <= n) + if (g.score = g.score * CH(o, c, _), g.score <= n) break; } - g.suppressBeginIndex = l.length, w || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && H1(u, g, q1)); + g.suppressBeginIndex = l.length, w || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && T1(u, g, _1)); } - let d = l.length, f = t6 - d; + let d = l.length, f = t10 - d; i && f > 0 && (l.push(...new Array(f).fill(0)), m.push(...new Array(f).fill(0))); let h = { selectedIndices: l }; return a && (h.selectedScores = m), p && (h.validOutputs = d), h; } -function TH(r, e, t6) { - let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t6 * 4, t6 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); +function bH(r, e, t10) { + let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t10 * 4, t10 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); if (d <= 0 || f <= 0) return 0; let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), C = Math.max(x - h, 0) * Math.max(b - g, 0); return C / (d + f - C); } -function _H(r, e, t6) { - let o = Math.exp(e * t6 * t6); - return t6 <= r ? o : 0; +function CH(r, e, t10) { + let o = Math.exp(e * t10 * t10); + return t10 <= r ? o : 0; } -function q1(r, e) { +function _1(r, e) { return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex; } -async function EH(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = So(s, a, t6, o, n); - t6 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; - let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Vd(u, c, t6, o, n); - return s !== r && s.dispose(), a !== e && a.dispose(), mr(l, "int32"); +async function SH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = wo(s, a, t10, o, n); + t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; + let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Pd(u, c, t10, o, n); + return s !== r && s.dispose(), a !== e && a.dispose(), dr(l, "int32"); } -var K1 = EH; -function $H(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = So(a, i, t6, o, n, s); - t6 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = { boxes: a, scores: i }, c = { maxOutputSize: t6, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(_n, u, c); +var $1 = SH; +function wH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = wo(a, i, t10, o, n, s); + t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; + let u = { boxes: a, scores: i }, c = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(_n, u, c); return { selectedIndices: l[0], selectedScores: l[1] }; } -var j1 = N({ nonMaxSuppressionWithScore_: $H }); -async function AH(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = So(a, i, t6, o, n, s); - t6 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = Wd(c, l, t6, o, n, s); - return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: mr(m, "int32"), selectedScores: mr(d) }; +var E1 = N({ nonMaxSuppressionWithScore_: wH }); +async function IH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = wo(a, i, t10, o, n, s); + t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; + let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = Ld(c, l, t10, o, n, s); + return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: dr(m, "int32"), selectedScores: dr(d) }; } -var X1 = AH; -function RH(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = So(a, i, t6, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(ba, m, d); +var A1 = IH; +function vH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = wo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(ba, m, d); return { selectedIndices: f[0], validOutputs: f[1] }; } -var Y1 = N({ nonMaxSuppressionPadded_: RH }); -async function FH(r, e, t6, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = So(a, i, t6, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = zd(m, d, u, c, l, s); - return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: mr(f, "int32"), validOutputs: be(h, "int32") }; +var R1 = N({ nonMaxSuppressionPadded_: vH }); +async function kH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = wo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Md(m, d, u, c, l, s); + return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: dr(f, "int32"), validOutputs: Ce(h, "int32") }; } -var Q1 = FH; -function DH(r, e, t6 = false, o = false) { +var D1 = kH; +function NH(r, e, t10 = false, o = false) { let n = v(r, "images", "resizeBilinear"); - E(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), E(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), E(o === false || t6 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."); + $(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), $(o === false || t10 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."); let s = n, a = false; - n.rank === 3 && (a = true, s = z(n, [1, n.shape[0], n.shape[1], n.shape[2]])); - let [] = e, i = { images: s }, p = { alignCorners: t6, halfPixelCenters: o, size: e }, u = T.runKernel(Mn, i, p); - return a ? z(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; + n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); + let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(Mn, i, p); + return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; } -var Z1 = N({ resizeBilinear_: DH }); -function OH(r, e, t6 = false, o = false) { +var F1 = N({ resizeBilinear_: NH }); +function TH(r, e, t10 = false, o = false) { let n = v(r, "images", "resizeNearestNeighbor"); - E(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), E(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), E(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), E(o === false || t6 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); + $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || t10 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); let s = n, a = false; - n.rank === 3 && (a = true, s = z(n, [1, n.shape[0], n.shape[1], n.shape[2]])); - let [] = e, i = { images: s }, p = { alignCorners: t6, halfPixelCenters: o, size: e }, u = T.runKernel(Pn, i, p); - return a ? z(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; + n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); + let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(Pn, i, p); + return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; } -var J1 = N({ resizeNearestNeighbor_: OH }); -function PH(r, e = "binary", t6 = false, o = 0.5) { - let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = ae(mr([o]), 255), c, l, m, d; - if (E(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), E(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), E(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), E(e === "otsu" || e === "binary", () => `Method must be binary or otsu, but was ${e}`), n.shape[2] === 3) { +var O1 = N({ resizeNearestNeighbor_: TH }); +function _H(r, e = "binary", t10 = false, o = 0.5) { + let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = ne(dr([o]), 255), c, l, m, d; + if ($(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), $(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), $(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), $(e === "otsu" || e === "binary", () => `Method must be binary or otsu, but was ${e}`), n.shape[2] === 3) { [c, l, m] = Oa(n, [1, 1, 1], -1); - let g = ae(c, s), x = ae(l, a), b = ae(m, i); - d = xe(xe(g, x), b); + let g = ne(c, s), x = ne(l, a), b = ne(m, i); + d = be(be(g, x), b); } else d = r; if (e === "otsu") { - let g = od(Ke(Rd(d), "int32"), nr([]), 256); - u = MH(g, p); + let g = Xm(je(Nd(d), "int32"), nr([]), 256); + u = $H(g, p); } - let f = t6 ? Vp(d, u) : cu(d, u); - return Ke(ae(f, 255), "int32"); + let f = t10 ? Vp(d, u) : uu(d, u); + return je(ne(f, 255), "int32"); } -function MH(r, e) { - let t6 = mr([-1]), o = mr([0]), n = mr([0]), s, a, i, p, u, c; +function $H(r, e) { + let t10 = dr([-1]), o = dr([0]), n = dr([0]), s, a, i, p, u, c; for (let l = 0; l < r.size - 1; l++) { - s = He(r, 0, l + 1), a = He(r, l + 1), u = Ge(et(s), e), c = Ge(et(a), e); - let m = et(ae(s, Ni(0, s.size))); - i = Ge(m, et(s)); - let d = Ws(a.shape, s.size), f = xe(Ni(0, a.size), d), h = ae(a, f); - p = Ge(et(h), et(a)); - let g = Ne(i, p), x = Ne(i, p), b = ae(u, c); - n = ae(ae(b, g), x); - let C = cu(n, o); - o = os(C, n, o), t6 = os(C, mr([l]), t6); + s = Ke(r, 0, l + 1), a = Ke(r, l + 1), u = He(et(s), e), c = He(et(a), e); + let m = et(ne(s, Di(0, s.size))); + i = He(m, et(s)); + let d = Us(a.shape, s.size), f = be(Di(0, a.size), d), h = ne(a, f); + p = He(et(h), et(a)); + let g = ke(i, p), x = ke(i, p), b = ne(u, c); + n = ne(ne(b, g), x); + let C = uu(n, o); + o = ss(C, n, o), t10 = ss(C, dr([l]), t10); } - return t6; + return t10; } -var eN = N({ threshold_: PH }); -function LH(r, e, t6 = "nearest", o = "constant", n = 0, s) { +var P1 = N({ threshold_: _H }); +function EH(r, e, t10 = "nearest", o = "constant", n = 0, s) { let a = v(r, "image", "transform", "float32"), i = v(e, "transforms", "transform", "float32"); - E(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), E(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), E(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`); - let p = { image: a, transforms: i }, u = { interpolation: t6, fillMode: o, fillValue: n, outputShape: s }; - return T.runKernel(Jn, p, u); + $(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), $(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`); + let p = { image: a, transforms: i }, u = { interpolation: t10, fillMode: o, fillValue: n, outputShape: s }; + return T.runKernel(es, p, u); } -var tN = N({ transform_: LH }); -function BH(r, e, t6) { - E(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), E(t6 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t6}.`); +var M1 = N({ transform_: EH }); +function AH(r, e, t10) { + $(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), $(t10 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t10}.`); let o = v(r, "a", "bandPart"); - E(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`); + $(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`); let n = o.shape, [s, a] = o.shape.slice(-2); if (!(e <= s)) throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`); - if (!(t6 <= a)) - throw new Error(`bandPart(): numUpper (${t6}) must not be greater than the number of columns (${a}).`); - e < 0 && (e = s), t6 < 0 && (t6 = a); - let i = z(Ni(0, s, 1, "int32"), [-1, 1]), p = Ni(0, a, 1, "int32"), u = Ne(i, p), c = lu(Vp(u, be(+e, "int32")), cd(u, be(-t6, "int32"))), l = Vr([s, a], o.dtype); - return z(Sr(so(z(o, [-1, s, a])).map((m) => os(c, m, l))), n); + if (!(t10 <= a)) + throw new Error(`bandPart(): numUpper (${t10}) must not be greater than the number of columns (${a}).`); + e < 0 && (e = s), t10 < 0 && (t10 = a); + let i = W(Di(0, s, 1, "int32"), [-1, 1]), p = Di(0, a, 1, "int32"), u = ke(i, p), c = pu(Vp(u, Ce(+e, "int32")), od(u, Ce(-t10, "int32"))), l = Br([s, a], o.dtype); + return W(Sr(so(W(o, [-1, s, a])).map((m) => ss(c, m, l))), n); } -var rN = N({ bandPart_: BH }); -function VH(r) { +var L1 = N({ bandPart_: AH }); +function RH(r) { let e; if (Array.isArray(r)) { - e = false, E(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); + e = false, $(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); let n = r[0].shape[0]; for (let s = 1; s < r.length; ++s) - E(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`); + $(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`); } else e = true, r = Oa(r, r.shape[0], 0).map((n) => Up(n, [0])); - E(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`); - let t6 = [], o = r; + $(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`); + let t10 = [], o = r; for (let n = 0; n < r.length; ++n) - t6.push(T.tidy(() => { + t10.push(T.tidy(() => { let s = o[n]; if (n > 0) for (let a = 0; a < n; ++a) { - let i = ae(et(ae(t6[a], s)), t6[a]); - s = Ne(s, i); + let i = ne(et(ne(t10[a], s)), t10[a]); + s = ke(s, i); } - return Ge(s, pu(s, "euclidean")); + return He(s, iu(s, "euclidean")); })); - return e ? Sr(t6, 0) : t6; + return e ? Sr(t10, 0) : t10; } -var oN = N({ gramSchmidt_: VH }); -function zH(r, e = false) { - if (E(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2) - return nN(r, e); +var B1 = N({ gramSchmidt_: RH }); +function DH(r, e = false) { + if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2) + return V1(r, e); { - let t6 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = so(z(r, [t6, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = []; + let t10 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = so(W(r, [t10, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = []; o.forEach((p) => { - let [u, c] = nN(p, e); + let [u, c] = V1(p, e); n.push(u), s.push(c); }); - let a = z(Sr(n, 0), r.shape), i = z(Sr(s, 0), r.shape); + let a = W(Sr(n, 0), r.shape), i = W(Sr(s, 0), r.shape); return [a, i]; } } -function nN(r, e = false) { +function V1(r, e = false) { return T.tidy(() => { - E(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`); - let t6 = r.shape[0], o = r.shape[1], n = id(t6), s = Br(r), a = _i([[1]], [1, 1]), i = Br(a), p = t6 >= o ? o : t6; + $(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`); + let t10 = r.shape[0], o = r.shape[1], n = ed(t10), s = Lr(r), a = Oi([[1]], [1, 1]), i = Lr(a), p = t10 >= o ? o : t10; for (let u = 0; u < p; ++u) { let c = s, l = i, m = n; [i, s, n] = T.tidy(() => { - let d = He(s, [u, u], [t6 - u, 1]), f = pu(d), h = He(s, [u, u], [1, 1]), g = os(cu(h, 0), _i([[-1]]), _i([[1]])), x = Ne(h, ae(g, f)), b = Ge(d, x); - b.shape[0] === 1 ? i = Br(a) : i = gt([a, He(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); - let C = yr(Ge(Xe(g, x), f)), w = He(s, [u, 0], [t6 - u, o]), k = ae(C, i), _ = Mp(i); + let d = Ke(s, [u, u], [t10 - u, 1]), f = iu(d), h = Ke(s, [u, u], [1, 1]), g = ss(uu(h, 0), Oi([[-1]]), Oi([[1]])), x = ke(h, ne(g, f)), b = He(d, x); + b.shape[0] === 1 ? i = Lr(a) : i = xt([a, Ke(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); + let C = Cr(He(Xe(g, x), f)), w = Ke(s, [u, 0], [t10 - u, o]), k = ne(C, i), _ = Gp(i); if (u === 0) - s = Ne(w, Xe(k, Xe(_, w))); + s = ke(w, Xe(k, Xe(_, w))); else { - let R = Ne(w, Xe(k, Xe(_, w))); - s = gt([He(s, [0, 0], [u, o]), R], 0); + let R = ke(w, Xe(k, Xe(_, w))); + s = xt([Ke(s, [0, 0], [u, o]), R], 0); } - let $ = Mp(k), A = He(n, [0, u], [t6, n.shape[1] - u]); + let E = Gp(k), A = Ke(n, [0, u], [t10, n.shape[1] - u]); if (u === 0) - n = Ne(A, Xe(Xe(A, i), $)); + n = ke(A, Xe(Xe(A, i), E)); else { - let R = Ne(A, Xe(Xe(A, i), $)); - n = gt([He(n, [0, 0], [t6, u]), R], 1); + let R = ke(A, Xe(Xe(A, i), E)); + n = xt([Ke(n, [0, 0], [t10, u]), R], 1); } return [i, s, n]; - }), Dt([c, l, m]); + }), Ot([c, l, m]); } - return !e && t6 > o && (n = He(n, [0, 0], [t6, o]), s = He(s, [0, 0], [o, o])), [n, s]; + return !e && t10 > o && (n = Ke(n, [0, 0], [t10, o]), s = Ke(s, [0, 0], [o, o])), [n, s]; }); } -var sN = N({ qr_: zH }); -var Et; +var z1 = N({ qr_: DH }); +var $t; (function(r) { r[r.NONE = 0] = "NONE", r[r.MEAN = 1] = "MEAN", r[r.SUM = 2] = "SUM", r[r.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS"; -})(Et || (Et = {})); -function WH(r, e, t6 = Et.SUM_BY_NONZERO_WEIGHTS) { +})($t || ($t = {})); +function FH(r, e, t10 = $t.SUM_BY_NONZERO_WEIGHTS) { let o = v(r, "losses", "computeWeightedLoss"), n = null; e != null && (n = v(e, "weights", "computeWeightedLoss")); - let s = n == null ? o : ae(o, n); - if (t6 === Et.NONE) + let s = n == null ? o : ne(o, n); + if (t10 === $t.NONE) return s; - if (t6 === Et.SUM) + if (t10 === $t.SUM) return et(s); - if (t6 === Et.MEAN) { + if (t10 === $t.MEAN) { if (n == null) - return mu(s); + return cu(s); { - let a = o.size / n.size, i = Ge(et(s), et(n)); - return a > 1 ? Ge(i, be(a)) : i; + let a = o.size / n.size, i = He(et(s), et(n)); + return a > 1 ? He(i, Ce(a)) : i; } } - if (t6 === Et.SUM_BY_NONZERO_WEIGHTS) { + if (t10 === $t.SUM_BY_NONZERO_WEIGHTS) { if (n == null) - return Ge(et(s), be(o.size)); + return He(et(s), Ce(o.size)); { - let a = ae(n, Gs(o.shape)), i = Ke(et(wd(a, be(0))), "float32"); - return Ge(et(s), i); + let a = ne(n, Hs(o.shape)), i = je(et(hd(a, Ce(0))), "float32"); + return He(et(s), i); } } - throw Error(`Unknown reduction: ${t6}`); + throw Error(`Unknown reduction: ${t10}`); } -var sr = N({ computeWeightedLoss_: WH }); -function UH(r, e, t6, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var ar = N({ computeWeightedLoss_: FH }); +function OH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null; - t6 != null && (a = v(t6, "weights", "absoluteDifference")), ht(n.shape, s.shape, "Error in absoluteDifference: "); - let i = Yt(Ne(n, s)); - return sr(i, a, o); + t10 != null && (a = v(t10, "weights", "absoluteDifference")), gt(n.shape, s.shape, "Error in absoluteDifference: "); + let i = Yt(ke(n, s)); + return ar(i, a, o); } -var aN = N({ absoluteDifference_: UH }); -function GH(r, e, t6, o, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var W1 = N({ absoluteDifference_: OH }); +function PH(r, e, t10, o, n = $t.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "cosineDistance"), a = v(e, "predictions", "cosineDistance"), i = null; - o != null && (i = v(o, "weights", "cosineDistance")), ht(s.shape, a.shape, "Error in cosineDistance: "); - let p = be(1), u = Ne(p, et(ae(s, a), t6, true)); - return sr(u, i, n); + o != null && (i = v(o, "weights", "cosineDistance")), gt(s.shape, a.shape, "Error in cosineDistance: "); + let p = Ce(1), u = ke(p, et(ne(s, a), t10, true)); + return ar(u, i, n); } -var iN = N({ cosineDistance_: GH }); -function HH(r, e, t6, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var U1 = N({ cosineDistance_: PH }); +function MH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null; - t6 != null && (a = v(t6, "weights", "hingeLoss")), ht(n.shape, s.shape, "Error in hingeLoss: "); - let i = be(1); - n = Ne(ae(be(2), n), i); - let p = Ti(Ne(i, ae(n, s))); - return sr(p, a, o); + t10 != null && (a = v(t10, "weights", "hingeLoss")), gt(n.shape, s.shape, "Error in hingeLoss: "); + let i = Ce(1); + n = ke(ne(Ce(2), n), i); + let p = Fi(ke(i, ne(n, s))); + return ar(p, a, o); } -var uN = N({ hingeLoss_: HH }); -function qH(r, e, t6, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var G1 = N({ hingeLoss_: MH }); +function LH(r, e, t10, o = 1, n = $t.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null; - t6 != null && (i = v(t6, "weights", "huberLoss")), ht(s.shape, a.shape, "Error in huberLoss: "); - let p = be(o), u = Yt(Ne(a, s)), c = Sd(u, p), l = Ne(u, c), m = xe(ae(be(0.5), Qt(c)), ae(p, l)); - return sr(m, i, n); + t10 != null && (i = v(t10, "weights", "huberLoss")), gt(s.shape, a.shape, "Error in huberLoss: "); + let p = Ce(o), u = Yt(ke(a, s)), c = fd(u, p), l = ke(u, c), m = be(ne(Ce(0.5), Qt(c)), ne(p, l)); + return ar(m, i, n); } -var pN = N({ huberLoss_: qH }); -function KH(r, e, t6, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) { +var H1 = N({ huberLoss_: LH }); +function BH(r, e, t10, o = 1e-7, n = $t.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null; - t6 != null && (i = v(t6, "weights", "logLoss")), ht(s.shape, a.shape, "Error in logLoss: "); - let p = be(1), u = be(o), c = yr(ae(s, Da(xe(a, u)))), l = ae(Ne(p, s), Da(xe(Ne(p, a), u))), m = Ne(c, l); - return sr(m, i, n); + t10 != null && (i = v(t10, "weights", "logLoss")), gt(s.shape, a.shape, "Error in logLoss: "); + let p = Ce(1), u = Ce(o), c = Cr(ne(s, Da(be(a, u)))), l = ne(ke(p, s), Da(be(ke(p, a), u))), m = ke(c, l); + return ar(m, i, n); } -var cN = N({ logLoss_: KH }); -function jH(r, e, t6, o = Et.SUM_BY_NONZERO_WEIGHTS) { +var K1 = N({ logLoss_: BH }); +function VH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) { let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null; - t6 != null && (a = v(t6, "weights", "meanSquaredError")), ht(n.shape, s.shape, "Error in meanSquaredError: "); - let i = Dd(n, s); - return sr(i, a, o); + t10 != null && (a = v(t10, "weights", "meanSquaredError")), gt(n.shape, s.shape, "Error in meanSquaredError: "); + let i = _d(n, s); + return ar(i, a, o); } -var lN = N({ meanSquaredError_: jH }); -function XH(r, e) { - let t6 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits"); - ht(t6.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); - let n = Ti(o), s = ae(o, t6), a = md(Co(yr(Yt(o)))); - return xe(Ne(n, s), a); +var q1 = N({ meanSquaredError_: VH }); +function zH(r, e) { + let t10 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits"); + gt(t10.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); + let n = Fi(o), s = ne(o, t10), a = sd(So(Cr(Yt(o)))); + return be(ke(n, s), a); } -function YH(r, e, t6, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { +function WH(r, e, t10, o = 0, n = $t.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(e, "logits", "sigmoidCrossEntropy"), i = null; - if (t6 != null && (i = v(t6, "weights", "sigmoidCrossEntropy")), ht(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { - let u = be(o), c = be(1), l = be(0.5); - s = xe(ae(s, Ne(c, u)), ae(l, u)); + if (t10 != null && (i = v(t10, "weights", "sigmoidCrossEntropy")), gt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { + let u = Ce(o), c = Ce(1), l = Ce(0.5); + s = be(ne(s, ke(c, u)), ne(l, u)); } - let p = XH(s, a); - return sr(p, i, n); + let p = zH(s, a); + return ar(p, i, n); } -var mN = N({ sigmoidCrossEntropy_: YH }); -function QH(r, e, t6 = -1) { - if (t6 === -1 && (t6 = e.rank - 1), t6 !== e.rank - 1) - throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t6}`); - return Cr((n, s, a) => { - let p = hd(s, [t6], true), u = Ne(Ke(s, "float32"), p); +var j1 = N({ sigmoidCrossEntropy_: WH }); +function UH(r, e, t10 = -1) { + if (t10 === -1 && (t10 = e.rank - 1), t10 !== e.rank - 1) + throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t10}`); + return br((n, s, a) => { + let p = ud(s, [t10], true), u = ke(je(s, "float32"), p); a([n, u]); - let c = yr(ae(u, n)); - return { value: et(c, [t6]), gradFunc: (d, f) => { - let [h, g] = f, x = Aa(d.shape, [t6]); - return [ae(z(d, x), Ne(Ke(h, "float32"), Co(g))), ae(z(d, x), Ne(Co(g), Ke(h, "float32")))]; + let c = Cr(ne(u, n)); + return { value: et(c, [t10]), gradFunc: (d, f) => { + let [h, g] = f, x = Ea(d.shape, [t10]); + return [ne(W(d, x), ke(je(h, "float32"), So(g))), ne(W(d, x), ke(So(g), je(h, "float32")))]; } }; })(r, e); } -function ZH(r, e, t6, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { +function GH(r, e, t10, o = 0, n = $t.SUM_BY_NONZERO_WEIGHTS) { let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(e, "logits", "softmaxCrossEntropy"), i = null; - if (t6 != null && (i = v(t6, "weights", "softmaxCrossEntropy")), ht(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { - let u = be(o), c = be(1), l = be(s.shape[1]); - s = xe(ae(s, Ne(c, u)), Ge(u, l)); + if (t10 != null && (i = v(t10, "weights", "softmaxCrossEntropy")), gt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { + let u = Ce(o), c = Ce(1), l = Ce(s.shape[1]); + s = be(ne(s, ke(c, u)), He(u, l)); } - let p = QH(s, a); - return sr(p, i, n); + let p = UH(s, a); + return ar(p, i, n); } -var dN = N({ softmaxCrossEntropy_: ZH }); -function JH(r, e, t6, o) { - let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t6, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); +var X1 = N({ softmaxCrossEntropy_: GH }); +function HH(r, e, t10, o) { + let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t10, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); if (n.rank !== 2) throw new Error(`Indices should be Tensor2D but received shape ${n.shape}`); @@ -8536,12 +7876,12 @@ function JH(r, e, t6, o) { throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`); if (i.rank !== 0) throw new Error(`Default value should be a scalar but received shape ${i.shape}`); - let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(ui, p); + let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(hi, p); return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] }; } -var fN = N({ sparseFillEmptyRows_: JH }); -function eq(r, e, t6) { - let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t6, "newShape", "sparseReshape", "int32"); +var Y1 = N({ sparseFillEmptyRows_: HH }); +function KH(r, e, t10) { + let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t10, "newShape", "sparseReshape", "int32"); if (o.rank !== 2) throw new Error(`Input indices should be Tensor2D but received shape ${o.shape}`); @@ -8549,12 +7889,12 @@ function eq(r, e, t6) { throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`); if (s.rank !== 1) throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`); - let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(wa, a); + let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(va, a); return { outputIndices: i[0], outputShape: i[1] }; } -var hN = N({ sparseReshape_: eq }); -function tq(r, e, t6) { - let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t6, "segmentIds", "sparseSegmentMean", "int32"); +var Q1 = N({ sparseReshape_: KH }); +function qH(r, e, t10) { + let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t10, "segmentIds", "sparseSegmentMean", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8564,11 +7904,11 @@ function tq(r, e, t6) { throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`); let a = { data: o, indices: n, segmentIds: s }; - return T.runKernel(pi, a); + return T.runKernel(gi, a); } -var gN = N({ sparseSegmentMean_: tq }); -function rq(r, e, t6) { - let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t6, "segmentIds", "sparseSegmentSum", "int32"); +var Z1 = N({ sparseSegmentMean_: qH }); +function jH(r, e, t10) { + let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t10, "segmentIds", "sparseSegmentSum", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8578,10 +7918,10 @@ function rq(r, e, t6) { throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`); let a = { data: o, indices: n, segmentIds: s }; - return T.runKernel(ci, a); + return T.runKernel(xi, a); } -var xN = N({ sparseSegmentSum_: rq }); -function oq(r, e, t6, o, n, s, a, i) { +var J1 = N({ sparseSegmentSum_: jH }); +function XH(r, e, t10, o, n, s, a, i) { let p = v(r, "data", "stringNGrams", "string"); if (p.dtype !== "string") throw new Error("Data must be of datatype string"); @@ -8590,44 +7930,68 @@ function oq(r, e, t6, o, n, s, a, i) { let u = v(e, "dataSplits", "stringNGrams"); if (u.dtype !== "int32") throw new Error("Data splits must be of datatype int32"); - let c = { separator: t6, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(As, l, c); + let c = { separator: t10, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(Ds, l, c); return { nGrams: m[0], nGramsSplits: m[1] }; } -var yN = N({ stringNGrams_: oq }); -function nq(r, e, t6 = true) { +var eN = N({ stringNGrams_: XH }); +function YH(r, e, t10 = true) { let o = v(r, "input", "stringSplit", "string"), n = v(e, "delimiter", "stringSplit", "string"); if (o.rank !== 1) throw new Error(`Input should be Tensor1D but received shape ${o.shape}`); if (n.rank !== 0) throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`); - let s = { skipEmpty: t6 }, a = { input: o, delimiter: n }, i = T.runKernel(di, a, s); + let s = { skipEmpty: t10 }, a = { input: o, delimiter: n }, i = T.runKernel(Ci, a, s); return { indices: i[0], values: i[1], shape: i[2] }; } -var bN = N({ stringSplit_: nq }); -function sq(r, e) { - let t6 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e }; +var tN = N({ stringSplit_: YH }); +function QH(r, e) { + let t10 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e }; if (e <= 0) throw new Error("Number of buckets must be at least 1"); - let n = { input: t6 }; - return T.runKernel(fi, n, o); + let n = { input: t10 }; + return T.runKernel(Si, n, o); } -var CN = N({ stringToHashBucketFast_: sq }); -var aq = { fft: zp, ifft: hu, rfft: Wp, irfft: Fd }; -var iq = { hammingWindow: L1, hannWindow: Ld, frame: Bd, stft: B1 }; -var uq = { flipLeftRight: z1, grayscaleToRGB: W1, resizeNearestNeighbor: J1, resizeBilinear: Z1, rotateWithOffset: U1, cropAndResize: V1, nonMaxSuppression: G1, nonMaxSuppressionAsync: K1, nonMaxSuppressionWithScore: j1, nonMaxSuppressionWithScoreAsync: X1, nonMaxSuppressionPadded: Y1, nonMaxSuppressionPaddedAsync: Q1, threshold: eN, transform: tN }; -var pq = { bandPart: rN, gramSchmidt: oN, qr: sN }; -var cq = { absoluteDifference: aN, computeWeightedLoss: sr, cosineDistance: iN, hingeLoss: uN, huberLoss: pN, logLoss: cN, meanSquaredError: lN, sigmoidCrossEntropy: mN, softmaxCrossEntropy: dN }; -var lq = { sparseFillEmptyRows: fN, sparseReshape: hN, sparseSegmentMean: gN, sparseSegmentSum: xN }; -var mq = { stringNGrams: yN, stringSplit: bN, stringToHashBucketFast: CN }; -var wr = class extends ol { - minimize(e, t6 = false, o) { +var rN = N({ stringToHashBucketFast_: QH }); +var ZH = { fft: zp, ifft: du, rfft: Wp, irfft: Td }; +var JH = { hammingWindow: C1, hannWindow: Fd, frame: Od, stft: S1 }; +var eK = { flipLeftRight: I1, grayscaleToRGB: v1, resizeNearestNeighbor: O1, resizeBilinear: F1, rotateWithOffset: k1, cropAndResize: w1, nonMaxSuppression: N1, nonMaxSuppressionAsync: $1, nonMaxSuppressionWithScore: E1, nonMaxSuppressionWithScoreAsync: A1, nonMaxSuppressionPadded: R1, nonMaxSuppressionPaddedAsync: D1, threshold: P1, transform: M1 }; +var tK = { bandPart: L1, gramSchmidt: B1, qr: z1 }; +var rK = { absoluteDifference: W1, computeWeightedLoss: ar, cosineDistance: U1, hingeLoss: G1, huberLoss: H1, logLoss: K1, meanSquaredError: q1, sigmoidCrossEntropy: j1, softmaxCrossEntropy: X1 }; +var oK = { sparseFillEmptyRows: Y1, sparseReshape: Q1, sparseSegmentMean: Z1, sparseSegmentSum: J1 }; +var nK = { stringNGrams: eN, stringSplit: tN, stringToHashBucketFast: rN }; +var oN = {}; +Ge(oN, { Serializable: () => il, SerializationMap: () => qs, registerClass: () => IC }); +var il = class { + getClassName() { + return this.constructor.className; + } + static fromConfig(e, t10) { + return new e(t10); + } +}; +var qs = class { + constructor() { + this.classNameMap = {}; + } + static getMap() { + return qs.instance == null && (qs.instance = new qs()), qs.instance; + } + static register(e) { + qs.getMap().classNameMap[e.className] = [e, e.fromConfig]; + } +}; +function IC(r) { + $(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), qs.register(r); +} +var wr = class extends il { + minimize(e, t10 = false, o) { let { value: n, grads: s } = this.computeGradients(e, o); if (o != null) { let a = o.map((i) => ({ name: i.name, tensor: s[i.name] })); this.applyGradients(a); } else this.applyGradients(s); - return Dt(s), t6 ? n : (n.dispose(), null); + return Ot(s), t10 ? n : (n.dispose(), null); } get iterations() { return this.iterations_ == null && (this.iterations_ = 0), this.iterations_; @@ -8635,14 +7999,14 @@ var wr = class extends ol { incrementIterations() { this.iterations_ = this.iterations + 1; } - computeGradients(e, t6) { - return pC(e, t6); + computeGradients(e, t10) { + return pC(e, t10); } dispose() { - this.iterations_ != null && Dt(this.iterations_); + this.iterations_ != null && Ot(this.iterations_); } async saveIterations() { - return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: be(this.iterations_, "int32") }; + return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: Ce(this.iterations_, "int32") }; } async getWeights() { throw new Error("getWeights() is not implemented for this optimizer yet."); @@ -8655,9 +8019,12 @@ var wr = class extends ol { } }; Object.defineProperty(wr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null }); -var Ei = class extends wr { - constructor(e, t6, o = null) { - super(), this.learningRate = e, this.rho = t6, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon()); +var yu = class extends wr { + constructor(e, t10, o = null) { + super(), this.learningRate = e, this.rho = t10, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon()); + } + static get className() { + return "Adadelta"; } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { @@ -8668,145 +8035,148 @@ var Ei = class extends wr { return; let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable; Ee(() => { - let c = xe(ae(p, this.rho), ae(Qt(i), 1 - this.rho)), l = ae(Ge($r(xe(u, this.epsilon)), $r(xe(p, this.epsilon))), i), m = xe(ae(u, this.rho), ae(Qt(l), 1 - this.rho)); + let c = be(ne(p, this.rho), ne(Qt(i), 1 - this.rho)), l = ne(He($r(be(u, this.epsilon)), $r(be(p, this.epsilon))), i), m = be(ne(u, this.rho), ne(Qt(l), 1 - this.rho)); p.assign(c), u.assign(m); - let d = xe(ae(l, -this.learningRate), s); + let d = be(ne(l, -this.learningRate), s); s.assign(d); }); }), this.incrementIterations(); } dispose() { - this.accumulatedUpdates != null && (Dt(this.accumulatedGrads.map((e) => e.variable)), Dt(this.accumulatedUpdates.map((e) => e.variable))); + this.accumulatedUpdates != null && (Ot(this.accumulatedGrads.map((e) => e.variable)), Ot(this.accumulatedUpdates.map((e) => e.variable))); } async getWeights() { let e = [...this.accumulatedGrads, ...this.accumulatedUpdates]; - return [await this.saveIterations()].concat(e.map((t6) => ({ name: t6.originalName, tensor: t6.variable }))); + return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); } async setWeights(e) { e = await this.extractIterations(e); - let t6 = e.length / 2, o = false; - this.accumulatedGrads = e.slice(0, t6).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t6, t6 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); + let t10 = e.length / 2, o = false; + this.accumulatedGrads = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); } getConfig() { return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.rho, t6.epsilon); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.rho, t10.epsilon); } }; -Ei.className = "Adadelta"; -Er(Ei); -var $i = class extends wr { - constructor(e, t6 = 0.1) { - super(), this.learningRate = e, this.initialAccumulatorValue = t6, this.accumulatedGrads = []; +var bu = class extends wr { + constructor(e, t10 = 0.1) { + super(), this.learningRate = e, this.initialAccumulatorValue = t10, this.accumulatedGrads = []; + } + static get className() { + return "Adagrad"; } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { let s = T.registeredVariables[o]; - this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: Ee(() => Ws(s.shape, this.initialAccumulatorValue).variable(false)) }); + this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: Ee(() => Us(s.shape, this.initialAccumulatorValue).variable(false)) }); let a = Array.isArray(e) ? e[n].tensor : e[o]; if (a == null) return; let i = this.accumulatedGrads[n].variable; Ee(() => { - let p = xe(i, Qt(a)); + let p = be(i, Qt(a)); i.assign(p); - let u = xe(ae(Ge(a, $r(xe(p, T.backend.epsilon()))), -this.learningRate), s); + let u = be(ne(He(a, $r(be(p, T.backend.epsilon()))), -this.learningRate), s); s.assign(u); }); }), this.incrementIterations(); } dispose() { - this.accumulatedGrads != null && Dt(this.accumulatedGrads.map((e) => e.variable)); + this.accumulatedGrads != null && Ot(this.accumulatedGrads.map((e) => e.variable)); } async getWeights() { return [await this.saveIterations()].concat(this.accumulatedGrads.map((e) => ({ name: e.originalName, tensor: e.variable }))); } async setWeights(e) { e = await this.extractIterations(e); - let t6 = false; - this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t6) })); + let t10 = false; + this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) })); } getConfig() { return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.initialAccumulatorValue); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.initialAccumulatorValue); } }; -$i.className = "Adagrad"; -Er($i); -var Ai = class extends wr { - constructor(e, t6, o, n = null) { - super(), this.learningRate = e, this.beta1 = t6, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], Ee(() => { - this.accBeta1 = be(t6).variable(), this.accBeta2 = be(o).variable(); +var Cu = class extends wr { + constructor(e, t10, o, n = null) { + super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], Ee(() => { + this.accBeta1 = Ce(t10).variable(), this.accBeta2 = Ce(o).variable(); }), n == null && (this.epsilon = T.backend.epsilon()); } + static get className() { + return "Adam"; + } applyGradients(e) { - let t6 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); + let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); Ee(() => { - let o = Ne(1, this.accBeta1), n = Ne(1, this.accBeta2); - t6.forEach((s, a) => { + let o = ke(1, this.accBeta1), n = ke(1, this.accBeta2); + t10.forEach((s, a) => { let i = T.registeredVariables[s], p = false; this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ee(() => Ut(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: Ee(() => Ut(i).variable(p)) }); let u = Array.isArray(e) ? e[a].tensor : e[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = xe(ae(c, this.beta1), ae(u, 1 - this.beta1)), d = xe(ae(l, this.beta2), ae(Qt(u), 1 - this.beta2)), f = Ge(m, o), h = Ge(d, n); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = be(ne(c, this.beta1), ne(u, 1 - this.beta1)), d = be(ne(l, this.beta2), ne(Qt(u), 1 - this.beta2)), f = He(m, o), h = He(d, n); c.assign(m), l.assign(d); - let g = xe(ae(Ge(f, xe($r(h), this.epsilon)), -this.learningRate), i); + let g = be(ne(He(f, be($r(h), this.epsilon)), -this.learningRate), i); i.assign(g); - }), this.accBeta1.assign(ae(this.accBeta1, this.beta1)), this.accBeta2.assign(ae(this.accBeta2, this.beta2)); + }), this.accBeta1.assign(ne(this.accBeta1, this.beta1)), this.accBeta2.assign(ne(this.accBeta2, this.beta2)); }), this.incrementIterations(); } dispose() { - this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Dt(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Dt(this.accumulatedSecondMoment.map((e) => e.variable)); + this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Ot(this.accumulatedSecondMoment.map((e) => e.variable)); } async getWeights() { let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment]; - return [await this.saveIterations()].concat(e.map((t6) => ({ name: t6.originalName, tensor: t6.variable }))); + return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); } async setWeights(e) { e = await this.extractIterations(e), Ee(() => { - this.accBeta1.assign(Ra(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(Ra(this.beta2, this.iterations_ + 1)); + this.accBeta1.assign(Aa(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(Aa(this.beta2, this.iterations_ + 1)); }); - let t6 = e.length / 2, o = false; - this.accumulatedFirstMoment = e.slice(0, t6).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t6, t6 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); + let t10 = e.length / 2, o = false; + this.accumulatedFirstMoment = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); } getConfig() { return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.beta1, t6.beta2, t6.epsilon); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon); } }; -Ai.className = "Adam"; -Er(Ai); -var Ri = class extends wr { - constructor(e, t6, o, n = null, s = 0) { - super(), this.learningRate = e, this.beta1 = t6, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], Ee(() => { - this.iteration = be(0).variable(), this.accBeta1 = be(t6).variable(); +var Su = class extends wr { + constructor(e, t10, o, n = null, s = 0) { + super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], Ee(() => { + this.iteration = Ce(0).variable(), this.accBeta1 = Ce(t10).variable(); }), n == null && (this.epsilon = T.backend.epsilon()); } + static get className() { + return "Adamax"; + } applyGradients(e) { - let t6 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); + let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e); Ee(() => { - let o = Ne(1, this.accBeta1), n = Ge(-this.learningRate, xe(ae(this.iteration, this.decay), 1)); - t6.forEach((s, a) => { + let o = ke(1, this.accBeta1), n = He(-this.learningRate, be(ne(this.iteration, this.decay), 1)); + t10.forEach((s, a) => { let i = T.registeredVariables[s], p = false; this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ut(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Ut(i).variable(p) }); let u = Array.isArray(e) ? e[a].tensor : e[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = xe(ae(c, this.beta1), ae(u, 1 - this.beta1)), d = ae(l, this.beta2), f = Yt(u), h = Cd(d, f); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = be(ne(c, this.beta1), ne(u, 1 - this.beta1)), d = ne(l, this.beta2), f = Yt(u), h = dd(d, f); c.assign(m), l.assign(h); - let g = xe(ae(Ge(n, o), Ge(m, xe(h, this.epsilon))), i); + let g = be(ne(He(n, o), He(m, be(h, this.epsilon))), i); i.assign(g); - }), this.iteration.assign(xe(this.iteration, 1)), this.accBeta1.assign(ae(this.accBeta1, this.beta1)); + }), this.iteration.assign(be(this.iteration, 1)), this.accBeta1.assign(ne(this.accBeta1, this.beta1)); }), this.incrementIterations(); } dispose() { - this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Dt(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Dt(this.accumulatedWeightedInfNorm.map((e) => e.variable)); + this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Ot(this.accumulatedWeightedInfNorm.map((e) => e.variable)); } async getWeights() { throw new Error("getWeights() is not implemented for Adamax yet."); @@ -8817,16 +8187,17 @@ var Ri = class extends wr { getConfig() { return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon, decay: this.decay }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.beta1, t6.beta2, t6.epsilon, t6.decay); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon, t10.decay); } }; -Ri.className = "Adamax"; -Er(Ri); -var qs = class extends wr { +var Pa = class extends wr { constructor(e) { super(), this.learningRate = e, this.setLearningRate(e); } + static get className() { + return "SGD"; + } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { let s = Array.isArray(e) ? e[n].tensor : e[o]; @@ -8834,13 +8205,13 @@ var qs = class extends wr { return; let a = T.registeredVariables[o]; Ee(() => { - let i = xe(ae(this.c, s), a); + let i = be(ne(this.c, s), a); a.assign(i); }); }), this.incrementIterations(); } setLearningRate(e) { - this.learningRate = e, this.c != null && this.c.dispose(), this.c = _r(be(-e)); + this.learningRate = e, this.c != null && this.c.dispose(), this.c = _r(Ce(-e)); } dispose() { this.c.dispose(); @@ -8855,15 +8226,16 @@ var qs = class extends wr { getConfig() { return { learningRate: this.learningRate }; } - static fromConfig(e, t6) { - return new e(t6.learningRate); + static fromConfig(e, t10) { + return new e(t10.learningRate); } }; -qs.className = "SGD"; -Er(qs); -var Fi = class extends qs { - constructor(e, t6, o = false) { - super(e), this.learningRate = e, this.momentum = t6, this.useNesterov = o, this.accumulations = [], this.m = be(this.momentum); +var wu = class extends Pa { + constructor(e, t10, o = false) { + super(e), this.learningRate = e, this.momentum = t10, this.useNesterov = o, this.accumulations = [], this.m = Ce(this.momentum); + } + static get className() { + return "Momentum"; } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { @@ -8871,13 +8243,13 @@ var Fi = class extends qs { this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: Ee(() => Ut(s).variable(false)) }); let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o]; i != null && Ee(() => { - let p, u = xe(ae(this.m, a), i); - this.useNesterov ? p = xe(ae(this.c, xe(i, ae(u, this.m))), s) : p = xe(ae(this.c, u), s), a.assign(u), s.assign(p); + let p, u = be(ne(this.m, a), i); + this.useNesterov ? p = be(ne(this.c, be(i, ne(u, this.m))), s) : p = be(ne(this.c, u), s), a.assign(u), s.assign(p); }); }), this.incrementIterations(); } dispose() { - this.m.dispose(), this.accumulations != null && Dt(this.accumulations.map((e) => e.variable)); + this.m.dispose(), this.accumulations != null && Ot(this.accumulations.map((e) => e.variable)); } setMomentum(e) { this.momentum = e; @@ -8887,23 +8259,24 @@ var Fi = class extends qs { } async setWeights(e) { e = await this.extractIterations(e); - let t6 = false; - this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t6) })); + let t10 = false; + this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) })); } getConfig() { return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.momentum, t6.useNesterov); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.momentum, t10.useNesterov); } }; -Fi.className = "Momentum"; -Er(Fi); -var Di = class extends wr { - constructor(e, t6 = 0.9, o = 0, n = null, s = false) { - if (super(), this.learningRate = e, this.decay = t6, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null) +var Iu = class extends wr { + constructor(e, t10 = 0.9, o = 0, n = null, s = false) { + if (super(), this.learningRate = e, this.decay = t10, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null) throw new Error("learningRate for RMSPropOptimizer must be defined."); } + static get className() { + return "RMSProp"; + } applyGradients(e) { (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => { let s = T.registeredVariables[o], a = false; @@ -8913,108 +8286,723 @@ var Di = class extends wr { return; let p = this.accumulatedMeanSquares[n].variable, u = this.accumulatedMoments[n].variable; Ee(() => { - let c = xe(ae(p, this.decay), ae(Qt(i), 1 - this.decay)); + let c = be(ne(p, this.decay), ne(Qt(i), 1 - this.decay)); if (this.centered) { - let l = this.accumulatedMeanGrads[n].variable, m = xe(ae(l, this.decay), ae(i, 1 - this.decay)), d = Ge(ae(i, this.learningRate), $r(Ne(c, xe(Qt(m), this.epsilon)))), f = xe(ae(u, this.momentum), d); + let l = this.accumulatedMeanGrads[n].variable, m = be(ne(l, this.decay), ne(i, 1 - this.decay)), d = He(ne(i, this.learningRate), $r(ke(c, be(Qt(m), this.epsilon)))), f = be(ne(u, this.momentum), d); p.assign(c), l.assign(m), u.assign(f); - let h = Ne(s, f); + let h = ke(s, f); s.assign(h); } else { - let l = xe(ae(p, this.decay), ae(Qt(i), 1 - this.decay)), m = xe(ae(u, this.momentum), Ge(ae(i, this.learningRate), $r(xe(l, this.epsilon)))); + let l = be(ne(p, this.decay), ne(Qt(i), 1 - this.decay)), m = be(ne(u, this.momentum), He(ne(i, this.learningRate), $r(be(l, this.epsilon)))); p.assign(l), u.assign(m); - let d = Ne(s, m); + let d = ke(s, m); s.assign(d); } }); }), this.incrementIterations(); } dispose() { - this.accumulatedMeanSquares != null && Dt(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Dt(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Dt(this.accumulatedMoments.map((e) => e.variable)); + this.accumulatedMeanSquares != null && Ot(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Ot(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Ot(this.accumulatedMoments.map((e) => e.variable)); } async getWeights() { let e = [...this.accumulatedMeanSquares, ...this.accumulatedMoments]; - return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t6) => ({ name: t6.originalName, tensor: t6.variable }))); + return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable }))); } async setWeights(e) { e = await this.extractIterations(e); - let t6 = this.centered ? e.length / 3 : e.length / 2, o = false; - this.accumulatedMeanSquares = e.slice(0, t6).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t6, t6 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t6 * 2, t6 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }))); + let t10 = this.centered ? e.length / 3 : e.length / 2, o = false; + this.accumulatedMeanSquares = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t10 * 2, t10 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }))); } getConfig() { return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered }; } - static fromConfig(e, t6) { - return new e(t6.learningRate, t6.decay, t6.momentum, t6.epsilon, t6.centered); + static fromConfig(e, t10) { + return new e(t10.learningRate, t10.decay, t10.momentum, t10.epsilon, t10.centered); } }; -Di.className = "RMSProp"; -Er(Di); -var ns = class { +var sK = [yu, bu, Cu, Su, wu, Iu, Pa]; +function nN() { + for (let r of sK) + IC(r); +} +var La = {}; +Ge(La, { browserFiles: () => aN, browserHTTPRequest: () => pN, concatenateArrayBuffers: () => Fp, copyModel: () => V0, decodeWeights: () => Bm, encodeWeights: () => v0, fromMemory: () => cN, fromMemorySync: () => $C, getLoadHandlers: () => E0, getModelArtifactsForJSON: () => Op, getModelArtifactsForJSONSync: () => Qb, getModelArtifactsInfoForJSON: () => Ms, getSaveHandlers: () => $0, getWeightSpecs: () => zm, http: () => Vd, isHTTPScheme: () => Bd, listModels: () => L0, loadWeights: () => iN, moveModel: () => z0, registerLoadRouter: () => _0, registerSaveRouter: () => T0, removeModel: () => B0, weightsLoaderFactory: () => TC, withSaveHandler: () => lN, withSaveHandlerSync: () => mN }); +var aK = "model"; +var iK = ".json"; +var uK = ".weights.bin"; +function sN(r) { + return new Promise((e) => setTimeout(e)).then(r); +} +var Ma = class { + constructor(e) { + if (!O().getBool("IS_BROWSER")) + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + e.startsWith(Ma.URL_SCHEME) && (e = e.slice(Ma.URL_SCHEME.length)), (e == null || e.length === 0) && (e = aK), this.modelJsonFileName = e + iK, this.weightDataFileName = e + uK; + } + async save(e) { + if (typeof document == "undefined") + throw new Error("Browser downloads are not supported in this environment since `document` is not present"); + let t10 = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" })); + if (e.modelTopology instanceof ArrayBuffer) + throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); + { + let o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = Vm(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + if (a.download = this.modelJsonFileName, a.href = s, await sN(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { + let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + i.download = this.weightDataFileName, i.href = t10, await sN(() => i.dispatchEvent(new MouseEvent("click"))); + } + return { modelArtifactsInfo: Ms(e) }; + } + } +}; +Ma.URL_SCHEME = "downloads://"; +var vC = class { + constructor(e) { + if (e == null || e.length < 1) + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`); + this.jsonFile = e[0], this.weightsFiles = e.slice(1); + } + async load() { + return new Promise((e, t10) => { + let o = new FileReader(); + o.onload = (n) => { + let s = JSON.parse(n.target.result), a = s.modelTopology; + if (a == null) { + t10(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + return; + } + if (s.weightsManifest == null) { + t10(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + return; + } + if (this.weightsFiles.length === 0) { + e({ modelTopology: a }); + return; + } + let p = Op(s, (u) => this.loadWeights(u)); + e(p); + }, o.onerror = (n) => t10(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile); + }); + } + loadWeights(e) { + let t10 = [], o = []; + for (let a of e) + t10.push(...a.weights), o.push(...a.paths); + let n = this.checkManifestAndWeightFiles(e), s = o.map((a) => this.loadWeightsFile(a, n[a])); + return Promise.all(s).then((a) => [t10, Fp(a)]); + } + loadWeightsFile(e, t10) { + return new Promise((o, n) => { + let s = new FileReader(); + s.onload = (a) => { + let i = a.target.result; + o(i); + }, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t10); + }); + } + checkManifestAndWeightFiles(e) { + let t10 = [], o = this.weightsFiles.map((s) => Yb(s.name)), n = {}; + for (let s of e) + s.paths.forEach((a) => { + let i = Yb(a); + if (t10.indexOf(i) !== -1) + throw new Error(`Duplicate file basename found in weights manifest: '${i}'`); + if (t10.push(i), o.indexOf(i) === -1) + throw new Error(`Weight file with basename '${i}' is not provided.`); + n[a] = this.weightsFiles[o.indexOf(i)]; + }); + if (t10.length !== this.weightsFiles.length) + throw new Error(`Mismatch in the number of files in weights manifest (${t10.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + return n; + } +}; +var pK = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ma.URL_SCHEME) ? cK(r.slice(Ma.URL_SCHEME.length)) : null; +mt.registerSaveRouter(pK); +function cK(r = "model") { + return new Ma(r); +} +function aN(r) { + return new vC(r); +} +function kC(r, e, t10, o) { + a(r), t10 = t10 == null ? 0 : t10, o = o == null ? 1 : o, i(t10, o); + let n = 0, s = (p) => (p.then((u) => { + let c = t10 + ++n / r.length * (o - t10); + return e(c), u; + }), p); + function a(p) { + $(p != null && Array.isArray(p) && p.length > 0, () => "promises must be a none empty array"); + } + function i(p, u) { + $(p >= 0 && p <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${p}`), $(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${u}`), $(u >= p, () => `startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`); + } + return Promise.all(r.map(s)); +} +async function NC(r, e) { + e == null && (e = {}); + let t10 = e.fetchFunc == null ? O().platform.fetch : e.fetchFunc, o = r.map((l) => t10(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await kC(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; + return e.onProgress == null ? await Promise.all(i) : await kC(i, e.onProgress, p, u); +} +async function iN(r, e = "", t10, o) { + return TC((a) => NC(a, { requestInit: o }))(r, e, t10); +} +function TC(r) { + return async (e, t10 = "", o) => { + let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = []; + if (e.forEach((d, f) => { + let h = 0; + d.weights.forEach((g) => { + let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Zc[x] * We(g.shape), C = () => { + n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b }); + }; + o != null ? o.forEach((w, k) => { + w === g.name && (C(), a[k] = true); + }) : C(), i.push(g.name), h += b; + }); + }), !a.every((d) => d)) { + let d = o.filter((f, h) => !a[h]); + throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}. +Manifest JSON has weights with names: ${i.join(", ")}.`); + } + let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = []; + p.forEach((d) => { + e[d].paths.forEach((f) => { + let h = t10 + (t10.endsWith("/") ? "" : "/") + f; + u.push(h); + }); + }); + let c = await r(u), l = {}, m = 0; + return p.forEach((d) => { + let f = e[d].paths.length, h = 0; + for (let w = 0; w < f; w++) + h += c[m + w].byteLength; + let g = new ArrayBuffer(h), x = new Uint8Array(g), b = 0; + for (let w = 0; w < f; w++) { + let k = new Uint8Array(c[m + w]); + x.set(k, b), b += k.byteLength; + } + s[d].forEach((w) => { + let k = g.slice(w.groupOffset, w.groupOffset + w.sizeBytes), _ = Bm(k, [w.manifestEntry]); + for (let E in _) + l[E] = _[E]; + }), m += f; + }), l; + }; +} +var lK = "application/octet-stream"; +var mK = "application/json"; +var ul = class { + constructor(e, t10) { + if (this.DEFAULT_METHOD = "POST", t10 == null && (t10 = {}), this.weightPathPrefix = t10.weightPathPrefix, this.onProgress = t10.onProgress, this.weightUrlConverter = t10.weightUrlConverter, t10.fetchFunc != null ? ($(typeof t10.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t10.fetchFunc) : this.fetch = O().platform.fetch, $(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && $(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t10.requestInit != null && t10.requestInit.body != null) + throw new Error("requestInit is expected to have no pre-existing body, but has one."); + this.requestInit = t10.requestInit || {}; + } + async save(e) { + if (e.modelTopology instanceof ArrayBuffer) + throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); + let t10 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + t10.body = new FormData(); + let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = Vm(e, o); + t10.body.append("model.json", new Blob([JSON.stringify(n)], { type: mK }), "model.json"), e.weightData != null && t10.body.append("model.weights.bin", new Blob([e.weightData], { type: lK }), "model.weights.bin"); + let s = await this.fetch(this.path, t10); + if (s.ok) + return { modelArtifactsInfo: Ms(e), responses: [s] }; + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`); + } + async load() { + let e = await this.fetch(this.path, this.requestInit); + if (!e.ok) + throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`); + let t10; + try { + t10 = await e.json(); + } catch (s) { + let a = `Failed to parse model JSON of response from ${this.path}.`; + throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a); + } + let o = t10.modelTopology, n = t10.weightsManifest; + if (o == null && n == null) + throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); + return Op(t10, (s) => this.loadWeights(s)); + } + async loadWeights(e) { + let t10 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = dK(t10), s = this.weightPathPrefix || o, a = zm(e), i = [], p = []; + for (let c of e) + for (let l of c.paths) + this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n); + this.weightUrlConverter && i.push(...await Promise.all(p)); + let u = await NC(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress }); + return [a, Fp(u)]; + } +}; +ul.URL_SCHEME_REGEX = /^https?:\/\//; +function dK(r) { + let e = r.lastIndexOf("/"), t10 = r.lastIndexOf("?"), o = r.substring(0, e), n = t10 > e ? r.substring(t10) : ""; + return [o + "/", n]; +} +function Bd(r) { + return r.match(ul.URL_SCHEME_REGEX) != null; +} +var uN = (r, e) => { + if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null)) + return null; + { + let t10 = true; + if (Array.isArray(r) ? t10 = r.every((o) => Bd(o)) : t10 = Bd(r), t10) + return Vd(r, e); + } + return null; +}; +mt.registerSaveRouter(uN); +mt.registerLoadRouter(uN); +function Vd(r, e) { + return new ul(r, e); +} +function pN(r, e) { + return Vd(r, e); +} +var pl = class { + constructor(e) { + this.modelArtifacts = e; + } + load() { + return this.modelArtifacts; + } +}; +var zd = class { + constructor(e) { + this.saveHandler = e; + } + save(e) { + return this.saveHandler(e); + } +}; +var _C = class { + constructor(e) { + e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t10) => Promise.resolve(e.save(t10))); + } +}; +function cN(r, e, t10, o) { + let n = arguments; + return new _C($C(...n)); +} +function $C(r, e, t10, o) { + return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new pl(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new pl({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new pl({ modelTopology: r, weightSpecs: e, weightData: t10, trainingConfig: o })); +} +function lN(r) { + return new zd(r); +} +function mN(r) { + return new zd(r); +} +var fN = {}; +Ge(fN, { confusionMatrix: () => dN }); +function fK(r, e, t10) { + let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix"); + $(t10 == null || t10 > 0 && Number.isInteger(t10), () => `If provided, numClasses must be a positive integer, but got ${t10}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(t10 > 0 && Number.isInteger(t10), () => `numClasses is required to be a positive integer, but got ${t10}`); + let s = nl(je(o, "int32"), t10), a = nl(je(n, "int32"), t10), i = Gp(s), p = Xe(i, a); + return je(p, "int32"); +} +var dN = N({ confusionMatrix_: fK }); +var gN = {}; +Ge(gN, { fromPixels: () => SK, fromPixelsAsync: () => bK, toPixels: () => CK }); +var vu; +function hN(r, e = 3) { + if (e > 4) + throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); + if (r == null) + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + let t10 = false, o = false, n = false, s = false, a = false, i = false; + if (r.data instanceof Uint8Array) + t10 = true; + else if (typeof ImageData != "undefined" && r instanceof ImageData) + o = true; + else if (typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement) + n = true; + else if (typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement) + s = true; + else if (r.getContext != null) + a = true; + else if (typeof ImageBitmap != "undefined" && r instanceof ImageBitmap) + i = true; + else + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`); + if (Kc(Zi, T.backendName) != null) { + let f = { pixels: r }, h = { numChannels: e }; + return T.runKernel(Zi, f, h); + } + let [u, c] = n ? [r.videoWidth, r.videoHeight] : [r.width, r.height], l; + if (a) + l = r.getContext("2d").getImageData(0, 0, u, c).data; + else if (o || t10) + l = r.data; + else if (s || n || i) { + if (vu == null) + if (typeof document == "undefined") + if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined") + vu = new OffscreenCanvas(1, 1).getContext("2d"); + else + throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); + else + vu = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); + vu.canvas.width = u, vu.canvas.height = c, vu.drawImage(r, 0, 0, u, c), l = vu.getImageData(0, 0, u, c).data; + } + let m; + if (e === 4) + m = new Int32Array(l); + else { + let f = u * c; + m = new Int32Array(f * e); + for (let h = 0; h < f; h++) + for (let g = 0; g < e; ++g) + m[h * e + g] = l[h * 4 + g]; + } + return Ed(m, [c, u, e], "int32"); +} +function hK(r) { + return r != null && r.data instanceof Uint8Array; +} +function gK() { + return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap"); +} +function xK(r) { + return r != null && r.width !== 0 && r.height !== 0; +} +function yK(r) { + return gK() && !(r instanceof ImageBitmap) && xK(r) && !hK(r); +} +async function bK(r, e = 3) { + let t10 = null; + if (O().getBool("WRAP_TO_IMAGEBITMAP") && yK(r)) { + let o; + try { + o = await createImageBitmap(r, { premultiplyAlpha: "none" }); + } catch (n) { + o = null; + } + o != null && o.width === r.width && o.height === r.height ? t10 = o : t10 = r; + } else + t10 = r; + return hN(t10, e); +} +async function CK(r, e) { + let t10 = v(r, "img", "toPixels"); + if (!(r instanceof it)) { + let u = t10; + t10 = je(u, "int32"), u.dispose(); + } + if (t10.rank !== 2 && t10.rank !== 3) + throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${t10.rank}.`); + let [o, n] = t10.shape.slice(0, 2), s = t10.rank === 2 ? 1 : t10.shape[2]; + if (s > 4 || s === 2) + throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`); + if (t10.dtype !== "float32" && t10.dtype !== "int32") + throw new Error(`Unsupported type for toPixels: ${t10.dtype}. Please use float32 or int32 tensors.`); + let a = await t10.data(), i = t10.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4); + for (let u = 0; u < o * n; ++u) { + let c = [0, 0, 0, 255]; + for (let m = 0; m < s; m++) { + let d = a[u * s + m]; + if (t10.dtype === "float32") { + if (d < 0 || d > 1) + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`); + } else if (t10.dtype === "int32" && (d < 0 || d > 255)) + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`); + s === 1 ? (c[0] = d * i, c[1] = d * i, c[2] = d * i) : c[m] = d * i; + } + let l = u * 4; + p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]); + } + if (e != null) { + e.width = n, e.height = o; + let u = e.getContext("2d"), c = new ImageData(p, n, o); + u.putImageData(c, 0, 0); + } + return t10 !== r && t10.dispose(), p; +} +var SK = N({ fromPixels_: hN }); +var Wd = {}; +Ge(Wd, { prepareAndValidate: () => xN }); +function xN(r, e) { + let t10 = r.shape.length, o = e.shape.length; + if (t10 < 1) + throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t10}.`); + if (o < 1) + throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`); + if (e.dtype !== "int32") + throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`); + if (e.shape[o - 1] > t10) + throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t10}`); + if (We(r.shape) === 0) + throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`); + let n = e.shape, s = n[n.length - 1], a = 1; + for (let l = 0; l < n.length - 1; ++l) + a *= n[l]; + let i = r.shape, p = n.slice(); + p.pop(); + let u = 1; + for (let l = s; l < t10; ++l) + u *= i[l], p.push(i[l]); + let c = [...xs(r.shape).map((l) => l / u), 1].slice(0, s); + return [p, a, u, c]; +} +var ut = {}; +Ge(ut, { assertParamsValid: () => IK, computeFlatOffset: () => _K, computeOutShape: () => kK, getNormalizedAxes: () => NK, isSliceContinous: () => TK, maskToAxes: () => vK, parseSliceParams: () => $K, sliceInfo: () => EK, startForAxis: () => kN, startIndicesWithElidedDims: () => wN, stopForAxis: () => NN, stopIndicesWithElidedDims: () => IN, stridesForAxis: () => vN, stridesWithElidedDims: () => bN }); +var EC = -2; +var wK = -1; +function IK(r, e, t10) { + let o = r.shape.length; + $(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), $(o === t10.length, () => `Error in slice${o}D: Length of size ${t10} must match the rank of the array (${o}).`); + for (let n = 0; n < o; ++n) + $(e[n] + t10[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t10[n]}) would overflow input.shape[${n}] (${r.shape[n]})`); +} +function vK(r) { + let e = [], t10 = 0; + for (; r > 0; ) + r & 1 && e.push(t10), r /= 2, t10++; + return e; +} +function kK(r, e, t10) { + let o = []; + for (let n = 0; n < r.length; n++) + o[n] = Math.ceil((e[n] - r[n]) / t10[n]); + return o; +} +function bN(r, e, t10, o) { + let n = [...r]; + for (let s = n.length; s < o.length; s++) + n.push(1); + for (let s = 0; s < t10; s++) + s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop()); + return n; +} +function CN(r, e, t10) { + return t10 <= r ? t10 : t10 - (e - 1); +} +function SN(r, e) { + let t10 = []; + for (let o = 0; o < r; o++) + t10.push(e + o); + return t10; +} +function NK(r, e, t10, o, n, s, a, i, p) { + let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u); + if (e.length && t10 > 0) { + let d = e[0], f = t10 + 1; + c = wN(a, d, f, o, r), l = IN(i, d, f, n, r), m = bN(s, d, f, r); + } else + for (let d = 0; d < u; d++) + c[d] = kN(a, o, s, r, d, p), l[d] = NN(i, n, s, r, d, p), m[d] = vN(s, d, p); + return { begin: c, end: l, strides: m }; +} +function wN(r, e, t10, o, n) { + let s = [...n], a = SN(t10, e); + for (let i = 0; i < s.length; i++) + if (a.indexOf(i) > -1) + s[i] = 0; + else { + let p = CN(e, t10, i), u = o[p]; + r & 1 << p && (u = 0), s[i] = u; + } + return s; +} +function IN(r, e, t10, o, n) { + let s = [...n], a = SN(t10, e); + for (let i = 0; i < s.length; i++) + if (a.indexOf(i) > -1) + s[i] = Number.MAX_SAFE_INTEGER; + else { + let p = CN(e, t10, i), u = o[p]; + r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u; + } + for (let i = 0; i < s.length; i++) { + let p = n[i]; + s[i] < 0 && (s[i] += p), s[i] = ip(0, s[i], n[i]); + } + return s; +} +function vN(r, e, t10) { + let o = r[e]; + return (t10 & 1 << e || o == null) && (o = 1), o; +} +function kN(r, e, t10, o, n, s) { + let a = e[n], i = t10[n] || 1; + (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); + let p = o[n]; + return a < 0 && (a += p), a = ip(0, a, p - 1), a; +} +function NN(r, e, t10, o, n, s) { + let a = e[n], i = t10[n] || 1; + (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); + let p = o[n]; + return a < 0 && (a += p), i > 0 ? a = ip(0, a, p) : a = ip(-1, a, p - 1), a; +} +function TK(r, e, t10) { + let o = t10.length; + for (let n = 0; n < t10.length; n++) + if (t10[n] > 1) { + o = n; + break; + } + for (let n = o + 1; n < t10.length; n++) + if (e[n] > 0 || t10[n] !== r[n]) + return false; + return true; +} +function _K(r, e) { + let t10 = r.length > 0 ? r[r.length - 1] : 1; + for (let o = 0; o < r.length - 1; o++) + t10 += r[o] * e[o]; + return t10; +} +function $K(r, e, t10) { + let o, n = r.shape.length; + typeof e == "number" ? o = [e, ...new Array(n - 1).fill(0)] : e.length < n ? o = e.concat(new Array(n - e.length).fill(0)) : o = e.slice(), o.forEach((a) => { + $(a !== -1, () => "slice() does not support negative begin indexing."); + }); + let s; + return t10 == null ? s = new Array(n).fill(-1) : typeof t10 == "number" ? s = [t10, ...new Array(n - 1).fill(-1)] : t10.length < n ? s = t10.concat(new Array(n - t10.length).fill(-1)) : s = t10, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s]; +} +function EK(r, e, t10, o, n, s, a, i, p) { + let u; + if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && (a & a - 1) !== 0) + throw new Error("Multiple ellipses in slice is not allowed."); + let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; + for (let C = 0; C < l.dims; C++) + c && (1 << C & i) !== 0 && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); + c || (l.ellipsisMask |= 1 << l.dims, l.dims++); + let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; + AK(l, m); + let d = true, f = true, h = true, g = [], x = []; + for (let C = 0; C < r.length; ++C) { + if (m.strides[C] === 0) + throw Error(`strides[${C}] must be non-zero`); + let w = !!(m.shrinkAxisMask & 1 << C), k = r[C]; + if (k === -1) { + g.push(w ? 1 : -1); + continue; + } + let _ = [m.beginMask & 1 << C, m.endMask & 1 << C], E = [m.strides[C] > 0 ? 0 : -1, m.strides[C] > 0 ? k : k - 1]; + if (w && m.strides[C] <= 0) + throw Error("only stride 1 allowed on non-range indexing."); + h = h && m.strides[C] === 1; + let A = !!(m.beginMask & 1 << C && m.endMask & 1 << C); + if (m.beginValid && m.endValid) { + if (w) { + let M = m.begin[C] < 0 ? k + m.begin[C] : m.begin[C]; + if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k) + throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`); + } else + m.begin[C] = yN(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = yN(m.end[C], 1, m.strides[C], k, _, E); + let P = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k; + d = d && P, f = f && (C === 0 && m.strides[C] === 1 || P); + } else + d = d && m.strides[C] === 1 && A, f = f && (C === 0 && m.strides[C] === 1 || A); + let R, D = false; + if (m.beginValid && m.endValid ? (R = m.end[C] - m.begin[C], D = true) : w ? (R = 1, D = true) : A && k >= 0 && (m.strides[C] < 0 ? R = -k : R = k, D = true), D) { + let P; + R === 0 || R < 0 != m.strides[C] < 0 ? P = 0 : P = Math.trunc(R / m.strides[C]) + (R % m.strides[C] !== 0 ? 1 : 0), g.push(P); + } else + g.push(-1); + } + for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) { + let w = m.finalShapeGatherIndices[C]; + w >= 0 ? x.push(g[w]) : w === EC && x.push(1); + } + return { finalShapeSparse: x.filter((C, w) => m.finalShapeGatherIndices[w] !== EC), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides }; +} +function AK(r, e) { + e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0; + let t10 = 0; + e.beginValid = r.begin != null, e.endValid = r.end != null, e.begin = new Array(e.dims), e.end = new Array(e.dims), e.strides = new Array(e.dims), e.finalShapeGatherIndices = [], e.finalShapeGatherIndicesSparse = [], e.inputShapeGatherIndicesSparse = new Array(e.dims); + for (let o = 0; o < r.dims; o++) + if (1 << o & r.ellipsisMask) { + let n = Math.min(e.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, e.dims); + for (; t10 < n; t10++) + e.begin[t10] = 0, e.end[t10] = 0, e.strides[t10] = 1, e.beginMask |= 1 << t10, e.endMask |= 1 << t10, e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t10] = o; + } else if (1 << o & r.newAxisMask) + e.finalShapeGatherIndices.push(EC), e.finalShapeGatherIndicesSparse.push(-1); + else { + if (t10 === e.begin.length) + throw Error(`Index out of range using input dim ${t10}; input has only ${e.dims} dims, ${e.begin.length}.`); + r.begin != null && (e.begin[t10] = r.begin[o]), r.end != null && (e.end[t10] = r.end[o]), e.strides[t10] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t10), r.endMask & 1 << o && (e.endMask |= 1 << t10), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(wK), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t10) : (e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t10] = o, t10++; + } +} +function yN(r, e, t10, o, n, s) { + if (n[e]) + return t10 > 0 ? s[e] : s[e + 1 & 1]; + { + let a = r < 0 ? o + r : r; + return a < s[0] ? s[0] : a > s[1] ? s[1] : a; + } +} +var RK = "4.2.0"; +var cl = class { static sgd(e) { - return new qs(e); + return new Pa(e); } - static momentum(e, t6, o = false) { - return new Fi(e, t6, o); + static momentum(e, t10, o = false) { + return new wu(e, t10, o); } - static rmsprop(e, t6 = 0.9, o = 0, n = null, s = false) { - return new Di(e, t6, o, n, s); + static rmsprop(e, t10 = 0.9, o = 0, n = null, s = false) { + return new Iu(e, t10, o, n, s); } - static adam(e = 1e-3, t6 = 0.9, o = 0.999, n = null) { - return new Ai(e, t6, o, n); + static adam(e = 1e-3, t10 = 0.9, o = 0.999, n = null) { + return new Cu(e, t10, o, n); } - static adadelta(e = 1e-3, t6 = 0.95, o = null) { - return new Ei(e, t6, o); + static adadelta(e = 1e-3, t10 = 0.95, o = null) { + return new yu(e, t10, o); } - static adamax(e = 2e-3, t6 = 0.9, o = 0.999, n = null, s = 0) { - return new Ri(e, t6, o, n, s); + static adamax(e = 2e-3, t10 = 0.9, o = 0.999, n = null, s = 0) { + return new Su(e, t10, o, n, s); } - static adagrad(e, t6 = 0.1) { - return new $i(e, t6); + static adagrad(e, t10 = 0.1) { + return new bu(e, t10); } }; -var hMe = { sgd: ns.sgd, momentum: ns.momentum, adadelta: ns.adadelta, adagrad: ns.adagrad, rmsprop: ns.rmsprop, adamax: ns.adamax, adam: ns.adam }; -var dq = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())(); -function CC() { - return new Promise((r) => dq(() => r())); +var YMe = cl; +var DK = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())(); +function AC() { + return new Promise((r) => DK(() => r())); } var S = {}; -Ue(S, { ERF_A1: () => $q, ERF_A2: () => Aq, ERF_A3: () => Rq, ERF_A4: () => Fq, ERF_A5: () => Dq, ERF_P: () => Eq, PARALLELIZE_THRESHOLD: () => Ud, RowPartitionType: () => Ks, SELU_SCALE: () => _q, SELU_SCALEALPHA: () => Tq, applyActivation: () => yu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => DU, assertParamsConsistent: () => fq, assignToTypedArray: () => Vq, axesAreInnerMostDims: () => uC, calculateShapes: () => J0, checkEinsumDimSizes: () => qq, checkPadOnDimRoundingMode: () => Pt, combineLocations: () => nk, combineRaggedTensorToTensorShapes: () => gq, complexWithEvenIndex: () => Mq, complexWithOddIndex: () => Lq, computeConv2DInfo: () => uu, computeConv3DInfo: () => Nv, computeDefaultPad: () => iC, computeDilation2DInfo: () => OW, computeOptimalWindowSize: () => Cq, computeOutAndReduceShapes: () => FU, computeOutShape: () => hq, computePool2DInfo: () => aC, computePool3DInfo: () => PW, convertConv2DDataFormat: () => Tv, decodeEinsumEquation: () => Gq, eitherStridesOrDilationsAreOne: () => lr, expandShapeToKeepDim: () => Aa, exponent: () => Wq, exponents: () => zq, fromStringArrayToUint8: () => dK, fromUint8ToStringArray: () => mK, getAxesPermutation: () => OU, getBroadcastDims: () => X0, getComplexWithIndex: () => Bq, getEinsumComputePath: () => Kq, getEinsumPermutation: () => Hq, getFusedBiasGradient: () => xu, getFusedDyActivation: () => gu, getImageCenter: () => Sq, getInnerMostAxes: () => MU, getPermuted: () => Iq, getRaggedRank: () => yq, getReductionAxes: () => jm, getReshaped: () => wq, getReshapedPermuted: () => vq, getRowPartitionTypesHelper: () => xq, getSliceBeginCoords: () => kq, getSliceSize: () => Nq, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => Qq, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => Zq, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => Jq, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => rK, getSparseReshapeInputOutputMismatchErrorMessage: () => nK, getSparseReshapeInputOutputMultipleErrorMessage: () => oK, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => eK, getSparseReshapeNegativeOutputDimErrorMessage: () => tK, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => uK, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => sK, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => aK, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => iK, getUndoAxesPermutation: () => PU, isIdentityPermutation: () => jq, log: () => GV, mergeRealAndImagArrays: () => Oq, prepareAndValidate: () => Z0, prepareSplitSize: () => Yq, segment_util: () => wC, shouldFuse: () => bu, slice_util: () => ut, splitRealAndImagArrays: () => Pq, tupleValuesAreOne: () => iu, upcastType: () => dt, validateDefaultValueShape: () => bq, validateInput: () => Qm, validateUpdateShape: () => tC, warn: () => Os }); -function fq(r, e) { - let t6 = r[0].length; +Ge(S, { ERF_A1: () => YK, ERF_A2: () => QK, ERF_A3: () => ZK, ERF_A4: () => JK, ERF_A5: () => eq, ERF_P: () => XK, PARALLELIZE_THRESHOLD: () => Ud, RowPartitionType: () => js, SELU_SCALE: () => jK, SELU_SCALEALPHA: () => qK, applyActivation: () => gu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => dU, assertParamsConsistent: () => FK, assignToTypedArray: () => aq, axesAreInnerMostDims: () => uC, calculateShapes: () => l1, checkEinsumDimSizes: () => mq, checkPadOnDimRoundingMode: () => Mt, combineLocations: () => Ov, combineRaggedTensorToTensorShapes: () => PK, complexWithEvenIndex: () => oq, complexWithOddIndex: () => nq, computeConv2DInfo: () => au, computeConv3DInfo: () => rv, computeDefaultPad: () => iC, computeDilation2DInfo: () => dW, computeOptimalWindowSize: () => VK, computeOutAndReduceShapes: () => mU, computeOutShape: () => OK, computePool2DInfo: () => aC, computePool3DInfo: () => fW, convertConv2DDataFormat: () => ov, decodeEinsumEquation: () => cq, eitherStridesOrDilationsAreOne: () => mr, expandShapeToKeepDim: () => Ea, exponent: () => uq, exponents: () => iq, fromStringArrayToUint8: () => Dq, fromUint8ToStringArray: () => Rq, getAxesPermutation: () => fU, getBroadcastDims: () => Ev, getComplexWithIndex: () => sq, getEinsumComputePath: () => dq, getEinsumPermutation: () => lq, getFusedBiasGradient: () => hu, getFusedDyActivation: () => fu, getImageCenter: () => zK, getInnerMostAxes: () => gU, getPermuted: () => UK, getRaggedRank: () => LK, getReductionAxes: () => Qm, getReshaped: () => WK, getReshapedPermuted: () => GK, getRowPartitionTypesHelper: () => MK, getSliceBeginCoords: () => HK, getSliceSize: () => KK, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => xq, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => yq, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => bq, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => wq, getSparseReshapeInputOutputMismatchErrorMessage: () => vq, getSparseReshapeInputOutputMultipleErrorMessage: () => Iq, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => Cq, getSparseReshapeNegativeOutputDimErrorMessage: () => Sq, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => _q, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => kq, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => Nq, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => Tq, getUndoAxesPermutation: () => hU, isIdentityPermutation: () => fq, log: () => pz, mergeRealAndImagArrays: () => tq, prepareAndValidate: () => xN, prepareSplitSize: () => gq, segment_util: () => DC, shouldFuse: () => xu, slice_util: () => ut, splitRealAndImagArrays: () => rq, stridesOrDilationsArePositive: () => Vs, tupleValuesAreOne: () => su, upcastType: () => dt, validateDefaultValueShape: () => BK, validateInput: () => Dd, validateUpdateShape: () => bC, warn: () => Ps }); +function FK(r, e) { + let t10 = r[0].length; r.forEach((n, s) => { - E(n.length === t6, () => `Error in concat${t6}D: rank of tensors[${s}] must be the same as the rank of the rest (${t6})`); - }), E(e >= 0 && e < t6, () => `Error in concat${t6}D: axis must be between 0 and ${t6 - 1}.`); + $(n.length === t10, () => `Error in concat${t10}D: rank of tensors[${s}] must be the same as the rank of the rest (${t10})`); + }), $(e >= 0 && e < t10, () => `Error in concat${t10}D: axis must be between 0 and ${t10 - 1}.`); let o = r[0]; r.forEach((n, s) => { - for (let a = 0; a < t6; a++) - E(a === e || n[a] === o[a], () => `Error in concat${t6}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`); + for (let a = 0; a < t10; a++) + $(a === e || n[a] === o[a], () => `Error in concat${t10}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`); }); } -function hq(r, e) { - let t6 = r[0].slice(); +function OK(r, e) { + let t10 = r[0].slice(); for (let o = 1; o < r.length; o++) - t6[e] += r[o][e]; - return t6; + t10[e] += r[o][e]; + return t10; } -var Ks; +var js; (function(r) { r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS"; -})(Ks || (Ks = {})); -function gq(r, e, t6) { +})(js || (js = {})); +function PK(r, e, t10) { let o = new Array(); - if (t6 == null && e == null) + if (t10 == null && e == null) return o; if (e == null) - for (; o.length < r + t6.length; ) + for (; o.length < r + t10.length; ) o.push(-1); else o = e.slice(); - if (t6 == null) + if (t10 == null) return o; - if (r + t6.length !== o.length) - throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t6.length}, but shape.rank = ${o.length}`); - for (let n = 1; n < t6.length; ++n) { - let s = t6[n], a = o[o.length - t6.length + n], i = o[a]; + if (r + t10.length !== o.length) + throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t10.length}, but shape.rank = ${o.length}`); + for (let n = 1; n < t10.length; ++n) { + let s = t10[n], a = o[o.length - t10.length + n], i = o[a]; if (s >= 0) if (i >= 0) { if (i !== s) @@ -9024,42 +9012,42 @@ function gq(r, e, t6) { } return o; } -function xq(r) { - let e = { FIRST_DIM_SIZE: Ks.FIRST_DIM_SIZE, VALUE_ROWIDS: Ks.VALUE_ROWIDS, ROW_LENGTHS: Ks.ROW_LENGTHS, ROW_SPLITS: Ks.ROW_SPLITS, ROW_LIMITS: Ks.ROW_LIMITS, ROW_STARTS: Ks.ROW_STARTS }, t6 = []; +function MK(r) { + let e = { FIRST_DIM_SIZE: js.FIRST_DIM_SIZE, VALUE_ROWIDS: js.VALUE_ROWIDS, ROW_LENGTHS: js.ROW_LENGTHS, ROW_SPLITS: js.ROW_SPLITS, ROW_LIMITS: js.ROW_LIMITS, ROW_STARTS: js.ROW_STARTS }, t10 = []; for (let o of r) if (o in e) - t6.push(e[o]); + t10.push(e[o]); else break; - return t6; + return t10; } -function yq(r) { - return r.length === 0 ? 0 : r[0] === Ks.FIRST_DIM_SIZE ? r.length - 1 : r.length; +function LK(r) { + return r.length === 0 ? 0 : r[0] === js.FIRST_DIM_SIZE ? r.length - 1 : r.length; } -function bq(r, e) { +function BK(r, e) { if (r == null || e == null) return; - let t6 = r.length, o = e.length; - if (t6 >= o) - throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t6} must be less than ragged tensor input flatValues.rank = ${o})`); - for (let n = 0; n < Math.min(t6, o - 1); ++n) { + let t10 = r.length, o = e.length; + if (t10 >= o) + throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t10} must be less than ragged tensor input flatValues.rank = ${o})`); + for (let n = 0; n < Math.min(t10, o - 1); ++n) { let s = r[n], a = e[n + 1]; if (s >= 0 && a >= 0 && s !== 1 && s !== a) throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n - r.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r.length}] = ${a}`); } } var Ud = 30; -function Cq(r) { - return r <= Ud ? r : sp(r, Math.floor(Math.sqrt(r))); +function VK(r) { + return r <= Ud ? r : cp(r, Math.floor(Math.sqrt(r))); } -function Sq(r, e, t6) { - let o = t6 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]); +function zK(r, e, t10) { + let o = t10 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]); return [o, n]; } -function wq(r, e, t6, o = true) { +function WK(r, e, t10, o = true) { let n = []; if (o) - n = n.concat(e.slice(0)), n.push(r[0] / t6), n = n.concat(r.slice(1)); + n = n.concat(e.slice(0)), n.push(r[0] / t10), n = n.concat(r.slice(1)); else { n = n.concat(r[0]); let s = e.length; @@ -9069,9 +9057,9 @@ function wq(r, e, t6, o = true) { } return n; } -function Iq(r, e, t6 = true) { +function UK(r, e, t10 = true) { let o = []; - if (t6) { + if (t10) { o.push(e); for (let n = e + 1; n < r; ++n) n <= 2 * e ? (o.push(n), o.push(n - (e + 1))) : o.push(n); @@ -9083,92 +9071,92 @@ function Iq(r, e, t6 = true) { } return o; } -function vq(r, e, t6, o = true) { +function GK(r, e, t10, o = true) { let n = []; - o ? n.push(r[0] / t6) : n.push(r[0] * t6); + o ? n.push(r[0] / t10) : n.push(r[0] * t10); for (let s = 1; s < r.length; ++s) s <= e.length ? o ? n.push(e[s - 1] * r[s]) : n.push(r[s] / e[s - 1]) : n.push(r[s]); return n; } -function kq(r, e) { - let t6 = [0]; +function HK(r, e) { + let t10 = [0]; for (let o = 0; o < e; ++o) - t6.push(r[o][0]); - return t6; + t10.push(r[o][0]); + return t10; } -function Nq(r, e, t6) { +function KK(r, e, t10) { let o = r.slice(0, 1); - for (let n = 0; n < t6; ++n) + for (let n = 0; n < t10; ++n) o.push(r[n + 1] - e[n][0] - e[n][1]); return o; } -var Tq = 1.7580993408473768; -var _q = 1.0507009873554805; -var Eq = 0.3275911; -var $q = 0.254829592; -var Aq = -0.284496736; -var Rq = 1.421413741; -var Fq = -1.453152027; -var Dq = 1.061405429; -function Oq(r, e) { +var qK = 1.7580993408473768; +var jK = 1.0507009873554805; +var XK = 0.3275911; +var YK = 0.254829592; +var QK = -0.284496736; +var ZK = 1.421413741; +var JK = -1.453152027; +var eq = 1.061405429; +function tq(r, e) { if (r.length !== e.length) throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`); - let t6 = new Float32Array(r.length * 2); - for (let o = 0; o < t6.length; o += 2) - t6[o] = r[o / 2], t6[o + 1] = e[o / 2]; - return t6; + let t10 = new Float32Array(r.length * 2); + for (let o = 0; o < t10.length; o += 2) + t10[o] = r[o / 2], t10[o + 1] = e[o / 2]; + return t10; } -function Pq(r) { - let e = new Float32Array(r.length / 2), t6 = new Float32Array(r.length / 2); +function rq(r) { + let e = new Float32Array(r.length / 2), t10 = new Float32Array(r.length / 2); for (let o = 0; o < r.length; o += 2) - e[o / 2] = r[o], t6[o / 2] = r[o + 1]; - return { real: e, imag: t6 }; + e[o / 2] = r[o], t10[o / 2] = r[o + 1]; + return { real: e, imag: t10 }; } -function Mq(r) { - let e = Math.ceil(r.length / 4), t6 = new Float32Array(e), o = new Float32Array(e); +function oq(r) { + let e = Math.ceil(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e); for (let n = 0; n < r.length; n += 4) - t6[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; - return { real: t6, imag: o }; + t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; + return { real: t10, imag: o }; } -function Lq(r) { - let e = Math.floor(r.length / 4), t6 = new Float32Array(e), o = new Float32Array(e); +function nq(r) { + let e = Math.floor(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e); for (let n = 2; n < r.length; n += 4) - t6[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; - return { real: t6, imag: o }; + t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; + return { real: t10, imag: o }; } -function Bq(r, e) { - let t6 = r[e * 2], o = r[e * 2 + 1]; - return { real: t6, imag: o }; +function sq(r, e) { + let t10 = r[e * 2], o = r[e * 2 + 1]; + return { real: t10, imag: o }; } -function Vq(r, e, t6, o) { - r[o * 2] = e, r[o * 2 + 1] = t6; +function aq(r, e, t10, o) { + r[o * 2] = e, r[o * 2 + 1] = t10; } -function zq(r, e) { - let t6 = new Float32Array(r / 2), o = new Float32Array(r / 2); +function iq(r, e) { + let t10 = new Float32Array(r / 2), o = new Float32Array(r / 2); for (let n = 0; n < Math.ceil(r / 2); n++) { let s = (e ? 2 : -2) * Math.PI * (n / r); - t6[n] = Math.cos(s), o[n] = Math.sin(s); + t10[n] = Math.cos(s), o[n] = Math.sin(s); } - return { real: t6, imag: o }; + return { real: t10, imag: o }; } -function Wq(r, e, t6) { - let o = (t6 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o); +function uq(r, e, t10) { + let o = (t10 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o); return { real: n, imag: s }; } -var SC = "->"; -var Uq = /->/g; -var SN = ","; -var wN = "..."; -function Gq(r, e) { +var RC = "->"; +var pq = /->/g; +var TN = ","; +var _N = "..."; +function cq(r, e) { r = r.replace(/\s/g, ""); - let t6 = (r.length - r.replace(Uq, "").length) / SC.length; - if (t6 < 1) + let t10 = (r.length - r.replace(pq, "").length) / RC.length; + if (t10 < 1) throw new Error("Equations without an arrow are not supported."); - if (t6 > 1) - throw new Error(`Equation must contain exactly one arrow ("${SC}").`); - let [o, n] = r.split(SC); - E(o.indexOf(wN) === -1, () => `The ellipsis notation ("${wN}") is not supported yet.`); - let s = o.split(SN), a = s.length; + if (t10 > 1) + throw new Error(`Equation must contain exactly one arrow ("${RC}").`); + let [o, n] = r.split(RC); + $(o.indexOf(_N) === -1, () => `The ellipsis notation ("${_N}") is not supported yet.`); + let s = o.split(TN), a = s.length; if (e !== a) throw new Error(`Expected ${a} input tensors, received ${e}`); if (a > 2) @@ -9182,7 +9170,7 @@ function Gq(r, e) { } for (let m = 0; m < o.length; ++m) { let d = o[m]; - i.indexOf(d) === -1 && d !== SN && i.push(d); + i.indexOf(d) === -1 && d !== TN && i.push(d); } let p = new Array(s.length); for (let m = 0; m < a; ++m) { @@ -9197,207 +9185,208 @@ function Gq(r, e) { l.push(m); return { allDims: i, summedDims: l, idDims: p }; } -function Hq(r, e) { - let t6 = new Array(r); - t6.fill(-1); +function lq(r, e) { + let t10 = new Array(r); + t10.fill(-1); for (let n = 0; n < e.length; ++n) - t6[e[n]] = n; + t10[e[n]] = n; let o = []; for (let n = 0; n < r; ++n) - t6[n] === -1 && o.push(n); - return t6 = t6.filter((n) => n !== -1), { permutationIndices: t6, expandDims: o }; + t10[n] === -1 && o.push(n); + return t10 = t10.filter((n) => n !== -1), { permutationIndices: t10, expandDims: o }; } -function qq(r, e, t6) { +function mq(r, e, t10) { let o = new Array(r); - for (let n = 0; n < t6.length; ++n) { - let s = t6[n].shape; + for (let n = 0; n < t10.length; ++n) { + let s = t10[n].shape; for (let a = 0; a < e[n].length; ++a) - o[e[n][a]] === void 0 ? o[e[n][a]] = s[a] : E(o[e[n][a]] === s[a], () => `Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`); + o[e[n][a]] === void 0 ? o[e[n][a]] = s[a] : $(o[e[n][a]] === s[a], () => `Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`); } } -function Kq(r, e) { - let t6 = r, o = [], n = 0; - r.length === 0 && t6.push(-1), n = r.length + 1; +function dq(r, e) { + let t10 = r, o = [], n = 0; + r.length === 0 && t10.push(-1), n = r.length + 1; for (let a = 0; a < n; ++a) o.push([]); let s = []; - for (let a = 0; a < t6.length; ++a) { - let i = t6[a], p = Xq(e, i); + for (let a = 0; a < t10.length; ++a) { + let i = t10[a], p = hq(e, i); for (let u of p) s.indexOf(u) === -1 && (o[a].push(u), s.push(u)); } - return { path: t6, steps: o }; + return { path: t10, steps: o }; } -function jq(r) { - return r.every((e, t6) => e === t6); +function fq(r) { + return r.every((e, t10) => e === t10); } -function Xq(r, e) { - let t6 = []; +function hq(r, e) { + let t10 = []; for (let o = 0; o < r.length; ++o) - (r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t6.push(o); - return t6; + (r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t10.push(o); + return t10; } -function Yq(r, e, t6 = 0) { +function gq(r, e, t10 = 0) { let o = []; if (typeof e == "number") - E(r.shape[t6] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t6] / e); + $(r.shape[t10] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t10] / e); else { let n = e.reduce((a, i) => (i === -1 && (a += 1), a), 0); - E(n <= 1, () => "There should be only one negative value in split array."); + $(n <= 1, () => "There should be only one negative value in split array."); let s = e.indexOf(-1); if (s !== -1) { let a = e.reduce((i, p) => p > 0 ? i + p : i); - e[s] = r.shape[t6] - a; + e[s] = r.shape[t10] - a; } - E(r.shape[t6] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e; + $(r.shape[t10] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e; } return o; } -function Qq(r) { +function xq(r) { return `Received SparseTensor with denseShape[0] = 0 but indices.shape[0] = ${r}`; } -function Zq(r, e) { +function yq(r, e) { return `indices(${r}, 0) is invalid: ${e} < 0`; } -function Jq(r, e, t6) { - return `indices(${r}, 0) is invalid: ${e} >= ${t6}`; +function bq(r, e, t10) { + return `indices(${r}, 0) is invalid: ${e} >= ${t10}`; } -function eK(r, e) { +function Cq(r, e) { return `only one output dimension may be -1, not both ${r} and ${e}`; } -function tK(r, e) { +function Sq(r, e) { return `size ${r} must be non-negative, not ${e}`; } -function rK() { +function wq() { return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; } -function oK(r, e) { - let t6 = ze(r), o = ze(e); - return `Input to reshape is a SparseTensor with ${t6} +function Iq(r, e) { + let t10 = We(r), o = We(e); + return `Input to reshape is a SparseTensor with ${t10} dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`; } -function nK(r, e) { - let t6 = ze(r), o = ze(e); - return `Input to reshape is a tensor with ${t6} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`; +function vq(r, e) { + let t10 = We(r), o = We(e); + return `Input to reshape is a tensor with ${t10} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`; } -function sK() { +function kq() { return "segment ids must be >= 0"; } -function aK() { +function Nq() { return "segment ids are not increasing"; } -function iK(r, e) { +function Tq(r, e) { return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`; } -function uK(r, e, t6) { - return `Bad: indices[${r}] == ${e} out of range [0, ${t6})`; +function _q(r, e, t10) { + return `Bad: indices[${r}] == ${e} out of range [0, ${t10})`; } -var wC = {}; -Ue(wC, { collectGatherOpShapeInfo: () => lK, computeOutShape: () => cK, segOpComputeOptimalWindowSize: () => pK }); -function pK(r, e) { - let t6 = false, o; - for (r <= Ud ? (o = r, t6 = true) : o = sp(r, Math.floor(Math.sqrt(r))); !t6; ) - o > e || o === r ? t6 = true : o = sp(r, o + 1); +var DC = {}; +Ge(DC, { collectGatherOpShapeInfo: () => Aq, computeOutShape: () => Eq, segOpComputeOptimalWindowSize: () => $q }); +function $q(r, e) { + let t10 = false, o; + for (r <= Ud ? (o = r, t10 = true) : o = cp(r, Math.floor(Math.sqrt(r))); !t10; ) + o > e || o === r ? t10 = true : o = cp(r, o + 1); return o; } -function cK(r, e, t6) { +function Eq(r, e, t10) { let o = [], n = r.length; for (let s = 0; s < n; s++) - s !== e ? o.push(r[s]) : o.push(t6); + s !== e ? o.push(r[s]) : o.push(t10); return o; } -function lK(r, e, t6, o) { +function Aq(r, e, t10, o) { let n = e.shape.length, s = r.shape.length; if (o !== 0 && (o < -n || o > n)) throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`); if (o < 0 && (o += n), o > s) throw new Error(`batchDims (${o}) must be less than rank(x) ( ${s}).`); - if (t6 < o) - throw new Error(`batchDims (${o}) must be less than or equal to axis (${t6}).`); + if (t10 < o) + throw new Error(`batchDims (${o}) must be less than or equal to axis (${t10}).`); for (let l = 0; l < o; ++l) if (r.shape[l] !== e.shape[l]) throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${e.shape[l]}.`); - let a = r.shape[t6], i = [], p = 1, u = 1, c = 1; + let a = r.shape[t10], i = [], p = 1, u = 1, c = 1; for (let l = 0; l < o; ++l) i.push(r.shape[l]), p *= r.shape[l]; - for (let l = o; l < t6; l++) + for (let l = o; l < t10; l++) i.push(r.shape[l]), u *= r.shape[l]; for (let l = o; l < n; l++) i.push(e.shape[l]); - for (let l = t6 + 1; l < s; l++) + for (let l = t10 + 1; l < s; l++) i.push(r.shape[l]), c *= r.shape[l]; return { batchSize: p, sliceSize: c, outerSize: u, dimSize: a, outputShape: i }; } -function mK(r) { +function Rq(r) { try { - return r.map((e) => Ap(e)); + return r.map((e) => Rp(e)); } catch (e) { throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`); } } -function dK(r) { - return r.map((e) => gi(e)); +function Dq(r) { + return r.map((e) => Ii(e)); } -var Lt = {}; -Ue(Lt, { nonMaxSuppressionV3Impl: () => Vd, nonMaxSuppressionV4Impl: () => zd, nonMaxSuppressionV5Impl: () => Wd, whereImpl: () => Pd }); -var fK = O(); -fK.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => { +var Vt = {}; +Ge(Vt, { nonMaxSuppressionV3Impl: () => Pd, nonMaxSuppressionV4Impl: () => Md, nonMaxSuppressionV5Impl: () => Ld, whereImpl: () => Ad }); +nN(); +var Fq = O(); +Fq.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => { r && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); }); var ao; (function(r) { r[r.DT_INVALID = 0] = "DT_INVALID", r[r.DT_FLOAT = 1] = "DT_FLOAT", r[r.DT_DOUBLE = 2] = "DT_DOUBLE", r[r.DT_INT32 = 3] = "DT_INT32", r[r.DT_UINT8 = 4] = "DT_UINT8", r[r.DT_INT16 = 5] = "DT_INT16", r[r.DT_INT8 = 6] = "DT_INT8", r[r.DT_STRING = 7] = "DT_STRING", r[r.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r[r.DT_INT64 = 9] = "DT_INT64", r[r.DT_BOOL = 10] = "DT_BOOL", r[r.DT_QINT8 = 11] = "DT_QINT8", r[r.DT_QUINT8 = 12] = "DT_QUINT8", r[r.DT_QINT32 = 13] = "DT_QINT32", r[r.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r[r.DT_QINT16 = 15] = "DT_QINT16", r[r.DT_QUINT16 = 16] = "DT_QUINT16", r[r.DT_UINT16 = 17] = "DT_UINT16", r[r.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r[r.DT_HALF = 19] = "DT_HALF", r[r.DT_RESOURCE = 20] = "DT_RESOURCE", r[r.DT_VARIANT = 21] = "DT_VARIANT", r[r.DT_UINT32 = 22] = "DT_UINT32", r[r.DT_UINT64 = 23] = "DT_UINT64", r[r.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r[r.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r[r.DT_INT32_REF = 103] = "DT_INT32_REF", r[r.DT_UINT8_REF = 104] = "DT_UINT8_REF", r[r.DT_INT16_REF = 105] = "DT_INT16_REF", r[r.DT_INT8_REF = 106] = "DT_INT8_REF", r[r.DT_STRING_REF = 107] = "DT_STRING_REF", r[r.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r[r.DT_INT64_REF = 109] = "DT_INT64_REF", r[r.DT_BOOL_REF = 110] = "DT_BOOL_REF", r[r.DT_QINT8_REF = 111] = "DT_QINT8_REF", r[r.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r[r.DT_QINT32_REF = 113] = "DT_QINT32_REF", r[r.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r[r.DT_QINT16_REF = 115] = "DT_QINT16_REF", r[r.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r[r.DT_UINT16_REF = 117] = "DT_UINT16_REF", r[r.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r[r.DT_HALF_REF = 119] = "DT_HALF_REF", r[r.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r[r.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r[r.DT_UINT32_REF = 122] = "DT_UINT32_REF", r[r.DT_UINT64_REF = 123] = "DT_UINT64_REF"; })(ao || (ao = {})); -var IN; +var $N; (function(r) { let e; - (function(t6) { - t6[t6.LEGACY = 0] = "LEGACY", t6[t6.V1 = 1] = "V1", t6[t6.V2 = 2] = "V2"; + (function(t10) { + t10[t10.LEGACY = 0] = "LEGACY", t10[t10.V1 = 1] = "V1", t10[t10.V2 = 2] = "V2"; })(e = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {})); -})(IN || (IN = {})); -var vC = {}; -function gK(r, e) { - let t6 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e }; - vC[r] = t6; +})($N || ($N = {})); +var OC = {}; +function Pq(r, e) { + let t10 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e }; + OC[r] = t10; } function Gd(r) { - return vC[r]; + return OC[r]; } -function xK(r) { - delete vC[r]; +function Mq(r) { + delete OC[r]; } -function I(r, e, t6, o, n) { +function I(r, e, t10, o, n) { let s = e.inputParams[r]; if (s && s.inputIndexStart !== void 0) { let i = s.inputIndexStart, p = s.inputIndexEnd === 0 ? void 0 : s.inputIndexEnd === void 0 ? i + 1 : s.inputIndexEnd; if (s.type === "tensor") - return Gt(e.inputNames[s.inputIndexStart], t6, o, n); + return Gt(e.inputNames[s.inputIndexStart], t10, o, n); if (s.type === "tensors") - return e.inputNames.slice(i, p).map((m) => Gt(m, t6, o, n)); - let u = Gt(e.inputNames.slice(i)[0], t6, o, n), c = u.dataSync(); + return e.inputNames.slice(i, p).map((m) => Gt(m, t10, o, n)); + let u = Gt(e.inputNames.slice(i)[0], t10, o, n), c = u.dataSync(); return s.type === "number" ? c[0] : y.toNestedArray(u.shape, c); } let a = e.attrParams[r]; return a && a.value; } -function Gt(r, e, t6, o) { +function Gt(r, e, t10, o) { let [n, s] = Ir(r); if (o != null) { let i = o.getHashTableHandleByName(n); if (i != null) return i; } - let a = t6.currentContextIds.find((i) => !!e[Hd(n, i)]); + let a = t10.currentContextIds.find((i) => !!e[Hd(n, i)]); return a !== void 0 ? e[Hd(n, a)][s] : void 0; } -function vN(r, e, t6) { - return e[Hd(r, t6.currentContextId)]; +function EN(r, e, t10) { + return e[Hd(r, t10.currentContextId)]; } -function ss(r, e) { - let [t6, o, n] = Ir(r); - return [Hd(t6, e && e.currentContextId), o, n]; +function as(r, e) { + let [t10, o, n] = Ir(r); + return [Hd(t10, e && e.currentContextId), o, n]; } function Hd(r, e) { return e ? `${r}-${e}` : r; @@ -9406,13 +9395,13 @@ function Ir(r) { let e = r.split(":"); if (e.length === 1) return [r, 0, void 0]; - let t6 = e[0], o = e.length === 3 ? e[1] : void 0, n = Number(e[e.length - 1]); - return [t6, n, o]; + let t10 = e[0], o = e.length === 3 ? e[1] : void 0, n = Number(e[e.length - 1]); + return [t10, n, o]; } -function ul(r, e, t6) { - let o = I("pad", r, e, t6); +function ll(r, e, t10) { + let o = I("pad", r, e, t10); if (o === "explicit") { - o = I("explicitPaddings", r, e, t6); + o = I("explicitPaddings", r, e, t10); let n = [[0, 0], [0, 0], [0, 0], [0, 0]]; for (let s = 0; s < 4; s++) n[s][0] = o[s * 2], n[s][1] = o[s * 2 + 1]; @@ -9420,87 +9409,87 @@ function ul(r, e, t6) { } return o; } -function as(r) { - return r.kept ? r : Br(r); +function is(r) { + return r.kept ? r : Lr(r); } -var kC = {}; -Ue(kC, { json: () => yK }); -var yK = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; -var NC = {}; -Ue(NC, { json: () => bK }); -var bK = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; -var TC = {}; -Ue(TC, { json: () => CK }); -var CK = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { 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[{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }]; -var $C = {}; -Ue($C, { json: () => IK }); -var IK 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name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; -var AC = {}; -Ue(AC, { json: () => vK }); -var vK = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }]; -var RC = {}; -Ue(RC, { json: () => kK }); -var kK = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }]; -var FC = {}; -Ue(FC, { json: () => NK }); -var NK = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "InitializeTable", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }, { tfOpName: "InitializeTableV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }]; -var DC = {}; -Ue(DC, { json: () => TK }); -var TK = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }]; -var OC = {}; -Ue(OC, { json: () => _K }); -var _K = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var PC = {}; -Ue(PC, { json: () => EK }); -var EK = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }]; +Ge(PC, { json: () => Lq }); +var Lq = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var MC = {}; -Ue(MC, { json: () => $K }); -var $K = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }]; +Ge(MC, { json: () => Bq }); +var Bq = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var LC = {}; -Ue(LC, { json: () => AK }); -var AK = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }]; +Ge(LC, { json: () => Vq }); +var Vq = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape_except0", name: "elementShapeExcept0", type: "shape", notSupported: true }] }, { tfOpName: "TensorArraySplitV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }, { start: 2, name: "lengths", type: "number[]" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArraySizeV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }] }, { tfOpName: "TensorArrayCloseV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }] }, { tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcatV2", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }]; var BC = {}; -Ue(BC, { json: () => RK }); -var RK = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }]; +Ge(BC, { json: () => zq }); +var zq = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }]; var VC = {}; -Ue(VC, { json: () => FK }); -var FK = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }]; +Ge(VC, { json: () => Wq }); +var Wq = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomStandardNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }]; var zC = {}; -Ue(zC, { json: () => DK }); -var DK = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }]; +Ge(zC, { json: () => Uq }); +var Uq = [{ tfOpName: "NonMaxSuppressionV2", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }]; var WC = {}; -Ue(WC, { json: () => OK }); -var OK = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }]; +Ge(WC, { json: () => Gq }); +var Gq = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }]; var UC = {}; -Ue(UC, { json: () => PK }); -var PK = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }]; -var pl = class { +Ge(UC, { json: () => Hq }); +var Hq = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: 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notSupported: true }] }]; +var qC = {}; +Ge(qC, { json: () => Xq }); +var Xq = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: 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{ start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: 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"defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }]; +var XC = {}; +Ge(XC, { json: () => Qq }); +var Qq = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", 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"tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }]; +var YC = {}; +Ge(YC, { json: () => Zq }); +var Zq = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }]; +var QC = {}; +Ge(QC, { json: () => Jq }); +var Jq = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }]; +var ZC = {}; +Ge(ZC, { json: () => e6 }); +var e6 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }]; +var JC = {}; +Ge(JC, { json: () => t6 }); +var t6 = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }]; +var eS = {}; +Ge(eS, { json: () => r6 }); +var r6 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }]; +var ml = class { constructor() { - let e = [kC, NC, TC, _C, EC, $C, AC, RC, FC, DC, OC, PC, MC, LC, BC, VC, zC, WC, UC], t6 = [].concat(...e.map((o) => o.json)); - this.opMappers = t6.reduce((o, n) => (o[n.tfOpName] = n, o), {}); + let e = [PC, MC, LC, BC, VC, zC, WC, UC, GC, HC, KC, qC, jC, XC, YC, QC, ZC, JC, eS], t10 = [].concat(...e.map((o) => o.json)); + this.opMappers = t10.reduce((o, n) => (o[n.tfOpName] = n, o), {}); } static get Instance() { return this._instance || (this._instance = new this()); } - transformGraph(e, t6 = {}) { + transformGraph(e, t10 = {}) { let o = e.node, n = [], s = [], a = [], i = o.reduce((h, g) => (h[g.name] = this.mapNode(g), g.op.startsWith("Placeholder") ? n.push(h[g.name]) : g.op === "Const" ? s.push(h[g.name]) : (g.input == null || g.input.length === 0) && a.push(h[g.name]), h), {}), p = [], u = [], c = {}, l = {}; - t6 != null && (c = this.mapSignatureEntries(t6.inputs), l = this.mapSignatureEntries(t6.outputs)); + t10 != null && (c = this.mapSignatureEntries(t10.inputs), l = this.mapSignatureEntries(t10.outputs)); let m = Object.keys(i); m.forEach((h) => { let g = i[h]; g.inputNames.forEach((x, b) => { - let [C, , w] = ss(x), k = i[C]; + let [C, , w] = as(x), k = i[C]; if (k.outputs != null) { let _ = k.outputs.indexOf(w); if (_ !== -1) { - let $ = `${C}:${_}`; - g.inputNames[b] = $; + let E = `${C}:${_}`; + g.inputNames[b] = E; } } g.inputs.push(k), k.children.push(g); @@ -9509,29 +9498,29 @@ var pl = class { let g = i[h]; g.children.length === 0 && u.push(g); }) : Object.keys(l).forEach((h) => { - let [g] = ss(h), x = i[g]; + let [g] = as(h), x = i[g]; x != null && (x.signatureKey = l[h], u.push(x)); }), Object.keys(c).length > 0 ? Object.keys(c).forEach((h) => { - let [g] = ss(h), x = i[g]; + let [g] = as(h), x = i[g]; x && (x.signatureKey = c[h], p.push(x)); }) : p = n; let d = {}; e.library != null && e.library.function != null && (d = e.library.function.reduce((h, g) => (h[g.signature.name] = this.mapFunction(g), h), {})); - let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t6, functions: d }; + let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t10, functions: d }; return a.length > 0 && (f.initNodes = a), f; } mapSignatureEntries(e) { - return Object.keys(e || {}).reduce((t6, o) => (t6[e[o].name] = o, t6), {}); + return Object.keys(e || {}).reduce((t10, o) => (t10[e[o].name] = o, t10), {}); } mapNode(e) { - let t6 = Gd(e.op) || this.opMappers[e.op] || {}; + let t10 = Gd(e.op) || this.opMappers[e.op] || {}; e.attr == null && (e.attr = {}); - let o = { name: e.name, op: e.op, category: t6.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t6.outputs }; - return t6.inputs != null && (o.inputParams = t6.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t6.attrs != null && (o.attrParams = t6.attrs.reduce((n, s) => { + let o = { name: e.name, op: e.op, category: t10.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t10.outputs }; + return t10.inputs != null && (o.inputParams = t10.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t10.attrs != null && (o.attrParams = t10.attrs.reduce((n, s) => { let a = s.type, i; switch (s.type) { case "string": - i = qd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = qd(e.attr, s.tfDeprecatedName, s.defaultValue)); + i = Kd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = Kd(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "string[]": i = Jd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = Jd(e.attr, s.tfDeprecatedName, s.defaultValue)); @@ -9543,7 +9532,7 @@ var pl = class { i = Zd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = Zd(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "bool": - i = Kd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = Kd(e.attr, s.tfDeprecatedName, s.defaultValue)); + i = qd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = qd(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "bool[]": i = tf(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = tf(e.attr, s.tfDeprecatedName, s.defaultValue)); @@ -9561,7 +9550,7 @@ var pl = class { i = Yd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = Yd(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "func": - i = kN(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = kN(e.attr, s.tfDeprecatedName, s.defaultValue)); + i = AN(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = AN(e.attr, s.tfDeprecatedName, s.defaultValue)); break; case "tensor": case "tensors": @@ -9573,16 +9562,16 @@ var pl = class { }, {})), o; } mapFunction(e) { - let t6 = e.nodeDef, o = [], n = [], s = {}; - t6 != null && (s = t6.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {})); + let t10 = e.nodeDef, o = [], n = [], s = {}; + t10 != null && (s = t10.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {})); let a = [], i = []; e.signature.inputArg.forEach((l) => { - let [m] = ss(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: GC(l.type), type: "dtype" } }, children: [] }; + let [m] = as(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: tS(l.type), type: "dtype" } }, children: [] }; d.signatureKey = l.name, a.push(d), s[m] = d; }), Object.keys(s).forEach((l) => { let m = s[l]; m.inputNames.forEach((d, f) => { - let [h, , g] = ss(d), x = s[h]; + let [h, , g] = as(d), x = s[h]; if (x.outputs != null) { let b = x.outputs.indexOf(g); if (b !== -1) { @@ -9595,21 +9584,21 @@ var pl = class { }); let u = e.ret; e.signature.outputArg.forEach((l) => { - let [m, d] = ss(u[l.name]), f = s[m]; + let [m, d] = as(u[l.name]), f = s[m]; f != null && (f.defaultOutput = d, i.push(f)); }); let c = this.mapArgsToSignature(e); return { nodes: s, inputs: a, outputs: i, weights: n, placeholders: o, signature: c }; } mapArgsToSignature(e) { - return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t6, o) => (t6[o.name] = this.mapArgToTensorInfo(o), t6), {}), outputs: e.signature.outputArg.reduce((t6, o) => (t6[o.name] = this.mapArgToTensorInfo(o, e.ret), t6), {}) }; + return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o), t10), {}), outputs: e.signature.outputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o, e.ret), t10), {}) }; } - mapArgToTensorInfo(e, t6) { + mapArgToTensorInfo(e, t10) { let o = e.name; - return t6 != null && (o = t6[o]), { name: o, dtype: e.type }; + return t10 != null && (o = t10[o]), { name: o, dtype: e.type }; } }; -function MK(r) { +function o6(r) { let e = O().global; if (typeof e.atob != "undefined") return e.atob(r); @@ -9617,23 +9606,23 @@ function MK(r) { return new Buffer(r, "base64").toString(); throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()"); } -function NN(r, e) { - let t6 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : MK(r); - return e ? t6 : t6.toLowerCase(); +function RN(r, e) { + let t10 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : o6(r); + return e ? t10 : t10.toLowerCase(); } -function qd(r, e, t6, o = false) { +function Kd(r, e, t10, o = false) { let n = r[e]; - return n != null ? NN(n.s, o) : t6; + return n != null ? RN(n.s, o) : t10; } -function Kd(r, e, t6) { +function qd(r, e, t10) { let o = r[e]; - return o ? o.b : t6; + return o ? o.b : t10; } -function jd(r, e, t6) { - let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t6; +function jd(r, e, t10) { + let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t10; return typeof n == "number" ? n : parseInt(n, 10); } -function GC(r) { +function tS(r) { switch (typeof r == "string" && (r = ao[r]), r) { case ao.DT_FLOAT: case ao.DT_HALF: @@ -9653,224 +9642,224 @@ function GC(r) { return null; } } -function kN(r, e, t6) { +function AN(r, e, t10) { let o = r[e]; - return o && o.func ? o.func.name : t6; + return o && o.func ? o.func.name : t10; } -function Xd(r, e, t6) { +function Xd(r, e, t10) { let o = r[e]; - return o && o.type ? GC(o.type) : t6; + return o && o.type ? tS(o.type) : t10; } -function Yd(r, e, t6) { +function Yd(r, e, t10) { let o = r[e]; - return o && o.list && o.list.type ? o.list.type.map((n) => GC(n)) : t6; + return o && o.list && o.list.type ? o.list.type.map((n) => tS(n)) : t10; } -function TN(r) { +function DN(r) { if (!r.unknownRank) return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : []; } -function Qd(r, e, t6) { +function Qd(r, e, t10) { let o = r[e]; - return o && o.shape ? TN(o.shape) : t6; + return o && o.shape ? DN(o.shape) : t10; } -function Zd(r, e, t6) { +function Zd(r, e, t10) { let o = r[e]; - return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t6; + return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t10; } -function Jd(r, e, t6, o = false) { +function Jd(r, e, t10, o = false) { let n = r[e]; - return n && n.list && n.list.s ? n.list.s.map((s) => NN(s, o)) : t6; + return n && n.list && n.list.s ? n.list.s.map((s) => RN(s, o)) : t10; } -function ef(r, e, t6) { +function ef(r, e, t10) { let o = r[e]; - return o && o.list && o.list.shape ? o.list.shape.map((n) => TN(n)) : t6; + return o && o.list && o.list.shape ? o.list.shape.map((n) => DN(n)) : t10; } -function tf(r, e, t6) { +function tf(r, e, t10) { let o = r[e]; - return o && o.list && o.list.b ? o.list.b : t6; + return o && o.list && o.list.b ? o.list.b : t10; } var rf = class { - constructor(e, t6, o) { - this.node = e, this.tensorMap = t6, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {})); + constructor(e, t10, o) { + this.node = e, this.tensorMap = t10, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {})); } getInput(e) { return Gt(e, this.tensorMap, this.context); } - getAttr(e, t6) { + getAttr(e, t10) { let o = this.node.rawAttrs[e]; if (o.tensor != null) return Gt(e, this.tensorMap, this.context); if (o.i != null || o.f != null) - return jd(this.node.rawAttrs, e, t6); + return jd(this.node.rawAttrs, e, t10); if (o.s != null) - return qd(this.node.rawAttrs, e, t6); + return Kd(this.node.rawAttrs, e, t10); if (o.b != null) - return Kd(this.node.rawAttrs, e, t6); + return qd(this.node.rawAttrs, e, t10); if (o.shape != null) - return Qd(this.node.rawAttrs, e, t6); + return Qd(this.node.rawAttrs, e, t10); if (o.type != null) - return Xd(this.node.rawAttrs, e, t6); + return Xd(this.node.rawAttrs, e, t10); if (o.list != null) { if (o.list.i != null || o.list.f != null) - return Zd(this.node.rawAttrs, e, t6); + return Zd(this.node.rawAttrs, e, t10); if (o.list.s != null) - return Jd(this.node.rawAttrs, e, t6); + return Jd(this.node.rawAttrs, e, t10); if (o.list.shape != null) - return ef(this.node.rawAttrs, e, t6); + return ef(this.node.rawAttrs, e, t10); if (o.list.b != null) - return tf(this.node.rawAttrs, e, t6); + return tf(this.node.rawAttrs, e, t10); if (o.list.type != null) - return Yd(this.node.rawAttrs, e, t6); + return Yd(this.node.rawAttrs, e, t10); } - return t6; + return t10; } }; var Ye = {}; -Ue(Ye, { OP_SCOPE_SUFFIX: () => Lb, abs: () => Yt, acos: () => fv, acosh: () => hv, add: () => xe, addN: () => gv, all: () => xv, any: () => yv, argMax: () => bv, argMin: () => Cv, asin: () => Sv, asinh: () => wv, atan: () => Iv, atan2: () => vv, atanh: () => kv, avgPool: () => td, avgPool3d: () => _v, basicLSTMCell: () => Ev, batchNorm: () => wi, batchNorm2d: () => Av, batchNorm3d: () => Rv, batchNorm4d: () => Fv, batchToSpaceND: () => rd, bincount: () => od, booleanMaskAsync: () => XG, broadcastArgs: () => Dv, broadcastTo: () => Ii, buffer: () => le, cast: () => Ke, ceil: () => Ov, clipByValue: () => Pv, clone: () => Br, complex: () => Tr, concat: () => gt, concat1d: () => Mv, concat2d: () => Lv, concat3d: () => Bv, concat4d: () => Vv, conv1d: () => zv, conv2d: () => vi, conv2dTranspose: () => Wv, conv3d: () => Uv, conv3dTranspose: () => Hv, cos: () => qv, cosh: () => Kv, cosineWindow: () => il, cumprod: () => jv, cumsum: () => Xv, denseBincount: () => Yv, depthToSpace: () => Qv, depthwiseConv2d: () => Bp, diag: () => Zv, dilation2d: () => Jv, div: () => Ge, divNoNan: () => ek, dot: () => tk, dropout: () => aH, einsum: () => rk, elu: () => ad, enclosingPowerOfTwo: () => xC, equal: () => sd, erf: () => ok, euclideanNorm: () => ak, exp: () => Co, expandDims: () => Fa, expm1: () => ik, eye: () => id, fft: () => zp, fill: () => Ws, floor: () => ud, floorDiv: () => Jm, fused: () => yC, gather: () => pd, gatherND: () => nH, greater: () => cu, greaterEqual: () => cd, ifft: () => hu, imag: () => Si, image: () => uq, inTopKAsync: () => uH, irfft: () => Fd, isFinite: () => uk, isInf: () => pk, isNaN: () => ck, leakyRelu: () => ld, less: () => lk, lessEqual: () => Vp, linalg: () => pq, linspace: () => mk, localResponseNormalization: () => dk, log: () => Da, log1p: () => md, logSigmoid: () => fk, logSoftmax: () => hk, logSumExp: () => hd, logicalAnd: () => lu, logicalNot: () => gd, logicalOr: () => xd, logicalXor: () => gk, losses: () => cq, lowerBound: () => xk, matMul: () => Xe, max: () => Us, maxPool: () => bd, maxPool3d: () => yk, maxPoolWithArgmax: () => bk, maximum: () => Cd, mean: () => mu, meshgrid: () => Ck, min: () => sl, minimum: () => Sd, mirrorPad: () => Sk, mod: () => wk, moments: () => Ik, movingAverage: () => QG, mul: () => ae, multiRNNCell: () => vk, multinomial: () => kk, neg: () => yr, norm: () => pu, notEqual: () => wd, oneHot: () => tl, ones: () => Gs, onesLike: () => Nk, op: () => N, outerProduct: () => Tk, pad: () => Hs, pad1d: () => _k, pad2d: () => Ek, pad3d: () => $k, pad4d: () => Ak, pool: () => Rk, pow: () => Ra, prelu: () => vd, print: () => Gm, prod: () => Fk, raggedGather: () => Dk, raggedRange: () => Ok, raggedTensorToTensor: () => Pk, rand: () => Mk, randomGamma: () => e1, randomNormal: () => Ed, randomStandardNormal: () => t1, randomUniform: () => $d, range: () => Ni, real: () => $a, reciprocal: () => r1, relu: () => Ti, relu6: () => Ad, reshape: () => z, reverse: () => no, reverse1d: () => o1, reverse2d: () => n1, reverse3d: () => s1, reverse4d: () => a1, rfft: () => Wp, round: () => Rd, rsqrt: () => i1, scalar: () => be, scatterND: () => JG, searchSorted: () => al, selu: () => u1, separableConv2d: () => p1, setdiff1dAsync: () => c1, sigmoid: () => zs, sign: () => l1, signal: () => iq, sin: () => m1, sinh: () => d1, slice: () => He, slice1d: () => f1, slice2d: () => h1, slice3d: () => g1, slice4d: () => x1, softmax: () => y1, softplus: () => fd, spaceToBatchND: () => Id, sparse: () => lq, sparseToDense: () => rH, spectral: () => aq, split: () => Oa, sqrt: () => $r, square: () => Qt, squaredDifference: () => Dd, squeeze: () => Up, stack: () => Sr, step: () => Od, stridedSlice: () => b1, string: () => mq, sub: () => Ne, sum: () => et, tan: () => C1, tanh: () => nl, tensor: () => nr, tensor1d: () => mr, tensor2d: () => _i, tensor3d: () => Xm, tensor4d: () => S1, tensor5d: () => w1, tensor6d: () => I1, tile: () => ki, topk: () => v1, transpose: () => Mp, truncatedNormal: () => k1, unique: () => N1, unsortedSegmentSum: () => T1, unstack: () => so, upperBound: () => _1, variable: () => E1, where: () => os, whereAsync: () => Md, zeros: () => Vr, zerosLike: () => Ut }); -var _N = (r, e, t6, o = Ye) => { +Ge(Ye, { OP_SCOPE_SUFFIX: () => jb, abs: () => Yt, acos: () => G0, acosh: () => H0, add: () => be, addN: () => K0, all: () => q0, any: () => j0, argMax: () => X0, argMin: () => Y0, asin: () => Q0, asinh: () => Z0, atan: () => J0, atan2: () => ev, atanh: () => tv, avgPool: () => qm, avgPool3d: () => nv, basicLSTMCell: () => sv, batchNorm: () => _i, batchNorm2d: () => iv, batchNorm3d: () => uv, batchNorm4d: () => pv, batchToSpaceND: () => jm, bincount: () => Xm, booleanMaskAsync: () => VG, broadcastArgs: () => cv, broadcastTo: () => $i, buffer: () => le, cast: () => je, ceil: () => lv, clipByValue: () => mv, clone: () => Lr, complex: () => Tr, concat: () => xt, concat1d: () => dv, concat2d: () => fv, concat3d: () => hv, concat4d: () => gv, conv1d: () => xv, conv2d: () => Ei, conv2dTranspose: () => yv, conv3d: () => bv, conv3dTranspose: () => Sv, cos: () => wv, cosh: () => Iv, cosineWindow: () => al, cumprod: () => vv, cumsum: () => kv, denseBincount: () => Nv, depthToSpace: () => Tv, depthwiseConv2d: () => Bp, diag: () => _v, dilation2d: () => $v, div: () => He, divNoNan: () => Av, dot: () => Rv, dropout: () => ZG, einsum: () => Dv, elu: () => Jm, enclosingPowerOfTwo: () => CC, equal: () => Zm, erf: () => Fv, euclideanNorm: () => Mv, exp: () => So, expandDims: () => Ra, expm1: () => Lv, eye: () => ed, fft: () => zp, fill: () => Us, floor: () => td, floorDiv: () => Km, fused: () => SC, gather: () => rd, gatherND: () => YG, greater: () => uu, greaterEqual: () => od, ifft: () => du, imag: () => Ri, image: () => eK, inTopKAsync: () => eH, irfft: () => Td, isFinite: () => Bv, isInf: () => Vv, isNaN: () => zv, leakyRelu: () => nd, less: () => Wv, lessEqual: () => Vp, linalg: () => tK, linspace: () => Uv, localResponseNormalization: () => Gv, log: () => Da, log1p: () => sd, logSigmoid: () => Hv, logSoftmax: () => Kv, logSumExp: () => ud, logicalAnd: () => pu, logicalNot: () => pd, logicalOr: () => cd, logicalXor: () => qv, losses: () => rK, lowerBound: () => jv, matMul: () => Xe, max: () => Gs, maxPool: () => md, maxPool3d: () => Xv, maxPoolWithArgmax: () => Yv, maximum: () => dd, mean: () => cu, meshgrid: () => Qv, min: () => rl, minimum: () => fd, mirrorPad: () => Zv, mod: () => Jv, moments: () => ek, movingAverage: () => UG, mul: () => ne, multiRNNCell: () => tk, multinomial: () => rk, neg: () => Cr, norm: () => iu, notEqual: () => hd, oneHot: () => nl, ones: () => Hs, onesLike: () => ok, op: () => N, outerProduct: () => nk, pad: () => Ks, pad1d: () => sk, pad2d: () => ak, pad3d: () => ik, pad4d: () => uk, pool: () => pk, pow: () => Aa, prelu: () => xd, print: () => Hm, prod: () => ck, raggedGather: () => lk, raggedRange: () => mk, raggedTensorToTensor: () => dk, rand: () => fk, randomGamma: () => Ok, randomNormal: () => Id, randomStandardNormal: () => Pk, randomUniform: () => vd, range: () => Di, real: () => Fa, reciprocal: () => Mk, relu: () => Fi, relu6: () => kd, reshape: () => W, reverse: () => no, reverse1d: () => Lk, reverse2d: () => Bk, reverse3d: () => Vk, reverse4d: () => zk, rfft: () => Wp, round: () => Nd, rsqrt: () => Wk, scalar: () => Ce, scatterND: () => HG, searchSorted: () => ol, selu: () => Uk, separableConv2d: () => Gk, setdiff1dAsync: () => Hk, sigmoid: () => Ws, sign: () => Kk, signal: () => JH, sin: () => qk, sinh: () => jk, slice: () => Ke, slice1d: () => Xk, slice2d: () => Yk, slice3d: () => Qk, slice4d: () => Zk, softmax: () => Jk, softplus: () => id, spaceToBatchND: () => gd, sparse: () => oK, sparseToDense: () => jG, spectral: () => ZH, split: () => Oa, sqrt: () => $r, square: () => Qt, squaredDifference: () => _d, squeeze: () => Up, stack: () => Sr, step: () => $d, stridedSlice: () => e1, string: () => nK, sub: () => ke, sum: () => et, tan: () => t1, tanh: () => tl, tensor: () => nr, tensor1d: () => dr, tensor2d: () => Oi, tensor3d: () => Ed, tensor4d: () => r1, tensor5d: () => o1, tensor6d: () => n1, tile: () => Ai, topk: () => s1, transpose: () => Gp, truncatedNormal: () => a1, unique: () => i1, unsortedSegmentSum: () => u1, unstack: () => so, upperBound: () => p1, variable: () => c1, where: () => ss, whereAsync: () => Rd, zeros: () => Br, zerosLike: () => Ut }); +var FN = (r, e, t10, o = Ye) => { switch (r.op) { case "BiasAdd": case "AddV2": case "Add": - return [o.add(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.add(I("a", r, e, t10), I("b", r, e, t10))]; case "AddN": - return [o.addN(I("tensors", r, e, t6))]; + return [o.addN(I("tensors", r, e, t10))]; case "FloorMod": case "Mod": - return [o.mod(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.mod(I("a", r, e, t10), I("b", r, e, t10))]; case "Mul": - return [o.mul(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.mul(I("a", r, e, t10), I("b", r, e, t10))]; case "RealDiv": case "Div": - return [o.div(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.div(I("a", r, e, t10), I("b", r, e, t10))]; case "DivNoNan": - return [o.divNoNan(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.divNoNan(I("a", r, e, t10), I("b", r, e, t10))]; case "FloorDiv": - return [o.floorDiv(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.floorDiv(I("a", r, e, t10), I("b", r, e, t10))]; case "Sub": - return [o.sub(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.sub(I("a", r, e, t10), I("b", r, e, t10))]; case "Minimum": - return [o.minimum(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.minimum(I("a", r, e, t10), I("b", r, e, t10))]; case "Maximum": - return [o.maximum(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.maximum(I("a", r, e, t10), I("b", r, e, t10))]; case "Pow": - return [o.pow(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.pow(I("a", r, e, t10), I("b", r, e, t10))]; case "SquaredDifference": - return [o.squaredDifference(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.squaredDifference(I("a", r, e, t10), I("b", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var EN = (r, e, t6, o = Ye) => { +var ON = (r, e, t10, o = Ye) => { switch (r.op) { case "Abs": case "ComplexAbs": - return [o.abs(I("x", r, e, t6))]; + return [o.abs(I("x", r, e, t10))]; case "Acos": - return [o.acos(I("x", r, e, t6))]; + return [o.acos(I("x", r, e, t10))]; case "Acosh": - return [o.acosh(I("x", r, e, t6))]; + return [o.acosh(I("x", r, e, t10))]; case "Asin": - return [o.asin(I("x", r, e, t6))]; + return [o.asin(I("x", r, e, t10))]; case "Asinh": - return [o.asinh(I("x", r, e, t6))]; + return [o.asinh(I("x", r, e, t10))]; case "Atan": - return [o.atan(I("x", r, e, t6))]; + return [o.atan(I("x", r, e, t10))]; case "Atan2": - return [o.atan2(I("x", r, e, t6), I("y", r, e, t6))]; + return [o.atan2(I("x", r, e, t10), I("y", r, e, t10))]; case "Atanh": - return [o.atanh(I("x", r, e, t6))]; + return [o.atanh(I("x", r, e, t10))]; case "Ceil": - return [o.ceil(I("x", r, e, t6))]; + return [o.ceil(I("x", r, e, t10))]; case "Complex": - return [o.complex(I("real", r, e, t6), I("imag", r, e, t6))]; + return [o.complex(I("real", r, e, t10), I("imag", r, e, t10))]; case "Cos": - return [o.cos(I("x", r, e, t6))]; + return [o.cos(I("x", r, e, t10))]; case "Cosh": - return [o.cosh(I("x", r, e, t6))]; + return [o.cosh(I("x", r, e, t10))]; case "Elu": - return [o.elu(I("x", r, e, t6))]; + return [o.elu(I("x", r, e, t10))]; case "Erf": - return [o.erf(I("x", r, e, t6))]; + return [o.erf(I("x", r, e, t10))]; case "Exp": - return [o.exp(I("x", r, e, t6))]; + return [o.exp(I("x", r, e, t10))]; case "Expm1": - return [o.expm1(I("x", r, e, t6))]; + return [o.expm1(I("x", r, e, t10))]; case "Floor": - return [o.floor(I("x", r, e, t6))]; + return [o.floor(I("x", r, e, t10))]; case "Log": - return [o.log(I("x", r, e, t6))]; + return [o.log(I("x", r, e, t10))]; case "Log1p": - return [o.log1p(I("x", r, e, t6))]; + return [o.log1p(I("x", r, e, t10))]; case "Imag": - return [o.imag(I("x", r, e, t6))]; + return [o.imag(I("x", r, e, t10))]; case "Neg": - return [o.neg(I("x", r, e, t6))]; + return [o.neg(I("x", r, e, t10))]; case "Reciprocal": - return [o.reciprocal(I("x", r, e, t6))]; + return [o.reciprocal(I("x", r, e, t10))]; case "Real": - return [o.real(I("x", r, e, t6))]; + return [o.real(I("x", r, e, t10))]; case "Relu": - return [o.relu(I("x", r, e, t6))]; + return [o.relu(I("x", r, e, t10))]; case "Round": - return [o.round(I("x", r, e, t6))]; + return [o.round(I("x", r, e, t10))]; case "Selu": - return [o.selu(I("x", r, e, t6))]; + return [o.selu(I("x", r, e, t10))]; case "Sigmoid": - return [o.sigmoid(I("x", r, e, t6))]; + return [o.sigmoid(I("x", r, e, t10))]; case "Sin": - return [o.sin(I("x", r, e, t6))]; + return [o.sin(I("x", r, e, t10))]; case "Sign": - return [o.sign(I("x", r, e, t6))]; + return [o.sign(I("x", r, e, t10))]; case "Sinh": - return [o.sinh(I("x", r, e, t6))]; + return [o.sinh(I("x", r, e, t10))]; case "Softplus": - return [o.softplus(I("x", r, e, t6))]; + return [o.softplus(I("x", r, e, t10))]; case "Sqrt": - return [o.sqrt(I("x", r, e, t6))]; + return [o.sqrt(I("x", r, e, t10))]; case "Square": - return [o.square(I("x", r, e, t6))]; + return [o.square(I("x", r, e, t10))]; case "Tanh": - return [o.tanh(I("x", r, e, t6))]; + return [o.tanh(I("x", r, e, t10))]; case "Tan": - return [o.tan(I("x", r, e, t6))]; + return [o.tan(I("x", r, e, t10))]; case "ClipByValue": - return [o.clipByValue(I("x", r, e, t6), I("clipValueMin", r, e, t6), I("clipValueMax", r, e, t6))]; + return [o.clipByValue(I("x", r, e, t10), I("clipValueMin", r, e, t10), I("clipValueMax", r, e, t10))]; case "Relu6": - return [o.relu6(I("x", r, e, t6))]; + return [o.relu6(I("x", r, e, t10))]; case "Rsqrt": - return [o.rsqrt(Gt(r.inputNames[0], e, t6))]; + return [o.rsqrt(Gt(r.inputNames[0], e, t10))]; case "Prod": - return [o.prod(I("x", r, e, t6), I("axes", r, e, t6))]; + return [o.prod(I("x", r, e, t10), I("axes", r, e, t10))]; case "LeakyRelu": - return [o.leakyRelu(I("x", r, e, t6), I("alpha", r, e, t6))]; + return [o.leakyRelu(I("x", r, e, t10), I("alpha", r, e, t10))]; case "Prelu": - return [o.prelu(I("x", r, e, t6), I("alpha", r, e, t6))]; + return [o.prelu(I("x", r, e, t10), I("alpha", r, e, t10))]; case "IsNan": - return [o.isNaN(Gt(r.inputNames[0], e, t6))]; + return [o.isNaN(Gt(r.inputNames[0], e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function zr(r, e, t6 = "") { +function Vr(r, e, t10 = "") { if (!(typeof r == "number" || typeof e == "number")) { - y.assert(r.length === e.length, () => t6 + ` Shapes ${r} and ${e} must match`); + y.assert(r.length === e.length, () => t10 + ` Shapes ${r} and ${e} must match`); for (let o = 0; o < r.length; o++) { let n = r[o], s = e[o]; - y.assert(n < 0 || s < 0 || n === s, () => t6 + ` Shapes ${r} and ${e} must match`); + y.assert(n < 0 || s < 0 || n === s, () => t10 + ` Shapes ${r} and ${e} must match`); } } } -function $N(r) { +function PN(r) { return !(typeof r == "number" || r.some((e) => e < 0)); } -function Gp(r, e, t6) { - let o = of(r, t6), n = !$N(o); +function Hp(r, e, t10) { + let o = of(r, t10), n = !PN(o); if (n && e.length === 0) throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`); if (n && e.forEach((s) => { o = of(s.shape, o); - }), !$N(o)) + }), !PN(o)) throw new Error(`Non-fully-defined elementShape: ${o}`); return o; } @@ -9881,18 +9870,18 @@ function of(r, e) { return r; if (r.length !== e.length) throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`); - let t6 = []; + let t10 = []; for (let o = 0; o < r.length; ++o) { let n = r[o], s = e[o]; if (n >= 0 && s >= 0 && n !== s) throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`); - t6[o] = n >= 0 ? n : s; + t10[o] = n >= 0 ? n : s; } - return t6; + return t10; } var nf = class { - constructor(e, t6, o, n, s, a, i) { - this.name = e, this.dtype = t6, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = be(0), _r(this.idTensor); + constructor(e, t10, o, n, s, a, i) { + this.name = e, this.dtype = t10, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = Ce(0), _r(this.idTensor); } get id() { return this.idTensor.id; @@ -9901,8 +9890,8 @@ var nf = class { return this.closed_; } clearAndClose(e) { - this.tensors.forEach((t6) => { - (e == null || !e.has(t6.tensor.id)) && t6.tensor.dispose(); + this.tensors.forEach((t10) => { + (e == null || !e.has(t10.tensor.id)) && t10.tensor.dispose(); }), this.tensors = [], this.closed_ = true, this.idTensor.dispose(); } size() { @@ -9913,37 +9902,37 @@ var nf = class { throw new Error(`TensorArray ${this.name} has already been closed.`); if (e < 0 || e >= this.size()) throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`); - let t6 = this.tensors[e]; - if (t6.cleared) + let t10 = this.tensors[e]; + if (t10.cleared) throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`); - return this.clearAfterRead && (t6.cleared = true), t6.read = true, t6.tensor; + return this.clearAfterRead && (t10.cleared = true), t10.read = true, t10.tensor; } readMany(e) { - return e.map((t6) => this.read(t6)); + return e.map((t10) => this.read(t10)); } - write(e, t6) { + write(e, t10) { if (this.closed_) throw new Error(`TensorArray ${this.name} has already been closed.`); if (e < 0 || !this.dynamicSize && e >= this.maxSize) throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`); let o = this.tensors[e] || {}; - if (t6.dtype !== this.dtype) + if (t10.dtype !== this.dtype) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, - because the value dtype is ${t6.dtype}, but TensorArray dtype is ${this.dtype}.`); - if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t6.shape), zr(this.elementShape, t6.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read) + because the value dtype is ${t10.dtype}, but TensorArray dtype is ${this.dtype}.`); + if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t10.shape), Vr(this.elementShape, t10.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`); if (o.written) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`); - o.tensor = t6, _r(t6), o.written = true, this.tensors[e] = o; + o.tensor = t10, _r(t10), o.written = true, this.tensors[e] = o; } - writeMany(e, t6) { - if (e.length !== t6.length) - throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t6.length}.`); - e.forEach((o, n) => this.write(o, t6[n])); + writeMany(e, t10) { + if (e.length !== t10.length) + throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t10.length}.`); + e.forEach((o, n) => this.write(o, t10[n])); } - gather(e, t6) { - if (!!t6 && t6 !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t6}`); + gather(e, t10) { + if (!!t10 && t10 !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t10}`); if (e) e = e.slice(0, this.size()); else { @@ -9954,45 +9943,45 @@ var nf = class { if (e.length === 0) return nr([], [0].concat(this.elementShape)); let o = this.readMany(e); - return zr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), Sr(o, 0); + return Vr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), Sr(o, 0); } concat(e) { if (!!e && e !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`); if (this.size() === 0) return nr([], [0].concat(this.elementShape)); - let t6 = []; + let t10 = []; for (let n = 0; n < this.size(); n++) - t6.push(n); - let o = this.readMany(t6); - return zr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), gt(o, 0); + t10.push(n); + let o = this.readMany(t10); + return Vr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), xt(o, 0); } - scatter(e, t6) { - if (t6.dtype !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t6.dtype}`); - if (e.length !== t6.shape[0]) - throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t6.shape[0]}`); + scatter(e, t10) { + if (t10.dtype !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`); + if (e.length !== t10.shape[0]) + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t10.shape[0]}`); let o = Math.max(...e); if (!this.dynamicSize && o >= this.maxSize) throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`); - this.writeMany(e, so(t6, 0)); + this.writeMany(e, so(t10, 0)); } - split(e, t6) { - if (t6.dtype !== this.dtype) - throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t6.dtype}`); + split(e, t10) { + if (t10.dtype !== this.dtype) + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`); let o = 0, n = e.map((p) => (o += p, o)); - if (o !== t6.shape[0]) + if (o !== t10.shape[0]) throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${o}, and tensor's shape is: ${t6.shape}`); + ${o}, and tensor's shape is: ${t10.shape}`); if (!this.dynamicSize && e.length !== this.maxSize) throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`); - let s = o === 0 ? 0 : t6.size / o, a = []; + let s = o === 0 ? 0 : t10.size / o, a = []; Ee(() => { - t6 = z(t6, [1, o, s]); + t10 = W(t10, [1, o, s]); for (let p = 0; p < e.length; ++p) { let c = [0, p === 0 ? 0 : n[p - 1], 0], l = [1, e[p], s]; - a[p] = z(He(t6, c, l), this.elementShape); + a[p] = W(Ke(t10, c, l), this.elementShape); } return a; }); @@ -10002,52 +9991,52 @@ var nf = class { this.writeMany(i, a); } }; -var Pa = class { - constructor(e, t6, o, n = -1) { - this.tensors = e, this.elementShape = t6, this.elementDtype = o, e != null && e.forEach((s) => { +var Ba = class { + constructor(e, t10, o, n = -1) { + this.tensors = e, this.elementShape = t10, this.elementDtype = o, e != null && e.forEach((s) => { if (o !== s.dtype) throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`); - zr(t6, s.shape, "TensorList shape mismatch: "), _r(s); - }), this.idTensor = be(0), this.maxNumElements = n, _r(this.idTensor); + Vr(t10, s.shape, "TensorList shape mismatch: "), _r(s); + }), this.idTensor = Ce(0), this.maxNumElements = n, _r(this.idTensor); } get id() { return this.idTensor.id; } copy() { - return new Pa([...this.tensors], this.elementShape, this.elementDtype); + return new Ba([...this.tensors], this.elementShape, this.elementDtype); } clearAndClose(e) { - this.tensors.forEach((t6) => { - (e == null || !e.has(t6.id)) && t6.dispose(); + this.tensors.forEach((t10) => { + (e == null || !e.has(t10.id)) && t10.dispose(); }), this.tensors.length = 0, this.idTensor.dispose(); } size() { return this.tensors.length; } - stack(e, t6, o = -1) { - if (t6 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t6}, but list elements ${this.elementDtype}`); + stack(e, t10, o = -1) { + if (t10 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); if (o !== -1 && this.tensors.length !== o) throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`); - zr(e, this.elementShape, "TensorList shape mismatch: "); - let n = Gp(this.elementShape, this.tensors, e); + Vr(e, this.elementShape, "TensorList shape mismatch: "); + let n = Hp(this.elementShape, this.tensors, e); return Ee(() => { - let s = this.tensors.map((a) => z(a, n)); + let s = this.tensors.map((a) => W(a, n)); return Sr(s, 0); }); } - popBack(e, t6) { - if (t6 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t6}, but list elements ${this.elementDtype}`); + popBack(e, t10) { + if (t10 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); if (this.size() === 0) throw new Error("Trying to pop from an empty list."); - let o = Gp(this.elementShape, this.tensors, e), n = this.tensors.pop(); - return n.kept = false, zr(n.shape, e, "TensorList shape mismatch: "), z(n, o); + let o = Hp(this.elementShape, this.tensors, e), n = this.tensors.pop(); + return n.kept = false, Vr(n.shape, e, "TensorList shape mismatch: "), W(n, o); } pushBack(e) { if (e.dtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`); - if (zr(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) + if (Vr(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) throw new Error("Trying to push element into a full list."); _r(e), this.tensors.push(e); } @@ -10056,117 +10045,117 @@ var Pa = class { throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`); if (this.maxNumElements !== -1 && e > this.maxNumElements) throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`); - let t6 = new Pa([], this.elementShape, this.elementDtype, this.maxNumElements); - t6.tensors.length = e; + let t10 = new Ba([], this.elementShape, this.elementDtype, this.maxNumElements); + t10.tensors.length = e; for (let o = 0; o < Math.min(this.tensors.length, e); ++o) - t6.tensors[o] = this.tensors[o]; - return t6; + t10.tensors[o] = this.tensors[o]; + return t10; } - getItem(e, t6, o) { + getItem(e, t10, o) { if (o !== this.elementDtype) throw new Error(`Invalid data types; op elements ${o}, but list elements ${this.elementDtype}`); if (e < 0 || e > this.tensors.length) throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`); if (this.tensors[e] == null) throw new Error(`element at index ${e} is null.`); - zr(this.tensors[e].shape, t6, "TensorList shape mismatch: "); - let n = Gp(this.elementShape, this.tensors, t6); - return z(this.tensors[e], n); + Vr(this.tensors[e].shape, t10, "TensorList shape mismatch: "); + let n = Hp(this.elementShape, this.tensors, t10); + return W(this.tensors[e], n); } - setItem(e, t6) { - if (t6.dtype !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t6.dtype}, but list elements ${this.elementDtype}`); + setItem(e, t10) { + if (t10.dtype !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t10.dtype}, but list elements ${this.elementDtype}`); if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements) throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`); - zr(this.elementShape, t6.shape, "TensorList shape mismatch: "), _r(t6), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t6; + Vr(this.elementShape, t10.shape, "TensorList shape mismatch: "), _r(t10), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t10; } - gather(e, t6, o) { - if (t6 !== this.elementDtype) - throw new Error(`Invalid data types; op elements ${t6}, but list elements ${this.elementDtype}`); - zr(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size()); - let n = Gp(this.elementShape, this.tensors, o); + gather(e, t10, o) { + if (t10 !== this.elementDtype) + throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`); + Vr(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size()); + let n = Hp(this.elementShape, this.tensors, o); return e.length === 0 ? nr([], [0].concat(n)) : Ee(() => { - let s = e.map((a) => z(this.tensors[a], n)); + let s = e.map((a) => W(this.tensors[a], n)); return Sr(s, 0); }); } - concat(e, t6) { + concat(e, t10) { if (!!e && e !== this.elementDtype) throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`); - zr(this.elementShape, t6, "TensorList shape mismatch: "); - let o = Gp(this.elementShape, this.tensors, t6); + Vr(this.elementShape, t10, "TensorList shape mismatch: "); + let o = Hp(this.elementShape, this.tensors, t10); return this.size() === 0 ? nr([], [0].concat(o)) : Ee(() => { - let n = this.tensors.map((s) => z(s, o)); - return gt(n, 0); + let n = this.tensors.map((s) => W(s, o)); + return xt(n, 0); }); } }; -function AN(r, e, t6) { +function MN(r, e, t10) { let o = r.dtype; if (r.shape.length < 1) throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`); - if (r.dtype !== t6) - throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t6}`); + if (r.dtype !== t10) + throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t10}`); let n = r.shape.slice(1); - zr(n, e, "TensorList shape mismatch: "); + Vr(n, e, "TensorList shape mismatch: "); let s = so(r); - return new Pa(s, e, o); + return new Ba(s, e, o); } -function RN(r, e, t6, o) { - return new Pa([], r, e, o); +function LN(r, e, t10, o) { + return new Ba([], r, e, o); } -function FN(r, e, t6, o) { +function BN(r, e, t10, o) { if (e.length !== r.shape[0]) throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`); let n = Math.max(...e); if (o != null && o !== -1 && n >= o) throw new Error(`Max index must be < array size (${n} vs. ${o})`); - let s = new Pa([], t6, r.dtype, o), a = so(r, 0); + let s = new Ba([], t10, r.dtype, o), a = so(r, 0); return e.forEach((i, p) => { s.setItem(i, a[p]); }), s; } -function DN(r, e, t6) { +function VN(r, e, t10) { let o = 0, n = e.map((c) => (o += c, o)); if (o !== r.shape[0]) throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${o}, and tensor's shape is: ${r.shape}`); - let s = r.shape.slice(1), a = of(s, t6), i = o === 0 ? 0 : r.size / o, p = Ee(() => { + let s = r.shape.slice(1), a = of(s, t10), i = o === 0 ? 0 : r.size / o, p = Ee(() => { let c = []; - r = z(r, [1, o, i]); + r = W(r, [1, o, i]); for (let l = 0; l < e.length; ++l) { let d = [0, l === 0 ? 0 : n[l - 1], 0], f = [1, e[l], i]; - c[l] = z(He(r, d, f), a); + c[l] = W(Ke(r, d, f), a); } return r.dispose(), c; - }), u = new Pa([], t6, r.dtype, e.length); + }), u = new Ba([], t10, r.dtype, e.length); for (let c = 0; c < p.length; c++) u.setItem(c, p[c]); return u; } -var ON = async (r, e, t6) => { +var zN = async (r, e, t10) => { switch (r.op) { case "If": case "StatelessIf": { - let o = I("thenBranch", r, e, t6), n = I("elseBranch", r, e, t6), s = I("cond", r, e, t6), a = I("args", r, e, t6); - return (await s.data())[0] ? t6.functionMap[o].executeFunctionAsync(a, t6.tensorArrayMap, t6.tensorListMap) : t6.functionMap[n].executeFunctionAsync(a, t6.tensorArrayMap, t6.tensorListMap); + let o = I("thenBranch", r, e, t10), n = I("elseBranch", r, e, t10), s = I("cond", r, e, t10), a = I("args", r, e, t10); + return (await s.data())[0] ? t10.functionMap[o].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap) : t10.functionMap[n].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap); } case "While": case "StatelessWhile": { - let o = I("body", r, e, t6), n = I("cond", r, e, t6), s = I("args", r, e, t6), a = await t6.functionMap[n].executeFunctionAsync(s, t6.tensorArrayMap, t6.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); + let o = I("body", r, e, t10), n = I("cond", r, e, t10), s = I("args", r, e, t10), a = await t10.functionMap[n].executeFunctionAsync(s, t10.tensorArrayMap, t10.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); a.forEach((c) => { !c.kept && i.indexOf(c.id) === -1 && c.dispose(); }); let u = s; for (; p[0]; ) { let c = u; - u = await t6.functionMap[o].executeFunctionAsync(u, t6.tensorArrayMap, t6.tensorListMap); + u = await t10.functionMap[o].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap); let l = u.map((d) => d.id); c.forEach((d) => { !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose(); }); - let m = await t6.functionMap[n].executeFunctionAsync(u, t6.tensorArrayMap, t6.tensorListMap); + let m = await t10.functionMap[n].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap); p = await m[0].data(), m.forEach((d) => { !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose(); }); @@ -10174,132 +10163,132 @@ var ON = async (r, e, t6) => { return u; } case "LoopCond": { - let o = I("pred", r, e, t6); - return [as(o)]; + let o = I("pred", r, e, t10); + return [is(o)]; } case "Switch": { - let o = I("pred", r, e, t6), n = I("data", r, e, t6); - return n.kept || (n = as(n)), (await o.data())[0] ? [void 0, n] : [n, void 0]; + let o = I("pred", r, e, t10), n = I("data", r, e, t10); + return n.kept || (n = is(n)), (await o.data())[0] ? [void 0, n] : [n, void 0]; } case "Merge": { - let o = r.inputNames.find((n) => Gt(n, e, t6) !== void 0); + let o = r.inputNames.find((n) => Gt(n, e, t10) !== void 0); if (o) { - let n = Gt(o, e, t6); - return [as(n)]; + let n = Gt(o, e, t10); + return [is(n)]; } return; } case "Enter": { - let o = I("frameName", r, e, t6), n = I("tensor", r, e, t6); - return t6.enterFrame(o), [as(n)]; + let o = I("frameName", r, e, t10), n = I("tensor", r, e, t10); + return t10.enterFrame(o), [is(n)]; } case "Exit": { - let o = I("tensor", r, e, t6); - return t6.exitFrame(), [as(o)]; + let o = I("tensor", r, e, t10); + return t10.exitFrame(), [is(o)]; } case "NextIteration": { - let o = I("tensor", r, e, t6); - return t6.nextIteration(), [as(o)]; + let o = I("tensor", r, e, t10); + return t10.nextIteration(), [is(o)]; } case "TensorArrayV3": { - let o = I("size", r, e, t6), n = I("dtype", r, e, t6), s = I("elementShape", r, e, t6), a = I("dynamicSize", r, e, t6), i = I("clearAfterRead", r, e, t6), p = I("identicalElementShapes", r, e, t6), u = I("name", r, e, t6), c = new nf(u, n, o, s, p, a, i); - return t6.addTensorArray(c), [c.idTensor, be(1)]; + let o = I("size", r, e, t10), n = I("dtype", r, e, t10), s = I("elementShape", r, e, t10), a = I("dynamicSize", r, e, t10), i = I("clearAfterRead", r, e, t10), p = I("identicalElementShapes", r, e, t10), u = I("name", r, e, t10), c = new nf(u, n, o, s, p, a, i); + return t10.addTensorArray(c), [c.idTensor, Ce(1)]; } case "TensorArrayWriteV3": { - let o = I("tensorArrayId", r, e, t6), n = I("index", r, e, t6), s = I("tensor", r, e, t6), a = t6.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id); return a.write(n, s), [a.idTensor]; } case "TensorArrayReadV3": { - let o = I("tensorArrayId", r, e, t6), n = I("index", r, e, t6); - return [t6.getTensorArray(o.id).read(n)]; + let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10); + return [t10.getTensorArray(o.id).read(n)]; } case "TensorArrayGatherV3": { - let o = I("tensorArrayId", r, e, t6), n = I("indices", r, e, t6), s = I("dtype", r, e, t6); - return [t6.getTensorArray(o.id).gather(n, s)]; + let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("dtype", r, e, t10); + return [t10.getTensorArray(o.id).gather(n, s)]; } case "TensorArrayScatterV3": { - let o = I("tensorArrayId", r, e, t6), n = I("indices", r, e, t6), s = I("tensor", r, e, t6), a = t6.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id); return a.scatter(n, s), [a.idTensor]; } case "TensorArrayConcatV3": { - let o = I("tensorArrayId", r, e, t6), n = t6.getTensorArray(o.id), s = I("dtype", r, e, t6); + let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id), s = I("dtype", r, e, t10); return [n.concat(s)]; } case "TensorArraySplitV3": { - let o = I("tensorArrayId", r, e, t6), n = I("tensor", r, e, t6), s = I("lengths", r, e, t6), a = t6.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t10), n = I("tensor", r, e, t10), s = I("lengths", r, e, t10), a = t10.getTensorArray(o.id); return a.split(s, n), [a.idTensor]; } case "TensorArraySizeV3": { - let o = I("tensorArrayId", r, e, t6), n = t6.getTensorArray(o.id); - return [be(n.size(), "int32")]; + let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id); + return [Ce(n.size(), "int32")]; } case "TensorArrayCloseV3": { - let o = I("tensorArrayId", r, e, t6), n = t6.getTensorArray(o.id); + let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id); return n.clearAndClose(), [n.idTensor]; } case "TensorListSetItem": { - let o = I("tensorListId", r, e, t6), n = I("index", r, e, t6), s = I("tensor", r, e, t6), a = t6.getTensorList(o.id); + let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorList(o.id); return a.setItem(n, s), [a.idTensor]; } case "TensorListGetItem": { - let o = I("tensorListId", r, e, t6), n = I("index", r, e, t6), s = I("elementShape", r, e, t6), a = I("elementDType", r, e, t6); - return [t6.getTensorList(o.id).getItem(n, s, a)]; + let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10); + return [t10.getTensorList(o.id).getItem(n, s, a)]; } case "TensorListScatterV2": case "TensorListScatter": { - let o = I("indices", r, e, t6), n = I("tensor", r, e, t6), s = I("elementShape", r, e, t6), a = I("numElements", r, e, t6), i = FN(n, o, s, a); - return t6.addTensorList(i), [i.idTensor]; + let o = I("indices", r, e, t10), n = I("tensor", r, e, t10), s = I("elementShape", r, e, t10), a = I("numElements", r, e, t10), i = BN(n, o, s, a); + return t10.addTensorList(i), [i.idTensor]; } case "TensorListReserve": case "EmptyTensorList": { - let o = I("elementShape", r, e, t6), n = I("elementDType", r, e, t6), s; + let o = I("elementShape", r, e, t10), n = I("elementDType", r, e, t10), s; r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements"; - let a = I(s, r, e, t6), i = r.op === "TensorListReserve" ? -1 : a, p = RN(o, n, a, i); - return t6.addTensorList(p), [p.idTensor]; + let a = I(s, r, e, t10), i = r.op === "TensorListReserve" ? -1 : a, p = LN(o, n, a, i); + return t10.addTensorList(p), [p.idTensor]; } case "TensorListGather": { - let o = I("tensorListId", r, e, t6), n = I("indices", r, e, t6), s = I("elementShape", r, e, t6), a = I("elementDType", r, e, t6); - return [t6.getTensorList(o.id).gather(n, a, s)]; + let o = I("tensorListId", r, e, t10), n = I("indices", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10); + return [t10.getTensorList(o.id).gather(n, a, s)]; } case "TensorListStack": { - let o = I("tensorListId", r, e, t6), n = I("elementShape", r, e, t6), s = I("elementDType", r, e, t6), a = I("numElements", r, e, t6); - return [t6.getTensorList(o.id).stack(n, s, a)]; + let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = I("numElements", r, e, t10); + return [t10.getTensorList(o.id).stack(n, s, a)]; } case "TensorListFromTensor": { - let o = I("tensor", r, e, t6), n = I("elementShape", r, e, t6), s = I("elementDType", r, e, t6), a = AN(o, n, s); - return t6.addTensorList(a), [a.idTensor]; + let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = MN(o, n, s); + return t10.addTensorList(a), [a.idTensor]; } case "TensorListConcat": case "TensorListConcatV2": { - let o = I("tensorListId", r, e, t6), n = t6.getTensorList(o.id), s = I("dtype", r, e, t6), a = I("elementShape", r, e, t6); + let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id), s = I("dtype", r, e, t10), a = I("elementShape", r, e, t10); return [n.concat(s, a)]; } case "TensorListPushBack": { - let o = I("tensorListId", r, e, t6), n = I("tensor", r, e, t6), s = t6.getTensorList(o.id); + let o = I("tensorListId", r, e, t10), n = I("tensor", r, e, t10), s = t10.getTensorList(o.id); return s.pushBack(n), [s.idTensor]; } case "TensorListPopBack": { - let o = I("tensorListId", r, e, t6), n = I("elementShape", r, e, t6), s = I("elementDType", r, e, t6); - return [t6.getTensorList(o.id).popBack(n, s)]; + let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10); + return [t10.getTensorList(o.id).popBack(n, s)]; } case "TensorListSplit": { - let o = I("tensor", r, e, t6), n = I("elementShape", r, e, t6), s = I("lengths", r, e, t6), a = DN(o, s, n); - return t6.addTensorList(a), [a.idTensor]; + let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("lengths", r, e, t10), a = VN(o, s, n); + return t10.addTensorList(a), [a.idTensor]; } case "TensorListLength": { - let o = I("tensorListId", r, e, t6), n = t6.getTensorList(o.id); - return [be(n.size(), "int32")]; + let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id); + return [Ce(n.size(), "int32")]; } case "TensorListResize": { - let o = I("tensorListId", r, e, t6), n = I("size", r, e, t6), a = t6.getTensorList(o.id).resize(n); - return t6.addTensorList(a), [a.idTensor]; + let o = I("tensorListId", r, e, t10), n = I("size", r, e, t10), a = t10.getTensorList(o.id).resize(n); + return t10.addTensorList(a), [a.idTensor]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function PN(r, e, t6) { - let [o, n] = I("fusedOps", r, e, t6), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t6); +function WN(r, e, t10) { + let [o, n] = I("fusedOps", r, e, t10), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t10); if (s) { if (i && u !== 2) throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha."); @@ -10308,200 +10297,200 @@ function PN(r, e, t6) { } if (p) throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported"); - let c = I("strides", r, e, t6), l = ul(r, e, t6), m = I("dataFormat", r, e, t6).toUpperCase(), d = I("dilations", r, e, t6), [f, h] = I("args", r, e, t6); + let c = I("strides", r, e, t10), l = ll(r, e, t10), m = I("dataFormat", r, e, t10).toUpperCase(), d = I("dilations", r, e, t10), [f, h] = I("args", r, e, t10); a && (h = f, f = void 0); - let g = I("leakyreluAlpha", r, e, t6); + let g = I("leakyreluAlpha", r, e, t10); return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g }; } -var MN = (r, e, t6, o = Ye) => { +var UN = (r, e, t10, o = Ye) => { switch (r.op) { case "Conv1D": { - let n = I("stride", r, e, t6), s = I("pad", r, e, t6), a = I("dataFormat", r, e, t6).toUpperCase(), i = I("dilation", r, e, t6); - return [o.conv1d(I("x", r, e, t6), I("filter", r, e, t6), n, s, a, i)]; + let n = I("stride", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilation", r, e, t10); + return [o.conv1d(I("x", r, e, t10), I("filter", r, e, t10), n, s, a, i)]; } case "Conv2D": { - let n = I("strides", r, e, t6), s = ul(r, e, t6), a = I("dataFormat", r, e, t6).toUpperCase(), i = I("dilations", r, e, t6); - return [o.conv2d(I("x", r, e, t6), I("filter", r, e, t6), [n[1], n[2]], s, a, [i[1], i[2]])]; + let n = I("strides", r, e, t10), s = ll(r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10); + return [o.conv2d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, a, [i[1], i[2]])]; } case "_FusedConv2D": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = PN(r, e, t6); - return [o.fused.conv2d({ x: I("x", r, e, t6), filter: I("filter", r, e, t6), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = WN(r, e, t10); + return [o.fused.conv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "FusedDepthwiseConv2dNative": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = PN(r, e, t6); - return [o.fused.depthwiseConv2d({ x: I("x", r, e, t6), filter: I("filter", r, e, t6), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = WN(r, e, t10); + return [o.fused.depthwiseConv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "Conv2DBackpropInput": case "Conv2dTranspose": { - let n = I("outputShape", r, e, t6), s = I("strides", r, e, t6), a = ul(r, e, t6); - return [o.conv2dTranspose(I("x", r, e, t6), I("filter", r, e, t6), n, [s[1], s[2]], a)]; + let n = I("outputShape", r, e, t10), s = I("strides", r, e, t10), a = ll(r, e, t10); + return [o.conv2dTranspose(I("x", r, e, t10), I("filter", r, e, t10), n, [s[1], s[2]], a)]; } case "DepthwiseConv2dNative": case "DepthwiseConv2d": { - let n = I("strides", r, e, t6), s = ul(r, e, t6), a = I("dilations", r, e, t6), i = I("dataFormat", r, e, t6).toUpperCase(); - return [o.depthwiseConv2d(I("input", r, e, t6), I("filter", r, e, t6), [n[1], n[2]], s, i, [a[1], a[2]])]; + let n = I("strides", r, e, t10), s = ll(r, e, t10), a = I("dilations", r, e, t10), i = I("dataFormat", r, e, t10).toUpperCase(); + return [o.depthwiseConv2d(I("input", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, i, [a[1], a[2]])]; } case "Conv3D": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("dataFormat", r, e, t6).toUpperCase(), i = I("dilations", r, e, t6); - return [o.conv3d(I("x", r, e, t6), I("filter", r, e, t6), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10); + return [o.conv3d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; } case "AvgPool": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("kernelSize", r, e, t6); - return [o.avgPool(I("x", r, e, t6), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); + return [o.avgPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPool": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("kernelSize", r, e, t6); - return [o.maxPool(I("x", r, e, t6), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); + return [o.maxPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPoolWithArgmax": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("kernelSize", r, e, t6), i = I("includeBatchInIndex", r, e, t6), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t6), [a[1], a[2]], [n[1], n[2]], s, i); + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10), i = I("includeBatchInIndex", r, e, t10), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s, i); return [p, u]; } case "AvgPool3D": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("kernelSize", r, e, t6); - return [o.avgPool3d(I("x", r, e, t6), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); + return [o.avgPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "MaxPool3D": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("kernelSize", r, e, t6); - return [o.maxPool3d(I("x", r, e, t6), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10); + return [o.maxPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "Dilation2D": { - let n = I("strides", r, e, t6), s = I("pad", r, e, t6), a = I("dilations", r, e, t6), i = n[1], p = n[2], u = a[1], c = a[2]; - return [o.dilation2d(I("x", r, e, t6), I("filter", r, e, t6), [i, p], s, [u, c], "NHWC")]; + let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dilations", r, e, t10), i = n[1], p = n[2], u = a[1], c = a[2]; + return [o.dilation2d(I("x", r, e, t10), I("filter", r, e, t10), [i, p], s, [u, c], "NHWC")]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var LN = (r, e, t6, o = Ye) => { +var GN = (r, e, t10, o = Ye) => { switch (r.op) { case "Fill": { - let n = I("shape", r, e, t6), s = I("dtype", r, e, t6), a = I("value", r, e, t6); + let n = I("shape", r, e, t10), s = I("dtype", r, e, t10), a = I("value", r, e, t10); return [o.fill(n, a, s)]; } case "LinSpace": { - let n = I("start", r, e, t6), s = I("stop", r, e, t6), a = I("num", r, e, t6); + let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("num", r, e, t10); return [o.linspace(n, s, a)]; } case "Multinomial": { - let n = I("logits", r, e, t6), s = I("numSamples", r, e, t6), a = I("seed", r, e, t6); + let n = I("logits", r, e, t10), s = I("numSamples", r, e, t10), a = I("seed", r, e, t10); return [o.multinomial(n, s, a)]; } case "OneHot": { - let n = I("indices", r, e, t6), s = I("depth", r, e, t6), a = I("onValue", r, e, t6), i = I("offValue", r, e, t6), p = I("dtype", r, e, t6); + let n = I("indices", r, e, t10), s = I("depth", r, e, t10), a = I("onValue", r, e, t10), i = I("offValue", r, e, t10), p = I("dtype", r, e, t10); return [o.oneHot(n, s, a, i, p)]; } case "Ones": - return [o.ones(I("shape", r, e, t6), I("dtype", r, e, t6))]; + return [o.ones(I("shape", r, e, t10), I("dtype", r, e, t10))]; case "OnesLike": - return [o.onesLike(I("x", r, e, t6))]; + return [o.onesLike(I("x", r, e, t10))]; case "RandomStandardNormal": - return [o.randomStandardNormal(I("shape", r, e, t6), I("dtype", r, e, t6), I("seed", r, e, t6))]; + return [o.randomStandardNormal(I("shape", r, e, t10), I("dtype", r, e, t10), I("seed", r, e, t10))]; case "RandomUniform": - return [o.randomUniform(I("shape", r, e, t6), I("minval", r, e, t6), I("maxval", r, e, t6), I("dtype", r, e, t6))]; + return [o.randomUniform(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("dtype", r, e, t10))]; case "Range": { - let n = I("start", r, e, t6), s = I("stop", r, e, t6), a = I("step", r, e, t6); - return [o.range(n, s, a, I("dtype", r, e, t6))]; + let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("step", r, e, t10); + return [o.range(n, s, a, I("dtype", r, e, t10))]; } case "TruncatedNormal": { - let n = I("shape", r, e, t6), s = I("mean", r, e, t6), a = I("stdDev", r, e, t6), i = I("seed", r, e, t6); - return [o.truncatedNormal(n, s, a, I("dtype", r, e, t6), i)]; + let n = I("shape", r, e, t10), s = I("mean", r, e, t10), a = I("stdDev", r, e, t10), i = I("seed", r, e, t10); + return [o.truncatedNormal(n, s, a, I("dtype", r, e, t10), i)]; } case "Zeros": - return [o.zeros(I("shape", r, e, t6), I("dtype", r, e, t6))]; + return [o.zeros(I("shape", r, e, t10), I("dtype", r, e, t10))]; case "ZerosLike": - return [o.zerosLike(I("x", r, e, t6))]; + return [o.zerosLike(I("x", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function HC(r, e, t6) { - let o = I("boxes", r, e, t6), n = I("scores", r, e, t6), s = I("maxOutputSize", r, e, t6), a = I("iouThreshold", r, e, t6), i = I("scoreThreshold", r, e, t6), p = I("softNmsSigma", r, e, t6); +function rS(r, e, t10) { + let o = I("boxes", r, e, t10), n = I("scores", r, e, t10), s = I("maxOutputSize", r, e, t10), a = I("iouThreshold", r, e, t10), i = I("scoreThreshold", r, e, t10), p = I("softNmsSigma", r, e, t10); return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p }; } -var BN = async (r, e, t6, o, n = Ye) => { +var HN = async (r, e, t10, o, n = Ye) => { switch (r.op) { case "NonMaxSuppressionV5": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = HC(r, e, t6), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = rS(r, e, t10), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); return [l.selectedIndices, l.selectedScores]; } case "NonMaxSuppressionV4": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = HC(r, e, t6), c = I("padToMaxOutputSize", r, e, t6), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = rS(r, e, t10), c = I("padToMaxOutputSize", r, e, t10), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); return [l.selectedIndices, l.validOutputs]; } case "NonMaxSuppressionV3": case "NonMaxSuppressionV2": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = HC(r, e, t6); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = rS(r, e, t10); return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)]; } case "Where": { - let s = n.cast(I("condition", r, e, t6), "bool"), a = [await n.whereAsync(s)]; + let s = n.cast(I("condition", r, e, t10), "bool"), a = [await n.whereAsync(s)]; return s.dispose(), a; } case "ListDiff": - return n.setdiff1dAsync(I("x", r, e, t6), I("y", r, e, t6)); + return n.setdiff1dAsync(I("x", r, e, t10), I("y", r, e, t10)); default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var VN = (r, e, t6, o = Ye) => { +var KN = (r, e, t10, o = Ye) => { switch (r.op) { case "LowerBound": { - let n = I("sortedSequence", r, e, t6), s = I("values", r, e, t6); + let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10); return [o.lowerBound(n, s)]; } case "TopKV2": { - let n = I("x", r, e, t6), s = I("k", r, e, t6), a = I("sorted", r, e, t6), i = o.topk(n, s, a); + let n = I("x", r, e, t10), s = I("k", r, e, t10), a = I("sorted", r, e, t10), i = o.topk(n, s, a); return [i.values, i.indices]; } case "UpperBound": { - let n = I("sortedSequence", r, e, t6), s = I("values", r, e, t6); + let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10); return [o.upperBound(n, s)]; } case "Unique": { - let n = I("x", r, e, t6), s = o.unique(n); + let n = I("x", r, e, t10), s = o.unique(n); return [s.values, s.indices]; } case "UniqueV2": { - let n = I("x", r, e, t6), s = I("axis", r, e, t6), a = o.unique(n, s); + let n = I("x", r, e, t10), s = I("axis", r, e, t10), a = o.unique(n, s); return [a.values, a.indices]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var zN = (r, e, t6, o = Ye) => { +var qN = (r, e, t10, o = Ye) => { switch (r.op) { case "Const": return e[r.name]; case "PlaceholderWithDefault": - let n = I("default", r, e, t6); - return [Gt(r.name, e, t6) || n]; + let n = I("default", r, e, t10); + return [Gt(r.name, e, t10) || n]; case "Placeholder": - return [Gt(r.name, e, t6)]; + return [Gt(r.name, e, t10)]; case "Identity": case "StopGradient": case "FakeQuantWithMinMaxVars": { - let c = I("x", r, e, t6); - return [as(c)]; + let c = I("x", r, e, t10); + return [is(c)]; } case "IdentityN": - return I("x", r, e, t6).map((c) => as(c)); + return I("x", r, e, t10).map((c) => is(c)); case "Snapshot": - let s = I("x", r, e, t6); - return [as(s)]; + let s = I("x", r, e, t10); + return [is(s)]; case "Shape": - return [o.tensor1d(I("x", r, e, t6).shape, "int32")]; + return [o.tensor1d(I("x", r, e, t10).shape, "int32")]; case "ShapeN": - return I("x", r, e, t6).map((c) => o.tensor1d(c.shape)); + return I("x", r, e, t10).map((c) => o.tensor1d(c.shape)); case "Size": - return [o.scalar(I("x", r, e, t6).size, "int32")]; + return [o.scalar(I("x", r, e, t10).size, "int32")]; case "Rank": - return [o.scalar(I("x", r, e, t6).rank, "int32")]; + return [o.scalar(I("x", r, e, t10).rank, "int32")]; case "NoOp": return [o.scalar(1)]; case "Print": - let a = I("x", r, e, t6), i = I("data", r, e, t6), p = I("message", r, e, t6), u = I("summarize", r, e, t6); + let a = I("x", r, e, t10), i = I("data", r, e, t10), p = I("message", r, e, t10), u = I("summarize", r, e, t10); console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(p); for (let c = 0; c < i.length; c++) console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0, u)); @@ -10511,8 +10500,8 @@ var zN = (r, e, t6, o = Ye) => { } }; var sf = class { - constructor(e, t6) { - this.keyDType = e, this.valueDType = t6, this.handle = be(0), this.tensorMap = /* @__PURE__ */ new Map(), _r(this.handle); + constructor(e, t10) { + this.keyDType = e, this.valueDType = t10, this.handle = Ce(0), this.tensorMap = /* @__PURE__ */ new Map(), _r(this.handle); } get id() { return this.handle.id; @@ -10524,13 +10513,13 @@ var sf = class { return this.tensorMap.size; } tensorSize() { - return be(this.size(), "int32"); + return Ce(this.size(), "int32"); } - async import(e, t6) { - this.checkKeyAndValueTensor(e, t6); + async import(e, t10) { + this.checkKeyAndValueTensor(e, t10); let o = await e.data(); return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), Ee(() => { - let n = so(t6), s = o.length, a = n.length; + let n = so(t10), s = o.length, a = n.length; y.assert(s === a, () => `The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`); for (let i = 0; i < s; i++) { let p = o[i], u = n[i]; @@ -10539,30 +10528,30 @@ var sf = class { return this.handle; }); } - async find(e, t6) { - this.checkKeyAndValueTensor(e, t6); + async find(e, t10) { + this.checkKeyAndValueTensor(e, t10); let o = await e.data(); return Ee(() => { let n = []; for (let s = 0; s < o.length; s++) { - let a = o[s], i = this.findWithDefault(a, t6); + let a = o[s], i = this.findWithDefault(a, t10); n.push(i); } return Sr(n); }); } - findWithDefault(e, t6) { + findWithDefault(e, t10) { let o = this.tensorMap.get(e); - return o != null ? o : t6; + return o != null ? o : t10; } - checkKeyAndValueTensor(e, t6) { + checkKeyAndValueTensor(e, t10) { if (e.dtype !== this.keyDType) throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`); - if (t6.dtype !== this.valueDType) - throw new Error(`Expect value dtype ${this.valueDType}, but got ${t6.dtype}`); + if (t10.dtype !== this.valueDType) + throw new Error(`Expect value dtype ${this.valueDType}, but got ${t10.dtype}`); } }; -var WN = async (r, e, t6, o) => { +var jN = async (r, e, t10, o) => { switch (r.op) { case "HashTable": case "HashTableV2": { @@ -10570,7 +10559,7 @@ var WN = async (r, e, t6, o) => { if (n != null) return [n]; { - let s = I("keyDType", r, e, t6), a = I("valueDType", r, e, t6), i = new sf(s, a); + let s = I("keyDType", r, e, t10), a = I("valueDType", r, e, t10), i = new sf(s, a); return o.addHashTable(r.name, i), [i.handle]; } } @@ -10578,227 +10567,227 @@ var WN = async (r, e, t6, o) => { case "InitializeTableV2": case "LookupTableImport": case "LookupTableImportV2": { - let n = I("tableHandle", r, e, t6, o), s = I("keys", r, e, t6), a = I("values", r, e, t6); + let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("values", r, e, t10); return [await o.getHashTableById(n.id).import(s, a)]; } case "LookupTableFind": case "LookupTableFindV2": { - let n = I("tableHandle", r, e, t6, o), s = I("keys", r, e, t6), a = I("defaultValue", r, e, t6); + let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("defaultValue", r, e, t10); return [await o.getHashTableById(n.id).find(s, a)]; } case "LookupTableSize": case "LookupTableSizeV2": { - let n = I("tableHandle", r, e, t6, o); + let n = I("tableHandle", r, e, t10, o); return [o.getHashTableById(n.id).tensorSize()]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var UN = (r, e, t6, o = Ye) => { +var XN = (r, e, t10, o = Ye) => { switch (r.op) { case "ResizeBilinear": { - let n = I("images", r, e, t6), s = I("size", r, e, t6), a = I("alignCorners", r, e, t6), i = I("halfPixelCenters", r, e, t6); + let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10); return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)]; } case "ResizeNearestNeighbor": { - let n = I("images", r, e, t6), s = I("size", r, e, t6), a = I("alignCorners", r, e, t6), i = I("halfPixelCenters", r, e, t6); + let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10); return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)]; } case "CropAndResize": { - let n = I("image", r, e, t6), s = I("boxes", r, e, t6), a = I("boxInd", r, e, t6), i = I("cropSize", r, e, t6), p = I("method", r, e, t6), u = I("extrapolationValue", r, e, t6); + let n = I("image", r, e, t10), s = I("boxes", r, e, t10), a = I("boxInd", r, e, t10), i = I("cropSize", r, e, t10), p = I("method", r, e, t10), u = I("extrapolationValue", r, e, t10); return [o.image.cropAndResize(n, s, a, i, p, u)]; } case "ImageProjectiveTransformV3": { - let n = I("images", r, e, t6), s = I("transforms", r, e, t6), a = I("outputShape", r, e, t6), i = I("fillValue", r, e, t6), p = I("interpolation", r, e, t6), u = I("fillMode", r, e, t6); + let n = I("images", r, e, t10), s = I("transforms", r, e, t10), a = I("outputShape", r, e, t10), i = I("fillValue", r, e, t10), p = I("interpolation", r, e, t10), u = I("fillMode", r, e, t10); return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var GN = (r, e, t6, o = Ye) => { +var YN = (r, e, t10, o = Ye) => { switch (r.op) { case "Equal": - return [o.equal(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.equal(I("a", r, e, t10), I("b", r, e, t10))]; case "NotEqual": - return [o.notEqual(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.notEqual(I("a", r, e, t10), I("b", r, e, t10))]; case "Greater": - return [o.greater(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.greater(I("a", r, e, t10), I("b", r, e, t10))]; case "GreaterEqual": - return [o.greaterEqual(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.greaterEqual(I("a", r, e, t10), I("b", r, e, t10))]; case "Less": - return [o.less(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.less(I("a", r, e, t10), I("b", r, e, t10))]; case "LessEqual": - return [o.lessEqual(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.lessEqual(I("a", r, e, t10), I("b", r, e, t10))]; case "LogicalAnd": - return [o.logicalAnd(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.logicalAnd(I("a", r, e, t10), I("b", r, e, t10))]; case "LogicalNot": - return [o.logicalNot(I("a", r, e, t6))]; + return [o.logicalNot(I("a", r, e, t10))]; case "LogicalOr": - return [o.logicalOr(I("a", r, e, t6), I("b", r, e, t6))]; + return [o.logicalOr(I("a", r, e, t10), I("b", r, e, t10))]; case "Select": case "SelectV2": - return [o.where(I("condition", r, e, t6), I("a", r, e, t6), I("b", r, e, t6))]; + return [o.where(I("condition", r, e, t10), I("a", r, e, t10), I("b", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var HN = (r, e, t6, o = Ye) => { +var QN = (r, e, t10, o = Ye) => { switch (r.op) { case "BatchMatMul": case "BatchMatMulV2": case "MatMul": - return [o.matMul(I("a", r, e, t6), I("b", r, e, t6), I("transposeA", r, e, t6), I("transposeB", r, e, t6))]; + return [o.matMul(I("a", r, e, t10), I("b", r, e, t10), I("transposeA", r, e, t10), I("transposeB", r, e, t10))]; case "Einsum": - return [o.einsum(I("equation", r, e, t6), ...I("tensors", r, e, t6))]; + return [o.einsum(I("equation", r, e, t10), ...I("tensors", r, e, t10))]; case "Transpose": - return [o.transpose(I("x", r, e, t6), I("perm", r, e, t6))]; + return [o.transpose(I("x", r, e, t10), I("perm", r, e, t10))]; case "_FusedMatMul": - let [n, s] = I("fusedOps", r, e, t6), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t6), u = I("leakyreluAlpha", r, e, t6); + let [n, s] = I("fusedOps", r, e, t10), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t10), u = I("leakyreluAlpha", r, e, t10); if (a) { if (i && p !== 2) throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha."); if (!i && p !== 1) throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias."); } - let [c, l] = I("args", r, e, t6); - return [o.fused.matMul({ a: I("a", r, e, t6), b: I("b", r, e, t6), transposeA: I("transposeA", r, e, t6), transposeB: I("transposeB", r, e, t6), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; + let [c, l] = I("args", r, e, t10); + return [o.fused.matMul({ a: I("a", r, e, t10), b: I("b", r, e, t10), transposeA: I("transposeA", r, e, t10), transposeB: I("transposeB", r, e, t10), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var qN = (r, e, t6, o = Ye) => { +var ZN = (r, e, t10, o = Ye) => { switch (r.op) { case "EuclideanNorm": - return [o.euclideanNorm(I("x", r, e, t6), I("axis", r, e, t6), I("keepDims", r, e, t6))]; + return [o.euclideanNorm(I("x", r, e, t10), I("axis", r, e, t10), I("keepDims", r, e, t10))]; case "FusedBatchNorm": case "FusedBatchNormV2": - return [o.batchNorm(I("x", r, e, t6), I("mean", r, e, t6), I("variance", r, e, t6), I("offset", r, e, t6), I("scale", r, e, t6), I("epsilon", r, e, t6))]; + return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))]; case "FusedBatchNormV3": - return [o.batchNorm(I("x", r, e, t6), I("mean", r, e, t6), I("variance", r, e, t6), I("offset", r, e, t6), I("scale", r, e, t6), I("epsilon", r, e, t6))]; + return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))]; case "LRN": - return [o.localResponseNormalization(I("x", r, e, t6), I("radius", r, e, t6), I("bias", r, e, t6), I("alpha", r, e, t6), I("beta", r, e, t6))]; + return [o.localResponseNormalization(I("x", r, e, t10), I("radius", r, e, t10), I("bias", r, e, t10), I("alpha", r, e, t10), I("beta", r, e, t10))]; case "Softmax": - return [o.softmax(I("x", r, e, t6))]; + return [o.softmax(I("x", r, e, t10))]; case "LogSoftmax": - return [o.logSoftmax(I("x", r, e, t6))]; + return [o.logSoftmax(I("x", r, e, t10))]; case "SparseToDense": - return [o.sparseToDense(I("sparseIndices", r, e, t6), I("outputShape", r, e, t6), I("sparseValues", r, e, t6), I("defaultValue", r, e, t6))]; + return [o.sparseToDense(I("sparseIndices", r, e, t10), I("outputShape", r, e, t10), I("sparseValues", r, e, t10), I("defaultValue", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var KN = (r, e, t6, o = Ye) => { +var JN = (r, e, t10, o = Ye) => { switch (r.op) { case "RaggedGather": { - let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t6), I("paramsDenseValues", r, e, t6), I("indices", r, e, t6), I("outputRaggedRank", r, e, t6)); + let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t10), I("paramsDenseValues", r, e, t10), I("indices", r, e, t10), I("outputRaggedRank", r, e, t10)); return n.concat(s); } case "RaggedRange": { - let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t6), I("limits", r, e, t6), I("splits", r, e, t6)); + let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t10), I("limits", r, e, t10), I("splits", r, e, t10)); return [n, s]; } case "RaggedTensorToTensor": - return [o.raggedTensorToTensor(I("shape", r, e, t6), I("values", r, e, t6), I("defaultValue", r, e, t6), I("rowPartitionTensors", r, e, t6), I("rowPartitionTypes", r, e, t6))]; + return [o.raggedTensorToTensor(I("shape", r, e, t10), I("values", r, e, t10), I("defaultValue", r, e, t10), I("rowPartitionTensors", r, e, t10), I("rowPartitionTypes", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var jN = (r, e, t6, o = Ye) => { +var eT = (r, e, t10, o = Ye) => { switch (r.op) { case "Max": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.max(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.max(I("x", r, e, t10), i, p)]; } case "Mean": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.mean(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.mean(I("x", r, e, t10), i, p)]; } case "Min": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.min(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.min(I("x", r, e, t10), i, p)]; } case "Sum": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.sum(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.sum(I("x", r, e, t10), i, p)]; } case "All": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.all(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.all(I("x", r, e, t10), i, p)]; } case "Any": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.any(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.any(I("x", r, e, t10), i, p)]; } case "ArgMax": { - let i = I("axis", r, e, t6); - return [o.argMax(I("x", r, e, t6), i)]; + let i = I("axis", r, e, t10); + return [o.argMax(I("x", r, e, t10), i)]; } case "ArgMin": { - let i = I("axis", r, e, t6); - return [o.argMin(I("x", r, e, t6), i)]; + let i = I("axis", r, e, t10); + return [o.argMin(I("x", r, e, t10), i)]; } case "Prod": { - let i = I("axis", r, e, t6), p = I("keepDims", r, e, t6); - return [o.prod(I("x", r, e, t6), i, p)]; + let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10); + return [o.prod(I("x", r, e, t10), i, p)]; } case "Cumprod": { - let i = I("axis", r, e, t6), p = I("exclusive", r, e, t6), u = I("reverse", r, e, t6); - return [o.cumprod(I("x", r, e, t6), i, p, u)]; + let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10); + return [o.cumprod(I("x", r, e, t10), i, p, u)]; } case "Cumsum": { - let i = I("axis", r, e, t6), p = I("exclusive", r, e, t6), u = I("reverse", r, e, t6); - return [o.cumsum(I("x", r, e, t6), i, p, u)]; + let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10); + return [o.cumsum(I("x", r, e, t10), i, p, u)]; } case "Bincount": - let n = I("x", r, e, t6), s = I("weights", r, e, t6), a = I("size", r, e, t6); + let n = I("x", r, e, t10), s = I("weights", r, e, t10), a = I("size", r, e, t10); return [o.bincount(n, s, a)]; case "DenseBincount": { - let i = I("x", r, e, t6), p = I("weights", r, e, t6), u = I("size", r, e, t6), c = I("binaryOutput", r, e, t6); + let i = I("x", r, e, t10), p = I("weights", r, e, t10), u = I("size", r, e, t10), c = I("binaryOutput", r, e, t10); return [o.denseBincount(i, p, u, c)]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var XN = (r, e, t6, o = Ye) => { +var tT = (r, e, t10, o = Ye) => { switch (r.op) { case "ConcatV2": case "Concat": { - let n = I("n", r, e, t6), s = I("axis", r, e, t6), a = I("tensors", r, e, t6); + let n = I("n", r, e, t10), s = I("axis", r, e, t10), a = I("tensors", r, e, t10); return a = a.slice(0, n), [o.concat(a, s)]; } case "Gather": { - let n = I("x", r, e, t6), s = I("indices", r, e, t6); + let n = I("x", r, e, t10), s = I("indices", r, e, t10); return [o.gather(n, o.cast(s, "int32"), 0)]; } case "GatherV2": { - let n = I("axis", r, e, t6), s = I("batchDims", r, e, t6), a = I("x", r, e, t6), i = I("indices", r, e, t6); + let n = I("axis", r, e, t10), s = I("batchDims", r, e, t10), a = I("x", r, e, t10), i = I("indices", r, e, t10); return [o.gather(a, o.cast(i, "int32"), n, s)]; } case "Reverse": { - let n = I("dims", r, e, t6), s = []; + let n = I("dims", r, e, t10), s = []; for (let i = 0; i < n.length; i++) n[i] && s.push(i); - let a = I("x", r, e, t6); + let a = I("x", r, e, t10); return [o.reverse(a, s)]; } case "ReverseV2": { - let n = I("axis", r, e, t6), s = I("x", r, e, t6); + let n = I("axis", r, e, t10), s = I("x", r, e, t10); return [o.reverse(s, n)]; } case "Slice": { - let n = I("begin", r, e, t6), s = I("size", r, e, t6); - return [o.slice(I("x", r, e, t6), n, s)]; + let n = I("begin", r, e, t10), s = I("size", r, e, t10); + return [o.slice(I("x", r, e, t10), n, s)]; } case "StridedSlice": { - let n = I("begin", r, e, t6), s = I("end", r, e, t6), a = I("strides", r, e, t6), i = I("beginMask", r, e, t6), p = I("endMask", r, e, t6), u = I("ellipsisMask", r, e, t6), c = I("newAxisMask", r, e, t6), l = I("shrinkAxisMask", r, e, t6), m = I("x", r, e, t6); + let n = I("begin", r, e, t10), s = I("end", r, e, t10), a = I("strides", r, e, t10), i = I("beginMask", r, e, t10), p = I("endMask", r, e, t10), u = I("ellipsisMask", r, e, t10), c = I("newAxisMask", r, e, t10), l = I("shrinkAxisMask", r, e, t10), m = I("x", r, e, t10); return [o.stridedSlice(m, n, s, a, i, p, u, c, l)]; } case "Pack": return Ee(() => { - let n = I("axis", r, e, t6), s = I("tensors", r, e, t6), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { + let n = I("axis", r, e, t10), s = I("tensors", r, e, t10), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { let c = y.arraysEqual(u.shape, a); if (!c && !y.arraysEqual(o.squeeze(u).shape, i)) throw new Error("the input tensors shape does not match"); @@ -10807,164 +10796,164 @@ var XN = (r, e, t6, o = Ye) => { return [o.stack(p, n)]; }); case "Unpack": { - let n = I("axis", r, e, t6), s = I("tensor", r, e, t6); + let n = I("axis", r, e, t10), s = I("tensor", r, e, t10); return o.unstack(s, n); } case "Tile": { - let n = I("reps", r, e, t6); - return [o.tile(I("x", r, e, t6), n)]; + let n = I("reps", r, e, t10); + return [o.tile(I("x", r, e, t10), n)]; } case "Split": case "SplitV": { - let n = I("axis", r, e, t6), s = I("numOrSizeSplits", r, e, t6), a = I("x", r, e, t6); + let n = I("axis", r, e, t10), s = I("numOrSizeSplits", r, e, t10), a = I("x", r, e, t10); return o.split(a, s, n); } case "ScatterNd": { - let n = I("indices", r, e, t6), s = I("values", r, e, t6), a = I("shape", r, e, t6); + let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("shape", r, e, t10); return [o.scatterND(n, s, a)]; } case "GatherNd": { - let n = I("x", r, e, t6), s = I("indices", r, e, t6); + let n = I("x", r, e, t10), s = I("indices", r, e, t10); return [o.gatherND(n, s)]; } case "SparseToDense": { - let n = I("sparseIndices", r, e, t6), s = I("outputShape", r, e, t6), a = I("sparseValues", r, e, t6), i = I("defaultValue", r, e, t6); + let n = I("sparseIndices", r, e, t10), s = I("outputShape", r, e, t10), a = I("sparseValues", r, e, t10), i = I("defaultValue", r, e, t10); return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))]; } default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var YN = (r, e, t6, o = Ye) => { +var rT = (r, e, t10, o = Ye) => { switch (r.op) { case "SparseFillEmptyRows": { - let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t6), I("values", r, e, t6), I("denseShape", r, e, t6), I("defaultValue", r, e, t6)); + let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t10), I("values", r, e, t10), I("denseShape", r, e, t10), I("defaultValue", r, e, t10)); return [n, s, a, i]; } case "SparseReshape": { - let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t6), I("inputShape", r, e, t6), I("newShape", r, e, t6)); + let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t10), I("inputShape", r, e, t10), I("newShape", r, e, t10)); return [n, s]; } case "SparseSegmentMean": - return [o.sparse.sparseSegmentMean(I("data", r, e, t6), I("indices", r, e, t6), I("segmentIds", r, e, t6))]; + return [o.sparse.sparseSegmentMean(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))]; case "SparseSegmentSum": - return [o.sparse.sparseSegmentSum(I("data", r, e, t6), I("indices", r, e, t6), I("segmentIds", r, e, t6))]; + return [o.sparse.sparseSegmentSum(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var QN = (r, e, t6, o = Ye) => { +var oT = (r, e, t10, o = Ye) => { switch (r.op) { case "FFT": - return [o.fft(I("x", r, e, t6))]; + return [o.fft(I("x", r, e, t10))]; case "IFFT": - return [o.ifft(I("x", r, e, t6))]; + return [o.ifft(I("x", r, e, t10))]; case "RFFT": - return [o.rfft(I("x", r, e, t6))]; + return [o.rfft(I("x", r, e, t10))]; case "IRFFT": - return [o.irfft(I("x", r, e, t6))]; + return [o.irfft(I("x", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var ZN = (r, e, t6, o = Ye) => { +var nT = (r, e, t10, o = Ye) => { switch (r.op) { case "StringNGrams": { - let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t6), I("dataSplits", r, e, t6), I("separator", r, e, t6), I("nGramWidths", r, e, t6), I("leftPad", r, e, t6), I("rightPad", r, e, t6), I("padWidth", r, e, t6), I("preserveShortSequences", r, e, t6)); + let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t10), I("dataSplits", r, e, t10), I("separator", r, e, t10), I("nGramWidths", r, e, t10), I("leftPad", r, e, t10), I("rightPad", r, e, t10), I("padWidth", r, e, t10), I("preserveShortSequences", r, e, t10)); return [n, s]; } case "StringSplit": { - let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t6), I("delimiter", r, e, t6), I("skipEmpty", r, e, t6)); + let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t10), I("delimiter", r, e, t10), I("skipEmpty", r, e, t10)); return [n, s, a]; } case "StringToHashBucketFast": - return [o.string.stringToHashBucketFast(I("input", r, e, t6), I("numBuckets", r, e, t6))]; + return [o.string.stringToHashBucketFast(I("input", r, e, t10), I("numBuckets", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -var JN = (r, e, t6, o = Ye) => { +var sT = (r, e, t10, o = Ye) => { switch (r.op) { case "Cast": - return [o.cast(I("x", r, e, t6), I("dtype", r, e, t6))]; + return [o.cast(I("x", r, e, t10), I("dtype", r, e, t10))]; case "ExpandDims": { - let n = I("axis", r, e, t6); - return [o.expandDims(I("x", r, e, t6), n)]; + let n = I("axis", r, e, t10); + return [o.expandDims(I("x", r, e, t10), n)]; } case "Squeeze": { - let n = I("axis", r, e, t6); - return [o.squeeze(I("x", r, e, t6), n)]; + let n = I("axis", r, e, t10); + return [o.squeeze(I("x", r, e, t10), n)]; } case "Reshape": - return [o.reshape(I("x", r, e, t6), I("shape", r, e, t6))]; + return [o.reshape(I("x", r, e, t10), I("shape", r, e, t10))]; case "MirrorPad": - return [o.mirrorPad(I("x", r, e, t6), I("padding", r, e, t6), I("mode", r, e, t6))]; + return [o.mirrorPad(I("x", r, e, t10), I("padding", r, e, t10), I("mode", r, e, t10))]; case "PadV2": case "Pad": - return [o.pad(I("x", r, e, t6), I("padding", r, e, t6), I("constantValue", r, e, t6))]; + return [o.pad(I("x", r, e, t10), I("padding", r, e, t10), I("constantValue", r, e, t10))]; case "SpaceToBatchND": { - let n = I("blockShape", r, e, t6), s = I("paddings", r, e, t6); - return [o.spaceToBatchND(I("x", r, e, t6), n, s)]; + let n = I("blockShape", r, e, t10), s = I("paddings", r, e, t10); + return [o.spaceToBatchND(I("x", r, e, t10), n, s)]; } case "BatchToSpaceND": { - let n = I("blockShape", r, e, t6), s = I("crops", r, e, t6); - return [o.batchToSpaceND(I("x", r, e, t6), n, s)]; + let n = I("blockShape", r, e, t10), s = I("crops", r, e, t10); + return [o.batchToSpaceND(I("x", r, e, t10), n, s)]; } case "DepthToSpace": { - let n = I("blockSize", r, e, t6), s = I("dataFormat", r, e, t6).toUpperCase(); - return [o.depthToSpace(I("x", r, e, t6), n, s)]; + let n = I("blockSize", r, e, t10), s = I("dataFormat", r, e, t10).toUpperCase(); + return [o.depthToSpace(I("x", r, e, t10), n, s)]; } case "BroadcastTo": - return [o.broadcastTo(I("x", r, e, t6), I("shape", r, e, t6))]; + return [o.broadcastTo(I("x", r, e, t10), I("shape", r, e, t10))]; case "BroadcastArgs": - return [o.broadcastArgs(I("s0", r, e, t6), I("s1", r, e, t6))]; + return [o.broadcastArgs(I("s0", r, e, t10), I("s1", r, e, t10))]; default: throw TypeError(`Node type ${r.op} is not implemented`); } }; -function qC(r, e, t6, o, n = Ee) { +function oS(r, e, t10, o, n = Ee) { let s = ((a, i, p) => { switch (a.category) { case "arithmetic": - return n(() => _N(a, i, p)); + return n(() => FN(a, i, p)); case "basic_math": - return n(() => EN(a, i, p)); + return n(() => ON(a, i, p)); case "control": - return ON(a, i, p); + return zN(a, i, p); case "convolution": - return n(() => MN(a, i, p)); - case "creation": - return n(() => LN(a, i, p)); - case "dynamic": - return BN(a, i, p); - case "evaluation": - return n(() => VN(a, i, p)); - case "image": return n(() => UN(a, i, p)); - case "graph": - return n(() => zN(a, i, p)); - case "logical": + case "creation": return n(() => GN(a, i, p)); - case "matrices": - return n(() => HN(a, i, p)); - case "normalization": - return n(() => qN(a, i, p)); - case "ragged": + case "dynamic": + return HN(a, i, p); + case "evaluation": return n(() => KN(a, i, p)); - case "reduction": - return n(() => jN(a, i, p)); - case "slice_join": + case "image": return n(() => XN(a, i, p)); - case "sparse": + case "graph": + return n(() => qN(a, i, p)); + case "logical": return n(() => YN(a, i, p)); - case "spectral": + case "matrices": return n(() => QN(a, i, p)); - case "string": + case "normalization": return n(() => ZN(a, i, p)); - case "transformation": + case "ragged": return n(() => JN(a, i, p)); + case "reduction": + return n(() => eT(a, i, p)); + case "slice_join": + return n(() => tT(a, i, p)); + case "sparse": + return n(() => rT(a, i, p)); + case "spectral": + return n(() => oT(a, i, p)); + case "string": + return n(() => nT(a, i, p)); + case "transformation": + return n(() => sT(a, i, p)); case "hash_table": - return WN(a, i, p, o); + return jN(a, i, p, o); case "custom": let u = Gd(a.op); if (u && u.customExecutor) @@ -10973,15 +10962,15 @@ function qC(r, e, t6, o, n = Ee) { default: throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`); } - })(r, e, t6); + })(r, e, t10); return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s); } -var cl = class { - constructor(e = {}, t6 = {}, o = {}, n = {}) { - this.weightMap = e, this.tensorArrayMap = t6, this.tensorListMap = o, this.functionMap = n, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds(); +var dl = class { + constructor(e = {}, t10 = {}, o = {}, n = {}) { + this.weightMap = e, this.tensorArrayMap = t10, this.tensorListMap = o, this.functionMap = n, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds(); } - newFrame(e, t6) { - return { id: e, frameName: t6, iterationId: 0 }; + newFrame(e, t10) { + return { id: e, frameName: t10, iterationId: 0 }; } set currentContext(e) { this.contexts !== e && (this.contexts = e, this.generateCurrentContextIds()); @@ -10997,14 +10986,14 @@ var cl = class { } generateCurrentContextIds() { let e = []; - for (let t6 = 0; t6 < this.contexts.length - 1; t6++) { - let o = this.contexts.slice(0, this.contexts.length - t6); + for (let t10 = 0; t10 < this.contexts.length - 1; t10++) { + let o = this.contexts.slice(0, this.contexts.length - t10); e.push(this.contextIdforContexts(o)); } e.push(""), this._currentContextIds = e; } contextIdforContexts(e) { - return e ? e.map((t6) => t6.id === 0 && t6.iterationId === 0 ? "" : `${t6.frameName}-${t6.iterationId}`).join("/") : ""; + return e ? e.map((t10) => t10.id === 0 && t10.iterationId === 0 ? "" : `${t10.frameName}-${t10.iterationId}`).join("/") : ""; } enterFrame(e) { this.contexts && (this.lastId++, this.contexts = this.contexts.slice(), this.contexts.push(this.newFrame(this.lastId, e)), this._currentContextIds.unshift(this.contextIdforContexts(this.contexts))); @@ -11039,19 +11028,19 @@ var cl = class { return this.tensorListMap[e]; } dispose(e) { - for (let t6 in this.tensorArrayMap) - this.tensorArrayMap[t6].clearAndClose(e); - for (let t6 in this.tensorListMap) - this.tensorListMap[t6].clearAndClose(e); + for (let t10 in this.tensorArrayMap) + this.tensorArrayMap[t10].clearAndClose(e); + for (let t10 in this.tensorListMap) + this.tensorListMap[t10].clearAndClose(e); } }; -function KC(r, e, t6, o) { +function nS(r, e, t10, o) { let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = Object.keys(r).map((m) => Ir(m)[0]), c = []; o != null && (c = o.map((m) => Ir(m.name)[0])); let l = [...e]; for (; l.length > 0; ) { let m = l.pop(); - if ((jC(m) || i6(m) || u6(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t6[m.name] == null && u.indexOf(m.name) === -1 && c.indexOf(m.name) === -1) { + if ((sS(m) || T6(m) || _6(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t10[m.name] == null && u.indexOf(m.name) === -1 && c.indexOf(m.name) === -1) { if (m.inputs.length === 0) { s.push(m.name); continue; @@ -11063,8 +11052,8 @@ function KC(r, e, t6, o) { } return { inputs: r, outputs: e, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i }; } -function eT(r, e, t6) { - let { usedNodes: o, inputs: n } = t6, s = [], a = Object.keys(n).map((c) => Ir(c)[0]).map((c) => r.nodes[c]), i = r.initNodes; +function aT(r, e, t10) { + let { usedNodes: o, inputs: n } = t10, s = [], a = Object.keys(n).map((c) => Ir(c)[0]).map((c) => r.nodes[c]), i = r.initNodes; a.forEach((c) => { o.has(c.name) && s.push(c); }), r.weights.forEach((c) => { @@ -11081,22 +11070,22 @@ function eT(r, e, t6) { } return u; } -var n6 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]; -var s6 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]; -var a6 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]; -function jC(r) { - return n6.indexOf(r.op) >= 0; +var v6 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]; +var k6 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]; +var N6 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]; +function sS(r) { + return v6.indexOf(r.op) >= 0; } -function i6(r) { - return s6.indexOf(r.op) >= 0; +function T6(r) { + return k6.indexOf(r.op) >= 0; } -function u6(r) { - return a6.indexOf(r.op) >= 0; +function _6(r) { + return N6.indexOf(r.op) >= 0; } -var Cu = class { - constructor(e, t6) { - this.graph = e, this.parent = t6, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((o) => { - this._functionExecutorMap[o] = new Cu(e.functions[o], this); +var ku = class { + constructor(e, t10) { + this.graph = e, this.parent = t10, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((o) => { + this._functionExecutorMap[o] = new ku(e.functions[o], this); }); } get weightIds() { @@ -11109,8 +11098,8 @@ var Cu = class { return this.parent ? this.parent.weightMap : this._weightMap; } set weightMap(e) { - let t6 = Object.keys(e).map((o) => e[o].map((n) => n.id)); - this._weightIds = [].concat(...t6), this._weightMap = e; + let t10 = Object.keys(e).map((o) => e[o].map((n) => n.id)); + this._weightIds = [].concat(...t10), this._weightMap = e; } set resourceManager(e) { this._resourceManager = e; @@ -11126,44 +11115,44 @@ var Cu = class { } get outputNodes() { return this._outputs.map((e) => { - let t6 = e.signatureKey || e.name; - return e.defaultOutput ? `${t6}:${e.defaultOutput}` : t6; + let t10 = e.signatureKey || e.name; + return e.defaultOutput ? `${t10}:${e.defaultOutput}` : t10; }); } get functions() { - return Object.keys(this._functions).reduce((e, t6) => (e[t6] = this._functions[t6].signature, e), {}); + return Object.keys(this._functions).reduce((e, t10) => (e[t10] = this._functions[t10].signature, e), {}); } - getCompilationKey(e, t6) { - let o = e.map((s) => s.name).sort(), n = t6.map((s) => s.name).sort(); + getCompilationKey(e, t10) { + let o = e.map((s) => s.name).sort(), n = t10.map((s) => s.name).sort(); return o.join(this.SEPERATOR) + "--" + n.join(this.SEPERATOR); } - compile(e, t6) { - let o = KC(e, t6, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o; + compile(e, t10) { + let o = nS(e, t10, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o; if (s != null) throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`); if (n.length > 0) { - let i = t6.map((u) => u.name), p = Object.keys(e); + let i = t10.map((u) => u.name), p = Object.keys(e); throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`); } - return eT(this.graph, this.weightMap, o); + return aT(this.graph, this.weightMap, o); } cloneAndKeepTensor(e) { if (e == null) return null; - let t6 = e.clone(); - return _r(t6), t6; + let t10 = e.clone(); + return _r(t10), t10; } cloneTensorList(e) { return e ? e.map((o) => this.cloneAndKeepTensor(o)) : null; } cloneTensorMap(e) { - return Object.fromEntries(Object.entries(e).map(([t6, o]) => [t6, this.cloneTensorList(o)])); + return Object.fromEntries(Object.entries(e).map(([t10, o]) => [t10, this.cloneTensorList(o)])); } - execute(e, t6) { + execute(e, t10) { this.disposeIntermediateTensors(), e = this.mapInputs(e); let o = Object.keys(e).sort(); - this.checkInputs(e), this.checkInputShapeAndType(e), t6 = this.mapOutputs(t6), this.checkOutputs(t6); - let n = o.map((l) => this.graph.nodes[Ir(l)[0]]), s = t6.map((l) => Ir(l)[0]), a = s.map((l) => this.graph.nodes[l]); + this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10); + let n = o.map((l) => this.graph.nodes[Ir(l)[0]]), s = t10.map((l) => Ir(l)[0]), a = s.map((l) => this.graph.nodes[l]); a.length === 0 && (a = this._outputs); let i = this.getCompilationKey(n, a), p = this.compiledMap.get(i); p == null && (p = this.compile(e, a), this.compiledMap.set(i, p)); @@ -11174,7 +11163,7 @@ var Cu = class { } let u = {}, c = {}; return Ee(() => { - let l = new cl(this.weightMap, u, c, this.functionExecutorMap), m = Object.assign({}, this.weightMap); + let l = new dl(this.weightMap, u, c, this.functionExecutorMap), m = Object.assign({}, this.weightMap); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(e).forEach((h) => { let [g, x] = Ir(h), b = []; b[x] = e[h], m[g] = b, this.keepIntermediateTensors && (this.clonedTensorsMap[g] = this.cloneTensorList(b)); @@ -11183,25 +11172,25 @@ var Cu = class { for (let h = 0; h < p.length; h++) { let g = p[h]; if (!m[g.name]) { - let x = qC(g, m, l, this._resourceManager); + let x = oS(g, m, l, this._resourceManager); if (y.isPromise(x)) throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`); m[g.name] = x, this.keepIntermediateTensors && (this.clonedTensorsMap[g.name] = this.cloneTensorList(x)), this.checkTensorForDisposal(g.name, g, m, l, d, s, f); } } - return this.parent == null && l.dispose(d), t6.map((h) => Gt(h, m, l)); + return this.parent == null && l.dispose(d), t10.map((h) => Gt(h, m, l)); }); } getFrozenTensorIds(e) { - let t6 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id))); - return new Set(t6); + let t10 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id))); + return new Set(t10); } - checkTensorForDisposal(e, t6, o, n, s, a, i) { - t6.category === "control" || a.indexOf(e) !== -1 || (o[e].forEach((p) => { - p != null && (i[p.id] = (i[p.id] || 0) + t6.children.length); - }), t6.inputs.forEach((p) => { + checkTensorForDisposal(e, t10, o, n, s, a, i) { + t10.category === "control" || a.indexOf(e) !== -1 || (o[e].forEach((p) => { + p != null && (i[p.id] = (i[p.id] || 0) + t10.children.length); + }), t10.inputs.forEach((p) => { if (p.category !== "control") { - let u = vN(p.name, o, n); + let u = EN(p.name, o, n); u != null && u.forEach((c) => { if (c && !c.kept && !s.has(c.id)) { let l = i[c.id]; @@ -11211,87 +11200,87 @@ var Cu = class { } })); } - async executeAsync(e, t6) { - return this._executeAsync(e, t6); + async executeAsync(e, t10) { + return this._executeAsync(e, t10); } disposeIntermediateTensors() { !this.clonedTensorsMap || (Object.values(this.clonedTensorsMap).forEach((e) => { - for (let t6 of e) - t6 && !t6.isDisposed && t6.dispose(); + for (let t10 of e) + t10 && !t10.isDisposed && t10.dispose(); }), this.clonedTensorsMap = null); } getIntermediateTensors() { return this.clonedTensorsMap; } - async _executeAsync(e, t6, o = false, n = {}, s = {}) { - this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t6 = this.mapOutputs(t6), this.checkOutputs(t6)); + async _executeAsync(e, t10, o = false, n = {}, s = {}) { + this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10)); try { this.keepIntermediateTensors = O().getBool("KEEP_INTERMEDIATE_TENSORS"); } catch (m) { this.keepIntermediateTensors = false, console.warn(m.message); } - let a = new cl(this.weightMap, n, s, this.functionExecutorMap); + let a = new dl(this.weightMap, n, s, this.functionExecutorMap); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)); - let i = await this.executeWithControlFlow(e, a, t6, o), p = t6.map((m) => Gt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); + let i = await this.executeWithControlFlow(e, a, t10, o), p = t10.map((m) => Gt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); return Object.values(i).forEach((m) => { m.forEach((d) => { d && !d.isDisposed && !l.has(d.id) && d.dispose(); }); }), this.parent == null && a.dispose(l), p; } - async executeFunctionAsync(e, t6, o) { + async executeFunctionAsync(e, t10, o) { let n = e.reduce((s, a, i) => (s[this.inputs[i].name] = a, s), {}); - return this._executeAsync(n, this.outputNodes, true, t6, o); + return this._executeAsync(n, this.outputNodes, true, t10, o); } - async executeWithControlFlow(e, t6, o, n) { + async executeWithControlFlow(e, t10, o, n) { let s = Object.keys(e), a = s.map((C) => this.graph.nodes[Ir(C)[0]]), i = o.map((C) => Ir(C)[0]), p = i.map((C) => this.graph.nodes[C]); p.length === 0 && (p = this._outputs); - let { usedNodes: u, missingInputs: c, dynamicNode: l, syncInputs: m } = KC(e, p, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((C) => ({ node: C, contexts: t6.currentContext })), f = Object.assign({}, this.weightMap); + let { usedNodes: u, missingInputs: c, dynamicNode: l, syncInputs: m } = nS(e, p, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((C) => ({ node: C, contexts: t10.currentContext })), f = Object.assign({}, this.weightMap); Object.keys(e).forEach((C) => { let [w, k] = Ir(C), _ = []; _[k] = e[C], f[w] = _; }); let h = {}, g = this.getFrozenTensorIds(f), x = {}; for (; d.length > 0; ) { - let C = this.processStack(a, d, t6, f, x, g, i, h, u); + let C = this.processStack(a, d, t10, f, x, g, i, h, u); await Promise.all(C); } l == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead."); - let b = p.filter((C) => !jC(C) && !Gt(C.name, f, t6)).map((C) => C.name); + let b = p.filter((C) => !sS(C) && !Gt(C.name, f, t10)).map((C) => C.name); if (b.length > 0) { let C = ""; throw l != null && (C = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`), new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`); } return f; } - processStack(e, t6, o, n, s, a, i, p, u) { + processStack(e, t10, o, n, s, a, i, p, u) { let c = []; - for (; t6.length > 0; ) { - let l = t6.pop(); + for (; t10.length > 0; ) { + let l = t10.pop(); o.currentContext = l.contexts; let m = ""; - if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = ss(l.node.name, o)), n[l.node.name] == null) { - let d = qC(l.node, n, o, this._resourceManager); - m || ([m] = ss(l.node.name, o)); + if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = as(l.node.name, o)), n[l.node.name] == null) { + let d = oS(l.node, n, o, this._resourceManager); + m || ([m] = as(l.node.name, o)); let f = o.currentContext; - y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t6, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t6, o, n, s, u)); + y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u)); } else - this.processChildNodes(l.node, t6, o, n, s, u); + this.processChildNodes(l.node, t10, o, n, s, u); } return c; } - processChildNodes(e, t6, o, n, s, a) { + processChildNodes(e, t10, o, n, s, a) { e.children.forEach((i) => { - let [p] = ss(i.name, o); - s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Gt(u, n, o)) && (s[p] = true, t6.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Gt(u, n, o)) && (s[p] = true, t6.push({ contexts: o.currentContext, node: i }))); + let [p] = as(i.name, o); + s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Gt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Gt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i }))); }); } dispose() { - Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t6) => t6.dispose())); + Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t10) => t10.dispose())); } checkInputShapeAndType(e) { - Object.keys(e).forEach((t6) => { - let o = e[t6], [n] = Ir(t6), s = this.graph.nodes[n]; + Object.keys(e).forEach((t10) => { + let o = e[t10], [n] = Ir(t10), s = this.graph.nodes[n]; if (s.attrParams.shape && s.attrParams.shape.value) { let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p); y.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`); @@ -11300,43 +11289,43 @@ var Cu = class { }); } mapInputs(e) { - var t6, o; + var t10, o; let n = {}; for (let s in e) { - let a = (o = (t6 = this._signature) === null || t6 === void 0 ? void 0 : t6.inputs) === null || o === void 0 ? void 0 : o[s]; + let a = (o = (t10 = this._signature) === null || t10 === void 0 ? void 0 : t10.inputs) === null || o === void 0 ? void 0 : o[s]; a != null ? n[a.name] = e[s] : n[s] = e[s]; } return n; } checkInputs(e) { - let t6 = Object.keys(e).filter((o) => { + let t10 = Object.keys(e).filter((o) => { let [n] = Ir(o); return this.graph.nodes[n] == null; }); - if (t6.length > 0) - throw new Error(`The dict provided in model.execute(dict) has keys: [${t6}] that are not part of graph`); + if (t10.length > 0) + throw new Error(`The dict provided in model.execute(dict) has keys: [${t10}] that are not part of graph`); } mapOutputs(e) { - return e.map((t6) => { + return e.map((t10) => { var o, n; - let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t6]; - return s != null ? s.name : t6; + let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t10]; + return s != null ? s.name : t10; }, {}); } checkOutputs(e) { - e.forEach((t6) => { - let [o] = Ir(t6); + e.forEach((t10) => { + let [o] = Ir(t10); if (!this.graph.nodes[o]) - throw new Error(`The output '${t6}' is not found in the graph`); + throw new Error(`The output '${t10}' is not found in the graph`); }); } }; var af = class { - constructor(e = {}, t6 = {}) { - this.hashTableNameToHandle = e, this.hashTableMap = t6; + constructor(e = {}, t10 = {}) { + this.hashTableNameToHandle = e, this.hashTableMap = t10; } - addHashTable(e, t6) { - this.hashTableNameToHandle[e] = t6.handle, this.hashTableMap[t6.id] = t6; + addHashTable(e, t10) { + this.hashTableNameToHandle[e] = t10.handle, this.hashTableMap[t10.id] = t10; } getHashTableHandleByName(e) { return this.hashTableNameToHandle[e]; @@ -11351,11 +11340,11 @@ var af = class { this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e]; } }; -var p6 = "?tfjs-format=file"; -var c6 = "model.json"; -var ll = class { - constructor(e, t6 = {}, o = Ea) { - this.modelUrl = e, this.loadOptions = t6, this.version = "n/a", this.io = o, t6 == null && (this.loadOptions = {}), this.resourceManager = new af(); +var $6 = "?tfjs-format=file"; +var E6 = "model.json"; +var fl = class { + constructor(e, t10 = {}, o = La) { + this.modelUrl = e, this.loadOptions = t10, this.version = "n/a", this.io = o, t10 == null && (this.loadOptions = {}), this.resourceManager = new af(); } get modelVersion() { return this.version; @@ -11391,36 +11380,36 @@ var ll = class { else if (this.loadOptions.requestInit != null) this.handler = this.io.browserHTTPRequest(e, this.loadOptions); else { - let t6 = this.io.getLoadHandlers(e, this.loadOptions); - if (t6.length === 0) - t6.push(this.io.browserHTTPRequest(e, this.loadOptions)); - else if (t6.length > 1) - throw new Error(`Found more than one (${t6.length}) load handlers for URL '${[e]}'`); - this.handler = t6[0]; + let t10 = this.io.getLoadHandlers(e, this.loadOptions); + if (t10.length === 0) + t10.push(this.io.browserHTTPRequest(e, this.loadOptions)); + else if (t10.length > 1) + throw new Error(`Found more than one (${t10.length}) load handlers for URL '${[e]}'`); + this.handler = t10[0]; } } load() { if (this.findIOHandler(), this.handler.load == null) throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); let e = this.handler.load(); - return y.isPromise(e) ? e.then((t6) => this.loadSync(t6)) : this.loadSync(e); + return y.isPromise(e) ? e.then((t10) => this.loadSync(t10)) : this.loadSync(e); } loadSync(e) { this.artifacts = e; - let t6 = this.artifacts.modelTopology, o = this.artifacts.signature; + let t10 = this.artifacts.modelTopology, o = this.artifacts.signature; if (this.artifacts.userDefinedMetadata != null) { let s = this.artifacts.userDefinedMetadata; s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys); } - this.signature = o, this.version = `${t6.versions.producer}.${t6.versions.minConsumer}`; + this.signature = o, this.version = `${t10.versions.producer}.${t10.versions.minConsumer}`; let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs); - if (this.executor = new Cu(pl.Instance.transformGraph(t6, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) { - let s = pl.Instance.transformGraph(e.modelInitializer); - this.initializer = new Cu(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature; + if (this.executor = new ku(ml.Instance.transformGraph(t10, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) { + let s = ml.Instance.transformGraph(e.modelInitializer); + this.initializer = new ku(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature; } return true; } - async save(e, t6) { + async save(e, t10) { if (typeof e == "string") { let o = this.io.getSaveHandlers(e); if (o.length === 0) @@ -11435,23 +11424,23 @@ var ll = class { } addStructuredOutputNames(e) { if (this.structuredOutputKeys) { - let t6 = e instanceof it ? [e] : e, o = {}; - return t6.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o; + let t10 = e instanceof it ? [e] : e, o = {}; + return t10.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o; } return e; } - predict(e, t6) { + predict(e, t10) { let o = this.execute(e, this.outputNodes); return this.addStructuredOutputNames(o); } - async predictAsync(e, t6) { + async predictAsync(e, t10) { let o = await this.executeAsync(e, this.outputNodes); return this.addStructuredOutputNames(o); } normalizeInputs(e) { - var t6; + var t10; if (!(e instanceof it) && !Array.isArray(e)) { - let s = (t6 = this.signature) === null || t6 === void 0 ? void 0 : t6.inputs; + let s = (t10 = this.signature) === null || t10 === void 0 ? void 0 : t10.inputs; if (s != null) for (let a in s) { let i = s[a]; @@ -11481,21 +11470,21 @@ var ll = class { } setResourceIdToCapturedInput(e) { if (this.resourceIdToCapturedInput = {}, this.initializerSignature) { - let t6 = this.initializerSignature.outputs, o = Object.keys(t6); + let t10 = this.initializerSignature.outputs, o = Object.keys(t10); for (let n = 0; n < o.length; n++) { - let s = o[n], a = t6[s]; + let s = o[n], a = t10[s]; this.resourceIdToCapturedInput[a.resourceId] = e[n]; } } } - execute(e, t6) { - this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t6 = this.normalizeOutputs(t6); - let o = this.executor.execute(e, t6); + execute(e, t10) { + this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10); + let o = this.executor.execute(e, t10); return o.length > 1 ? o : o[0]; } - async executeAsync(e, t6) { - this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t6 = this.normalizeOutputs(t6); - let o = await this.executor.executeAsync(e, t6); + async executeAsync(e, t10) { + this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10); + let o = await this.executor.executeAsync(e, t10); return o.length > 1 ? o : o[0]; } getIntermediateTensors() { @@ -11505,20 +11494,20 @@ var ll = class { this.executor.disposeIntermediateTensors(); } convertTensorMapToTensorsMap(e) { - return Object.keys(e).reduce((t6, o) => (t6[o] = [e[o]], t6), {}); + return Object.keys(e).reduce((t10, o) => (t10[o] = [e[o]], t10), {}); } dispose() { - this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Dt(this.resourceIdToCapturedInput)), this.resourceManager.dispose(); + this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose(); } }; -async function l6(r, e = {}, t6 = Ea) { +async function A6(r, e = {}, t10 = La) { if (r == null) throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model"); - e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = d6(r)); - let o = new ll(r, e, t6); + e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = D6(r)); + let o = new fl(r, e, t10); return await o.load(), o; } -function m6(r) { +function R6(r) { if (r == null) throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model"); let e; @@ -11532,35 +11521,35 @@ function m6(r) { throw new Error("Model JSON is missing 'modelTopology'"); if (!("weightsManifest" in o)) throw new Error("Model JSON is missing 'weightsManifest'"); - let s = Ea.getWeightSpecs(o.weightsManifest), a = Ea.getModelArtifactsForJSONSync(o, s, n); - e = Ea.fromMemorySync(a); + let s = La.getWeightSpecs(o.weightsManifest), a = La.getModelArtifactsForJSONSync(o, s, n); + e = La.fromMemorySync(a); } else if ("load" in r) e = r; else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r) - e = Ea.fromMemorySync(r); + e = La.fromMemorySync(r); else throw new Error("Unknown model format"); - let t6 = new ll(e); - return t6.load(), t6; + let t10 = new fl(e); + return t10.load(), t10; } -function d6(r) { - return r.endsWith("/") || (r = r + "/"), `${r}${c6}${p6}`; +function D6(r) { + return r.endsWith("/") || (r = r + "/"), `${r}${E6}${$6}`; } -var f6 = "4.1.0"; -function K(r, e) { - Array.isArray(r) || (r = [r]), r.forEach((t6) => { - t6 != null && y.assert(t6.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`); +var F6 = "4.2.0"; +function j(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t10) => { + t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`); }); } -var h6 = Lt.whereImpl; -var Oi = class extends Zr { +var O6 = Vt.whereImpl; +var Pi = class extends Zr { constructor() { - super(), this.blockSize = 48, this.firstUse = true, this.data = new Do(this, cr()); + super(), this.blockSize = 48, this.firstUse = true, this.data = new Fo(this, sr()); } nextDataId() { - return Oi.nextDataId++; + return Pi.nextDataId++; } - write(e, t6, o) { + write(e, t10, o) { this.firstUse && (this.firstUse = false, O().get("IS_NODE") && S.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. @@ -11568,30 +11557,30 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram let n = { id: this.nextDataId() }; return this.data.set(n, { values: e, dtype: o, refCount: 1 }), n; } - makeTensorInfo(e, t6, o) { + makeTensorInfo(e, t10, o) { let n; - if (t6 === "string" && o != null && o.length > 0 && y.isString(o[0])) { + if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) { let s = o.map((a) => y.encodeString(a)); - n = this.write(s, e, t6); + n = this.write(s, e, t10); } else - n = this.write(o, e, t6); - return { dataId: n, shape: e, dtype: t6 }; + n = this.write(o, e, t10); + return { dataId: n, shape: e, dtype: t10 }; } refCount(e) { return this.data.has(e) ? this.data.get(e).refCount : 0; } incRef(e) { - let t6 = this.data.get(e); - t6.refCount++; + let t10 = this.data.get(e); + t10.refCount++; } decRef(e) { if (this.data.has(e)) { - let t6 = this.data.get(e); - t6.refCount--; + let t10 = this.data.get(e); + t10.refCount--; } } - move(e, t6, o, n, s) { - this.data.set(e, { values: t6, dtype: n, refCount: s }); + move(e, t10, o, n, s) { + this.data.set(e, { values: t10, dtype: n, refCount: s }); } numDataIds() { return this.data.numDataIds(); @@ -11600,30 +11589,30 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return this.readSync(e); } readSync(e) { - let { dtype: t6, complexTensorInfos: o } = this.data.get(e); - if (t6 === "complex64") { + let { dtype: t10, complexTensorInfos: o } = this.data.get(e); + if (t10 === "complex64") { let n = this.readSync(o.real.dataId), s = this.readSync(o.imag.dataId); return S.mergeRealAndImagArrays(n, s); } - return this.data.get(e).values; + return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t10); } bufferSync(e) { - let t6 = this.readSync(e.dataId); + let t10 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t6.map((n) => y.decodeString(n)); + let o = t10.map((n) => y.decodeString(n)); return le(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return le(e.shape, e.dtype, t6); + return le(e.shape, e.dtype, t10); } - makeOutput(e, t6, o) { - return cr().makeTensorFromTensorInfo(this.makeTensorInfo(t6, o, e), this); + makeOutput(e, t10, o) { + return sr().makeTensorFromTensorInfo(this.makeTensorInfo(t10, o, e), this); } - disposeData(e, t6 = false) { + disposeData(e, t10 = false) { if (this.data.has(e)) { - if (this.data.get(e).refCount--, !t6 && this.data.get(e).refCount > 0) + if (this.data.get(e).refCount--, !t10 && this.data.get(e).refCount > 0) return false; let { complexTensorInfos: o } = this.data.get(e); o != null && (this.disposeData(o.real.dataId, true), this.disposeData(o.imag.dataId, true)), this.data.delete(e); @@ -11634,16 +11623,16 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram this.disposeData(e.dataId); } async time(e) { - let t6 = y.now(); - return e(), { kernelMs: y.now() - t6 }; + let t10 = y.now(); + return e(), { kernelMs: y.now() - t10 }; } memory() { return { unreliable: true, reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] }; } where(e) { - K([e], "where"); - let t6 = this.readSync(e.dataId); - return h6(e.shape, t6); + j([e], "where"); + let t10 = this.readSync(e.dataId); + return O6(e.shape, t10); } dispose() { } @@ -11654,34 +11643,34 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return super.epsilon(); } }; -Oi.nextDataId = 0; -var Qp = {}; -Ue(Qp, { addImpl: () => QC, bincountImpl: () => Kp, bincountReduceImpl: () => uf, castImpl: () => YC, ceilImpl: () => ZC, concatImpl: () => Su, equalImpl: () => JC, expImpl: () => tS, expm1Impl: () => oS, floorImpl: () => nS, gatherNdImpl: () => pf, gatherV2Impl: () => cf, greaterEqualImpl: () => aS, greaterImpl: () => sS, lessEqualImpl: () => uS, lessImpl: () => iS, linSpaceImpl: () => lf, logImpl: () => pS, maxImpl: () => mf, maximumImpl: () => cS, minimumImpl: () => lS, multiplyImpl: () => ml, negImpl: () => mS, notEqualImpl: () => dS, prodImpl: () => fS, raggedGatherImpl: () => df, raggedRangeImpl: () => ff, raggedTensorToTensorImpl: () => hf, rangeImpl: () => Iu, rsqrtImpl: () => hS, scatterImpl: () => Ma, sigmoidImpl: () => ET, simpleAbsImpl: () => XC, sliceImpl: () => vu, sparseFillEmptyRowsImpl: () => gf, sparseReshapeImpl: () => xf, sparseSegmentReductionImpl: () => Yp, sqrtImpl: () => RT, squaredDifferenceImpl: () => xS, stridedSliceImpl: () => yf, stringNGramsImpl: () => ku, stringSplitImpl: () => Nu, stringToHashBucketFastImpl: () => Tu, subImpl: () => bS, tileImpl: () => bf, topKImpl: () => Cf, transposeImpl: () => jp, uniqueImpl: () => Sf }); -function XC(r) { +Pi.nextDataId = 0; +var Zp = {}; +Ge(Zp, { addImpl: () => uS, bincountImpl: () => jp, bincountReduceImpl: () => uf, castImpl: () => iS, ceilImpl: () => pS, concatImpl: () => Nu, equalImpl: () => cS, expImpl: () => mS, expm1Impl: () => fS, floorImpl: () => hS, gatherNdImpl: () => pf, gatherV2Impl: () => cf, greaterEqualImpl: () => xS, greaterImpl: () => gS, lessEqualImpl: () => bS, lessImpl: () => yS, linSpaceImpl: () => lf, logImpl: () => CS, maxImpl: () => mf, maximumImpl: () => SS, minimumImpl: () => wS, multiplyImpl: () => hl, negImpl: () => IS, notEqualImpl: () => vS, prodImpl: () => kS, raggedGatherImpl: () => df, raggedRangeImpl: () => ff, raggedTensorToTensorImpl: () => hf, rangeImpl: () => _u, rsqrtImpl: () => NS, scatterImpl: () => Va, sigmoidImpl: () => OT, simpleAbsImpl: () => aS, sliceImpl: () => $u, sparseFillEmptyRowsImpl: () => gf, sparseReshapeImpl: () => xf, sparseSegmentReductionImpl: () => Qp, sqrtImpl: () => LT, squaredDifferenceImpl: () => _S, stridedSliceImpl: () => yf, stringNGramsImpl: () => Eu, stringSplitImpl: () => Au, stringToHashBucketFastImpl: () => Ru, subImpl: () => ES, tileImpl: () => bf, topKImpl: () => Cf, transposeImpl: () => Xp, uniqueImpl: () => Sf }); +function aS(r) { let e = new Float32Array(r.length); - for (let t6 = 0; t6 < r.length; ++t6) - e[t6] = Math.abs(r[t6]); + for (let t10 = 0; t10 < r.length; ++t10) + e[t10] = Math.abs(r[t10]); return e; } -var g6 = (r) => { - let { x: e } = r.inputs, t6 = r.backend; - K(e, "abs"); - let o = new Float32Array(y.sizeFromShape(e.shape)), n = t6.data.get(e.dataId).values; - return o = XC(n), t6.makeOutput(o, e.shape, e.dtype); +var P6 = (r) => { + let { x: e } = r.inputs, t10 = r.backend; + j(e, "abs"); + let o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values; + return o = aS(n), t10.makeOutput(o, e.shape, e.dtype); }; -var tT = { kernelName: gs, backendName: "cpu", kernelFunc: g6 }; +var iT = { kernelName: ys, backendName: "cpu", kernelFunc: P6 }; function Be(r) { - return (e, t6, o, n, s) => { - let a = S.assertAndGetBroadcastShape(e, t6), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t6.length, d = y.computeStrides(e), f = y.computeStrides(t6), h = S.getBroadcastDims(e, a), g = S.getBroadcastDims(t6, a); + return (e, t10, o, n, s) => { + let a = S.assertAndGetBroadcastShape(e, t10), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, d = y.computeStrides(e), f = y.computeStrides(t10), h = S.getBroadcastDims(e, a), g = S.getBroadcastDims(t10, a); if (h.length + g.length === 0) for (let x = 0; x < c.length; ++x) c[x] = r(o[x % o.length], n[x % n.length]); else for (let x = 0; x < c.length; ++x) { let b = y.indexToLoc(x, i, p), C = b.slice(-l); - h.forEach(($) => C[$] = 0); + h.forEach((E) => C[E] = 0); let w = y.locToIndex(C, l, d), k = b.slice(-m); - g.forEach(($) => k[$] = 0); + g.forEach((E) => k[E] = 0); let _ = y.locToIndex(k, m, f); c[x] = r(o[w], n[_]); } @@ -11689,69 +11678,69 @@ function Be(r) { }; } function Ht(r) { - let { inputs: e, backend: t6 } = r, { real: o, imag: n } = e, s = t6.data.get(o.dataId).values, a = t6.data.get(n.dataId).values, i = t6.makeTensorInfo(o.shape, "complex64"), p = t6.data.get(i.dataId); - return p.complexTensorInfos = { real: t6.makeTensorInfo(o.shape, "float32", s), imag: t6.makeTensorInfo(n.shape, "float32", a) }, i; + let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = t10.makeTensorInfo(o.shape, "complex64"), p = t10.data.get(i.dataId); + return p.complexTensorInfos = { real: t10.makeTensorInfo(o.shape, "float32", s), imag: t10.makeTensorInfo(n.shape, "float32", a) }, i; } -var rT = { kernelName: ei, backendName: "cpu", kernelFunc: Ht }; -function Hp(r, e, t6 = "float32") { - if (t6 === "complex64") { - let n = Hp(r, e, "float32"), s = Hp(r, e, "float32"); +var uT = { kernelName: ri, backendName: "cpu", kernelFunc: Ht }; +function Kp(r, e, t10 = "float32") { + if (t10 === "complex64") { + let n = Kp(r, e, "float32"), s = Kp(r, e, "float32"); return Ht({ inputs: { real: n, imag: s }, backend: r }); } - let o = y.makeZerosTypedArray(y.sizeFromShape(e), t6); - return r.makeTensorInfo(e, t6, o); + let o = y.makeZerosTypedArray(y.sizeFromShape(e), t10); + return r.makeTensorInfo(e, t10, o); } -function ar(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - return t6.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; +function ir(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; + return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var oT = { kernelName: mo, backendName: "cpu", kernelFunc: ar }; -function wo(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.data.get(o.dataId).complexTensorInfos.real, s = t6.data.get(n.dataId).values; - return t6.makeTensorInfo(n.shape, n.dtype, s); +var pT = { kernelName: mo, backendName: "cpu", kernelFunc: ir }; +function Io(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.real, s = t10.data.get(n.dataId).values; + return t10.makeTensorInfo(n.shape, n.dtype, s); } -var nT = { kernelName: ai, backendName: "cpu", kernelFunc: wo }; -function YC(r, e, t6, o) { +var cT = { kernelName: di, backendName: "cpu", kernelFunc: Io }; +function iS(r, e, t10, o) { if (o === "int32") { let n = Int32Array.from(r); return [e, "int32", n]; } if (o === "bool") { - let n = y.toTypedArray([0], t6), [s, a] = Be((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool"); + let n = y.toTypedArray([0], t10), [s, a] = Be((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool"); return [a, "bool", s]; } - throw new Error(`Error in Cast: failed to cast ${t6} to ${o}`); + throw new Error(`Error in Cast: failed to cast ${t10} to ${o}`); } -function Io(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function vo(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return ar({ inputs: { x: n }, backend: t6 }); - let c = Hp(t6, n.shape, n.dtype), l = Io({ inputs: { x: n }, backend: t6, attrs: { dtype: "float32" } }), m = Ht({ inputs: { real: l, imag: c }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(c), t6.disposeIntermediateTensorInfo(l), m; + return ir({ inputs: { x: n }, backend: t10 }); + let c = Kp(t10, n.shape, n.dtype), l = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = Ht({ inputs: { real: l, imag: c }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m; } if (n.dtype === "complex64") { - let c = wo({ inputs: { input: n }, backend: t6 }), l = Io({ inputs: { x: c }, backend: t6, attrs: { dtype: s } }); - return t6.disposeIntermediateTensorInfo(c), l; + let c = Io({ inputs: { input: n }, backend: t10 }), l = vo({ inputs: { x: c }, backend: t10, attrs: { dtype: s } }); + return t10.disposeIntermediateTensorInfo(c), l; } if (!y.hasEncodingLoss(n.dtype, s)) { - let c = ar({ inputs: { x: n }, backend: t6 }); + let c = ir({ inputs: { x: n }, backend: t10 }); return { dataId: c.dataId, shape: c.shape, dtype: s }; } - let a = t6.data.get(n.dataId).values, [i, p, u] = YC(a, n.shape, n.dtype, s); - return t6.makeTensorInfo(i, p, u); + let a = t10.data.get(n.dataId).values, [i, p, u] = iS(a, n.shape, n.dtype, s); + return t10.makeTensorInfo(i, p, u); } -var sT = { kernelName: co, backendName: "cpu", kernelFunc: Io }; -function Qe(r, e, t6, o) { - return t6 == null ? ({ inputs: n, backend: s }) => { +var lT = { kernelName: co, backendName: "cpu", kernelFunc: vo }; +function Qe(r, e, t10, o) { + return t10 == null ? ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; - K([a, i], r); + j([a, i], r); let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? S.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? S.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = e(a.shape, i.shape, l, m, d); return p.makeTensorInfo(h, d, f); } : ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; if (a.dtype === "complex64" || i.dtype === "complex64") { - let u = Io({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = Io({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, w = p.data.get(b.dataId).values, [k, _, $] = t6(a.shape, i.shape, d, f, C, w), A = p.makeTensorInfo($, "float32", k), R = p.makeTensorInfo($, "float32", _), D = Ht({ inputs: { real: A, imag: R }, backend: p }); + let u = vo({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = vo({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, w = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, d, f, C, w), A = p.makeTensorInfo(E, "float32", k), R = p.makeTensorInfo(E, "float32", _), D = Ht({ inputs: { real: A, imag: R }, backend: p }); return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(A), p.disposeIntermediateTensorInfo(R), D; } else { let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, d] = e(a.shape, i.shape, u, c, l); @@ -11760,18 +11749,18 @@ function Qe(r, e, t6, o) { }; } function qp(r) { - return (e, t6, o, n, s, a) => { - let i = S.assertAndGetBroadcastShape(e, t6), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = S.getBroadcastDims(e, i), f = S.getBroadcastDims(t6, i), h = S.mergeRealAndImagArrays(o, n), g = S.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), C = t6.length, w = y.computeStrides(t6); + return (e, t10, o, n, s, a) => { + let i = S.assertAndGetBroadcastShape(e, t10), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = S.getBroadcastDims(e, i), f = S.getBroadcastDims(t10, i), h = S.mergeRealAndImagArrays(o, n), g = S.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), C = t10.length, w = y.computeStrides(t10); if (d.length + f.length === 0) for (let k = 0; k < l.length; k++) { - let _ = k % h.length, $ = k % g.length, A = r(h[_ * 2], h[_ * 2 + 1], g[$ * 2], g[$ * 2 + 1]); + let _ = k % h.length, E = k % g.length, A = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]); l[k] = A.real, m[k] = A.imag; } else for (let k = 0; k < l.length; k++) { - let _ = y.indexToLoc(k, u, c), $ = _.slice(-x); - d.forEach((M) => $[M] = 0); - let A = y.locToIndex($, x, b), R = _.slice(-C); + let _ = y.indexToLoc(k, u, c), E = _.slice(-x); + d.forEach((M) => E[M] = 0); + let A = y.locToIndex(E, x, b), R = _.slice(-C); f.forEach((M) => R[M] = 0); let D = y.locToIndex(R, C, w), P = r(h[A * 2], h[A * 2 + 1], g[D * 2], g[D * 2 + 1]); l[k] = P.real, m[k] = P.imag; @@ -11779,12 +11768,12 @@ function qp(r) { return [l, m, i]; }; } -var QC = Be((r, e) => r + e); -var x6 = qp((r, e, t6, o) => ({ real: r + t6, imag: e + o })); -var js = Qe(eo, QC, x6); -var aT = { kernelName: eo, backendName: "cpu", kernelFunc: js }; -function Kp(r, e, t6, o, n) { - let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t6); +var uS = Be((r, e) => r + e); +var M6 = qp((r, e, t10, o) => ({ real: r + t10, imag: e + o })); +var Xs = Qe(eo, uS, M6); +var mT = { kernelName: eo, backendName: "cpu", kernelFunc: Xs }; +function jp(r, e, t10, o, n) { + let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t10); for (let i = 0; i < r.length; i++) { let p = r[i]; if (p < 0) @@ -11793,51 +11782,51 @@ function Kp(r, e, t6, o, n) { } return a; } -function uf(r, e, t6, o = false) { - let n = r.shape[0], s = r.shape[1], a = le([n, t6], e.dtype); +function uf(r, e, t10, o = false) { + let n = r.shape[0], s = r.shape[1], a = le([n, t10], e.dtype); for (let i = 0; i < n; i++) for (let p = 0; p < s; p++) { let u = r.get(i, p); if (u < 0) throw new Error("Input x must be non-negative!"); - u >= t6 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u)); + u >= t10 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u)); } return a; } function vr(r) { - return (e, t6, o) => { - let n = y.getTypedArrayFromDType(t6, e.length); + return (e, t10, o) => { + let n = y.getTypedArrayFromDType(t10, e.length); for (let s = 0; s < e.length; ++s) n[s] = r(e[s], o); return n; }; } -function Ie(r, e, t6) { +function we(r, e, t10) { return ({ inputs: o, attrs: n, backend: s }) => { let { x: a } = o; - if (K(a, r), a.dtype === "string" || t6 === "string") + if (j(a, r), a.dtype === "string" || t10 === "string") throw new Error("unaryKernelFunc does not support string input/output"); - let i = s, p = i.data.get(a.dataId).values, u = y.sizeFromShape(a.shape), c = t6 || a.dtype, l = y.getArrayFromDType(c, u); + let i = s, p = i.data.get(a.dataId).values, u = y.sizeFromShape(a.shape), c = t10 || a.dtype, l = y.getArrayFromDType(c, u); for (let m = 0; m < u; ++m) l[m] = e(p[m], n); return i.makeTensorInfo(a.shape, c, l); }; } -function vo(r, e, t6) { +function ko(r, e, t10) { return ({ inputs: o, attrs: n, backend: s }) => { let { x: a } = o; - if (K(a, r), a.dtype === "string" || t6 === "string") + if (j(a, r), a.dtype === "string" || t10 === "string") throw new Error("unaryKernelFunc does not support string input/output"); - let i = s, p = i.data.get(a.dataId).values, u = t6 || a.dtype, c = e(p, u, n); + let i = s, p = i.data.get(a.dataId).values, u = t10 || a.dtype, c = e(p, u, n); return i.makeTensorInfo(a.shape, u, c); }; } -var ZC = vr((r) => Math.ceil(r)); -var y6 = vo(Uo, ZC); -var iT = { kernelName: Uo, backendName: "cpu", kernelFunc: y6 }; -function Su(r, e, t6, o) { - let n = y.getArrayFromDType(t6, y.sizeFromShape(e)); - if (o && t6 !== "string") { +var pS = vr((r) => Math.ceil(r)); +var L6 = ko(Wo, pS); +var dT = { kernelName: Wo, backendName: "cpu", kernelFunc: L6 }; +function Nu(r, e, t10, o) { + let n = y.getArrayFromDType(t10, y.sizeFromShape(e)); + if (o && t10 !== "string") { let s = 0; r.forEach((a) => { let i = y.sizeFromShape(a.shape); @@ -11846,7 +11835,7 @@ function Su(r, e, t6, o) { } else { let s = 0; r.forEach((a) => { - let i = t6 === "string" ? S.fromUint8ToStringArray(a.vals) : a.vals, p = 0; + let i = t10 === "string" ? S.fromUint8ToStringArray(a.vals) : a.vals, p = 0; for (let u = 0; u < a.shape[0]; ++u) { let c = u * e[1] + s; for (let l = 0; l < a.shape[1]; ++l) @@ -11857,20 +11846,20 @@ function Su(r, e, t6, o) { } return n; } -var JC = Be((r, e) => r === e ? 1 : 0); -var eS = Qe(tn, JC, null, "bool"); -var uT = { kernelName: tn, backendName: "cpu", kernelFunc: eS }; -var tS = vr((r) => Math.exp(r)); -var rS = vo(rn, tS, "float32"); -var pT = { kernelName: rn, backendName: "cpu", kernelFunc: rS }; -var oS = vr((r) => Math.expm1(r)); -var b6 = vo(da, oS); -var cT = { kernelName: da, backendName: "cpu", kernelFunc: b6 }; -var nS = vr((r) => Math.floor(r)); -var C6 = vo(nn, nS); -var lT = { kernelName: nn, backendName: "cpu", kernelFunc: C6 }; -function pf(r, e, t6, o, n, s, a, i, p) { - let u = le([o, s], t6); +var cS = Be((r, e) => r === e ? 1 : 0); +var lS = Qe(en, cS, null, "bool"); +var fT = { kernelName: en, backendName: "cpu", kernelFunc: lS }; +var mS = vr((r) => Math.exp(r)); +var dS = ko(tn, mS, "float32"); +var hT = { kernelName: tn, backendName: "cpu", kernelFunc: dS }; +var fS = vr((r) => Math.expm1(r)); +var B6 = ko(fa, fS); +var gT = { kernelName: fa, backendName: "cpu", kernelFunc: B6 }; +var hS = vr((r) => Math.floor(r)); +var V6 = ko(on, hS); +var xT = { kernelName: on, backendName: "cpu", kernelFunc: V6 }; +function pf(r, e, t10, o, n, s, a, i, p) { + let u = le([o, s], t10); for (let c = 0; c < o; c++) { let l = [], m = 0; for (let d = 0; d < n; d++) { @@ -11884,8 +11873,8 @@ function pf(r, e, t6, o, n, s, a, i, p) { } return u; } -function cf(r, e, t6) { - let o = le(t6, r.dtype); +function cf(r, e, t10) { + let o = le(t10, r.dtype); for (let n = 0; n < o.size; ++n) { let a = o.indexToLoc(n).slice(), i = a[0], p = a[2], u = e.locToIndex([i, p]); a[2] = e.values[u]; @@ -11894,30 +11883,30 @@ function cf(r, e, t6) { } return o; } -var sS = Be((r, e) => r > e ? 1 : 0); -var S6 = Qe(pn, sS, null, "bool"); -var mT = { kernelName: pn, backendName: "cpu", kernelFunc: S6 }; -var aS = Be((r, e) => r >= e ? 1 : 0); -var w6 = Qe(cn, aS, null, "bool"); -var dT = { kernelName: cn, backendName: "cpu", kernelFunc: w6 }; -var iS = Be((r, e) => r < e ? 1 : 0); -var I6 = Qe(dn, iS, null, "bool"); -var fT = { kernelName: dn, backendName: "cpu", kernelFunc: I6 }; -var uS = Be((r, e) => r <= e ? 1 : 0); -var v6 = Qe(fn, uS, null, "bool"); -var hT = { kernelName: fn, backendName: "cpu", kernelFunc: v6 }; -function lf(r, e, t6) { - let o = (e - r) / (t6 - 1), n = y.makeZerosTypedArray(t6, "float32"); +var gS = Be((r, e) => r > e ? 1 : 0); +var z6 = Qe(un, gS, null, "bool"); +var yT = { kernelName: un, backendName: "cpu", kernelFunc: z6 }; +var xS = Be((r, e) => r >= e ? 1 : 0); +var W6 = Qe(pn, xS, null, "bool"); +var bT = { kernelName: pn, backendName: "cpu", kernelFunc: W6 }; +var yS = Be((r, e) => r < e ? 1 : 0); +var U6 = Qe(mn, yS, null, "bool"); +var CT = { kernelName: mn, backendName: "cpu", kernelFunc: U6 }; +var bS = Be((r, e) => r <= e ? 1 : 0); +var G6 = Qe(dn, bS, null, "bool"); +var ST = { kernelName: dn, backendName: "cpu", kernelFunc: G6 }; +function lf(r, e, t10) { + let o = (e - r) / (t10 - 1), n = y.makeZerosTypedArray(t10, "float32"); n[0] = r; for (let s = 1; s < n.length; s++) n[s] = n[s - 1] + o; return n; } -var pS = vr((r) => Math.log(r)); -var k6 = vo(hn, pS); -var gT = { kernelName: hn, backendName: "cpu", kernelFunc: k6 }; -function mf(r, e, t6, o) { - let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t6)); +var CS = vr((r) => Math.log(r)); +var H6 = ko(fn, CS); +var wT = { kernelName: fn, backendName: "cpu", kernelFunc: H6 }; +function mf(r, e, t10, o) { + let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t10)); for (let s = 0; s < n.length; ++s) { let a = s * e, i = r[a]; for (let p = 0; p < e; ++p) { @@ -11928,32 +11917,32 @@ function mf(r, e, t6, o) { } return n; } -var cS = Be((r, e) => Math.max(r, e)); -var N6 = Qe(bn, cS); -var xT = { kernelName: bn, backendName: "cpu", kernelFunc: N6 }; -var lS = Be((r, e) => Math.min(r, e)); -var T6 = Qe(In, lS); -var yT = { kernelName: In, backendName: "cpu", kernelFunc: T6 }; -var ml = Be((r, e) => r * e); -var _6 = qp((r, e, t6, o) => ({ real: r * t6 - e * o, imag: r * o + e * t6 })); -var wu = Qe(kn, ml, _6); -var bT = { kernelName: kn, backendName: "cpu", kernelFunc: wu }; -function mS(r, e, t6) { - let o = y.createScalarValue(-1, t6); - return ml([], e, o, r, t6); +var SS = Be((r, e) => Math.max(r, e)); +var K6 = Qe(bn, SS); +var IT = { kernelName: bn, backendName: "cpu", kernelFunc: K6 }; +var wS = Be((r, e) => Math.min(r, e)); +var q6 = Qe(In, wS); +var vT = { kernelName: In, backendName: "cpu", kernelFunc: q6 }; +var hl = Be((r, e) => r * e); +var j6 = qp((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 })); +var Tu = Qe(kn, hl, j6); +var kT = { kernelName: kn, backendName: "cpu", kernelFunc: Tu }; +function IS(r, e, t10) { + let o = y.createScalarValue(-1, t10); + return hl([], e, o, r, t10); } -function E6(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - K(o, "neg"); - let n = t6.data.get(o.dataId).values, [s, a] = mS(n, o.shape, o.dtype); - return t6.makeTensorInfo(a, o.dtype, s); +function X6(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; + j(o, "neg"); + let n = t10.data.get(o.dataId).values, [s, a] = IS(n, o.shape, o.dtype); + return t10.makeTensorInfo(a, o.dtype, s); } -var CT = { kernelName: ws, backendName: "cpu", kernelFunc: E6 }; -var dS = Be((r, e) => r !== e ? 1 : 0); -var $6 = Qe(Nn, dS, null, "bool"); -var ST = { kernelName: Nn, backendName: "cpu", kernelFunc: $6 }; -function jp(r, e, t6, o, n) { - let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t6, y.sizeFromShape(n)); +var NT = { kernelName: vs, backendName: "cpu", kernelFunc: X6 }; +var vS = Be((r, e) => r !== e ? 1 : 0); +var Y6 = Qe(Nn, vS, null, "bool"); +var TT = { kernelName: Nn, backendName: "cpu", kernelFunc: Y6 }; +function Xp(r, e, t10, o, n) { + let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t10, y.sizeFromShape(n)); for (let c = 0; c < a; ++c) { let l = y.indexToLoc(c, s, i), m = new Array(l.length); for (let f = 0; f < m.length; f++) @@ -11963,46 +11952,46 @@ function jp(r, e, t6, o, n) { } return u; } -function Ct(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n } = e, { perm: s } = t6; - K(n, "transpose"); +function St(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { perm: s } = t10; + j(n, "transpose"); let a = n.shape.length, i = new Array(a); for (let l = 0; l < i.length; l++) i[l] = n.shape[s[l]]; - let p = o.data.get(n.dataId).values, u = jp(p, n.shape, n.dtype, s, i); + let p = o.data.get(n.dataId).values, u = Xp(p, n.shape, n.dtype, s, i); return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype }; } -var wT = { kernelName: ro, backendName: "cpu", kernelFunc: Ct }; -function fS(r, e, t6, o) { +var _T = { kernelName: ro, backendName: "cpu", kernelFunc: St }; +function kS(r, e, t10, o) { let [n, s] = S.computeOutAndReduceShapes(r, o), a = dt(e, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s); for (let u = 0; u < i.length; ++u) { let c = u * p, l = 1; for (let m = 0; m < p; ++m) - l *= t6[c + m]; + l *= t10[c + m]; i[u] = l; } return { outVals: i, outShape: n, outDtype: a }; } -function A6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - K(n, "prod"); +function Q6(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + j(n, "prod"); let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = S.getAxesPermutation(p, i), c = p, l = n, m = []; - u != null && (l = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: u } }), m.push(l), c = S.getInnerMostAxes(c.length, i)); - let d = t6.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = fS(l.shape, l.dtype, d, c), x = h; - return a && (x = S.expandShapeToKeepDim(h, p)), m.forEach((b) => t6.disposeIntermediateTensorInfo(b)), t6.makeTensorInfo(x, g, f); + u != null && (l = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), m.push(l), c = S.getInnerMostAxes(c.length, i)); + let d = t10.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = kS(l.shape, l.dtype, d, c), x = h; + return a && (x = S.expandShapeToKeepDim(h, p)), m.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.makeTensorInfo(x, g, f); } -var IT = { kernelName: Fn, backendName: "cpu", kernelFunc: A6 }; -function R6(r, e, t6) { +var $T = { kernelName: Dn, backendName: "cpu", kernelFunc: Q6 }; +function Z6(r, e, t10) { r.forEach((o, n) => { - if (o < 0 || o >= t6) { + if (o < 0 || o >= t10) { let s = y.indexToLoc(n, e.length, y.computeStrides(e)).join(","); - throw new Error(`indices[${s}] = ${o} is not in [0, ${t6})`); + throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`); } }); } -function F6(r, e) { - for (let t6 = 0; t6 < r.length; ++t6) { - let o = r[t6], n = t6 === r.length - 1 ? e : r[t6 + 1].length; +function J6(r, e) { + for (let t10 = 0; t10 < r.length; ++t10) { + let o = r[t10], n = t10 === r.length - 1 ? e : r[t10 + 1].length; if (o.length === 0) throw new Error("Ragged splits may not be empty"); if (o[0] < 0) @@ -12014,9 +12003,9 @@ function F6(r, e) { throw new Error("Ragged splits must be sorted in ascending order"); } } -function D6(r, e, t6, o) { - let n = [], s = 0, a = e.length - 1 + t6.length, i = new Array(a).fill(null).map(() => [0]); - F6(t6, o); +function ej(r, e, t10, o) { + let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]); + J6(t10, o); let p = 1; for (let u = 0; u < e.length - 1; ++u) { p *= e[u]; @@ -12026,8 +12015,8 @@ function D6(r, e, t6, o) { } for (let u = 0; u < r.length; ++u) { let c = r[u], l = r[u] + 1; - for (let m = 0; m < t6.length; ++m) { - let d = t6[m], f = m + e.length - 1; + for (let m = 0; m < t10.length; ++m) { + let d = t10[m], f = m + e.length - 1; if (f >= 0) { let h = i[f], g = h[h.length - 1] - d[c]; for (let x = c; x < l; ++x) @@ -12039,50 +12028,50 @@ function D6(r, e, t6, o) { } return { outSplits: i, valueSlices: n, numValues: s }; } -function O6(r) { +function tj(r) { let e = []; - for (let t6 = 0; t6 < r.length; ++t6) { - let o = r[t6].length, n = y.getArrayFromDType("int32", o); - e.push(n), r[t6].forEach((s, a) => n[a] = s); + for (let t10 = 0; t10 < r.length; ++t10) { + let o = r[t10].length, n = y.getArrayFromDType("int32", o); + e.push(n), r[t10].forEach((s, a) => n[a] = s); } return e; } -function vT(r, e) { - let t6 = r.slice(0, e); - for (; t6.length < e; ) - t6.push(1); +function ET(r, e) { + let t10 = r.slice(0, e); + for (; t10.length < e; ) + t10.push(1); for (let o = e; o < r.length; o++) - t6[e - 1] *= r[o]; - return t6; + t10[e - 1] *= r[o]; + return t10; } -function P6(r, e, t6, o, n, s) { - let a = vT(e, 2)[1], i = vT(s, 2)[1], p = 0; - for (let u of t6) +function rj(r, e, t10, o, n, s) { + let a = ET(e, 2)[1], i = ET(s, 2)[1], p = 0; + for (let u of t10) for (let c = u[0]; c < u[1]; ++c) { for (let l = 0; l < o; ++l) n[p * i + l] = r[c * a + l]; ++p; } } -function M6(r, e, t6, o, n) { +function oj(r, e, t10, o, n) { let s = e.slice(); s[0] = n; - let a = y.getArrayFromDType(t6, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0]; - return P6(r, e, o, p, a, s), [a, s]; + let a = y.getArrayFromDType(t10, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0]; + return rj(r, e, o, p, a, s), [a, s]; } -function df(r, e, t6, o, n, s, a, i) { +function df(r, e, t10, o, n, s, a, i) { if (r.length === 0) throw new Error("paramsNestedSplits must be non empty"); if (e[0].length === 0) throw new Error("Split tensors must not be scalars"); let p = e[0][0] - 1; - if (R6(s, a, p), o.length === 0) + if (Z6(s, a, p), o.length === 0) throw new Error("params.rank must be nonzero"); - let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = D6(s, a, r, u), d = O6(c), f = M6(t6, o, n, l, m); + let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = ej(s, a, r, u), d = tj(c), f = oj(t10, o, n, l, m); return [d, f[0], f[1]]; } -var kT = 2147483647; -function ff(r, e, t6, o, n, s, a) { +var AT = 2147483647; +function ff(r, e, t10, o, n, s, a) { if (e.length > 1) throw new Error("starts must be a scalar or vector"); if (n.length > 1) @@ -12103,11 +12092,11 @@ function ff(r, e, t6, o, n, s, a) { let w; if (C > 0 && b < x || C < 0 && b > x) w = 0; - else if (w = Math.ceil(Math.abs((b - x) / C)), w > kT) - throw new Error(`Requires ((limit - start) / delta) <= ${kT}`); + else if (w = Math.ceil(Math.abs((b - x) / C)), w > AT) + throw new Error(`Requires ((limit - start) / delta) <= ${AT}`); m[g + 1] = m[g] + w; } - let d = m[l], f = y.getArrayFromDType(t6, d), h = 0; + let d = m[l], f = y.getArrayFromDType(t10, d), h = 0; for (let g = 0; g < l; ++g) { let x = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g]; for (let w = 0; w < x; ++w) @@ -12115,79 +12104,79 @@ function ff(r, e, t6, o, n, s, a) { } return [m, f]; } -var ko = S.RowPartitionType; -var Xp = class { - constructor(e, t6, o, n, s, a, i, p, u, c) { - this.shape = e, this.shapeShape = t6, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = S.getRowPartitionTypesHelper(c), this.raggedRank = S.getRaggedRank(this.rowPartitionTypes); +var No = S.RowPartitionType; +var Yp = class { + constructor(e, t10, o, n, s, a, i, p, u, c) { + this.shape = e, this.shapeShape = t10, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = S.getRowPartitionTypesHelper(c), this.raggedRank = S.getRaggedRank(this.rowPartitionTypes); } getRowPartitionTypeByDimension(e) { - return this.rowPartitionTypes[0] === ko.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e]; + return this.rowPartitionTypes[0] === No.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e]; } getRowPartitionTensor(e) { - return this.rowPartitionTypes[0] === ko.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e]; + return this.rowPartitionTypes[0] === No.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e]; } getMaxWidth(e) { - let t6 = this.getRowPartitionTensor(e - 1); + let t10 = this.getRowPartitionTensor(e - 1); switch (this.getRowPartitionTypeByDimension(e - 1)) { - case ko.VALUE_ROWIDS: - return Xp.getMaxWidthValueRowID(t6); - case ko.ROW_SPLITS: - return Xp.getMaxWidthRowSplit(t6); + case No.VALUE_ROWIDS: + return Yp.getMaxWidthValueRowID(t10); + case No.ROW_SPLITS: + return Yp.getMaxWidthRowSplit(t10); default: - throw new Error(`Cannot handle partition type ${ko[this.getRowPartitionTypeByDimension(e - 1)]}`); + throw new Error(`Cannot handle partition type ${No[this.getRowPartitionTypeByDimension(e - 1)]}`); } } static getMaxWidthRowSplit(e) { - let t6 = e.length; - if (t6 === 0 || t6 === 1) + let t10 = e.length; + if (t10 === 0 || t10 === 1) return 0; let o = 0; - for (let n = 0; n < t6 - 1; ++n) { + for (let n = 0; n < t10 - 1; ++n) { let s = e[n + 1] - e[n]; s > o && (o = s); } return o; } static getMaxWidthValueRowID(e) { - let t6 = e.length; - if (t6 === 0) + let t10 = e.length; + if (t10 === 0) return 0; let o = 0, n = e[0], s = 0; - for (let a = 1; a < t6; ++a) { + for (let a = 1; a < t10; ++a) { let i = e[a]; i !== n && (n = i, s = Math.max(a - o, s), o = a); } - return Math.max(t6 - o, s); + return Math.max(t10 - o, s); } - tensorShapeFromTensor(e, t6, o = true) { - if (t6.length === 0) { + tensorShapeFromTensor(e, t10, o = true) { + if (t10.length === 0) { if (e[0] === -1) return []; throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1."); } - return TT(e, o); + return DT(e, o); } calculateOutputSize(e) { - let t6 = this.valuesShape, o = this.defaultValueShape; - S.validateDefaultValueShape(o, t6); - let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = S.combineRaggedTensorToTensorShapes(this.raggedRank, n, t6); + let t10 = this.valuesShape, o = this.defaultValueShape; + S.validateDefaultValueShape(o, t10); + let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = S.combineRaggedTensorToTensorShapes(this.raggedRank, n, t10); a[0] < 0 && (a[0] = e); for (let i = 1; i <= this.raggedRank; ++i) a[i] < 0 && (a[i] = this.getMaxWidth(i)); return a; } - calculateFirstParentOutputIndex(e, t6, o) { + calculateFirstParentOutputIndex(e, t10, o) { let n = Math.min(e, o), s = [], a = 0; - for (let i = 0; i < n; ++i, a += t6) + for (let i = 0; i < n; ++i, a += t10) s.push(a); for (let i = n; i < e; ++i) s.push(-1); return y.assert(s.length === e, () => "Final length of result must be equal to firstDimension."), s; } - calculateOutputIndexRowSplit(e, t6, o, n) { + calculateOutputIndexRowSplit(e, t10, o, n) { let s = e.length, a = []; for (let i = 0; i < s - 1; ++i) { - let p = e[i + 1] - e[i], u = Math.min(n, p), c = t6[i]; + let p = e[i + 1] - e[i], u = Math.min(n, p), c = t10[i]; c === -1 && (u = 0); for (let l = 0; l < u; ++l) a.push(c), c += o; @@ -12198,23 +12187,23 @@ var Xp = class { throw new Error("Invalid row split size."); return a; } - calculateOutputIndexValueRowID(e, t6, o, n) { + calculateOutputIndexValueRowID(e, t10, o, n) { let s = e.length, a = []; if (s === 0) return []; let i = 0, p = e[0]; - if (p >= t6.length) - throw new Error(`Got currentValueRowId=${p}, which is not less than ${t6.length}`); - let u = t6[p]; + if (p >= t10.length) + throw new Error(`Got currentValueRowId=${p}, which is not less than ${t10.length}`); + let u = t10[p]; a.push(u); for (let c = 1; c < s; ++c) { let l = e[c]; if (l === p) u >= 0 && (++i, i < n ? u += o : u = -1); else { - if (i = 0, p = l, l >= t6.length) - throw new Error(`Got nextValueRowId=${l} which is not less than ${t6.length}`); - u = t6[l]; + if (i = 0, p = l, l >= t10.length) + throw new Error(`Got nextValueRowId=${l} which is not less than ${t10.length}`); + u = t10[l]; } a.push(u); } @@ -12222,74 +12211,74 @@ var Xp = class { throw new Error("Invalid row ids."); return a; } - calculateOutputIndex(e, t6, o, n) { + calculateOutputIndex(e, t10, o, n) { let s = this.getRowPartitionTensor(e), a = this.getRowPartitionTypeByDimension(e); switch (a) { - case ko.VALUE_ROWIDS: - return this.calculateOutputIndexValueRowID(s, t6, o, n); - case ko.ROW_SPLITS: - if (s.length - 1 > t6.length) - throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t6.length}`); - return this.calculateOutputIndexRowSplit(s, t6, o, n); + case No.VALUE_ROWIDS: + return this.calculateOutputIndexValueRowID(s, t10, o, n); + case No.ROW_SPLITS: + if (s.length - 1 > t10.length) + throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t10.length}`); + return this.calculateOutputIndexRowSplit(s, t10, o, n); default: - throw new Error(`Unsupported partition type: ${ko[a]}`); + throw new Error(`Unsupported partition type: ${No[a]}`); } } getFirstDimensionSize() { let e = this.rowPartitionValues[0]; if (this.rowPartitionTypes.length === 0) throw new Error("No row_partition_types given."); - let t6 = this.rowPartitionTypes[0]; - switch (t6) { - case ko.FIRST_DIM_SIZE: + let t10 = this.rowPartitionTypes[0]; + switch (t10) { + case No.FIRST_DIM_SIZE: return e[0]; - case ko.VALUE_ROWIDS: + case No.VALUE_ROWIDS: throw new Error("Cannot handle VALUE_ROWIDS in first dimension."); - case ko.ROW_SPLITS: + case No.ROW_SPLITS: return this.rowPartitionValuesShapes[0][0] - 1; default: - throw new Error(`Cannot handle type ${ko[t6]}`); + throw new Error(`Cannot handle type ${No[t10]}`); } } compute() { if (this.rowPartitionValues[0].length <= 0) throw new Error("Invalid first partition input. Tensor requires at least one element."); - let t6 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t6), n = new Array(this.raggedRank + 1); + let t10 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t10), n = new Array(this.raggedRank + 1); n[n.length - 1] = 1; for (let p = n.length - 2; p >= 0; --p) n[p] = n[p + 1] * o[p + 1]; - let s = TT(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); + let s = DT(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); if (n[0] * o[0] > 0) { - let p = this.calculateFirstParentOutputIndex(t6, n[0], o[0]); + let p = this.calculateFirstParentOutputIndex(t10, n[0], o[0]); for (let u = 1; u <= this.raggedRank; ++u) p = this.calculateOutputIndex(u - 1, p, n[u], o[u]); this.setOutput(this.raggedRank, p, a, s); } return [s, a]; } - setOutput(e, t6, o, n) { + setOutput(e, t10, o, n) { if (o.length === 0) return; let s = this.values, a = o, i = n.slice(); i = i.slice(e + 1); - let p = y.sizeFromShape(i), u = t6.length, c = this.defaultValue; + let p = y.sizeFromShape(i), u = t10.length, c = this.defaultValue; if (c.length !== p && c.length !== 1) { let f = this.defaultValueShape; Ee(() => { - let h = z(c, f); - c = Ii(h, i).dataSync(); + let h = W(c, f); + c = $i(h, i).dataSync(); }); } let l = 0, m = 0, d = 0; for (let f = 0; f <= u; ++f) { - let h = f < u ? t6[f] : -1; + let h = f < u ? t10[f] : -1; if (h === d) { ++d; continue; } if (m < d) { let g = s.subarray(l * p), x = a.subarray(m * p), b = (d - m) * p; - NT(x, g, b); + RT(x, g, b); } if (f >= u) { let g = o.length; @@ -12301,18 +12290,18 @@ var Xp = class { else for (; h > d; ) { let g = a.slice(d * p); - NT(g, c, p), ++d; + RT(g, c, p), ++d; } h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1); } } }; -function NT(r, e, t6) { - for (let o = 0; o < t6; o++) +function RT(r, e, t10) { + for (let o = 0; o < t10; o++) r[o] = e[o]; } -function TT(r, e) { - let t6 = []; +function DT(r, e) { + let t10 = []; for (let o of r) { if (o < 0) { if (!e) @@ -12321,30 +12310,30 @@ function TT(r, e) { throw new Error(`Dimension ${o} must be >= -1`); o = -1; } - t6.push(o); + t10.push(o); } - return t6; + return t10; } -function hf(r, e, t6, o, n, s, a, i, p, u) { - return new Xp(r, e, t6, o, n, s, a, i, p, u).compute(); +function hf(r, e, t10, o, n, s, a, i, p, u) { + return new Yp(r, e, t10, o, n, s, a, i, p, u).compute(); } -function Iu(r, e, t6, o) { - let n = r === e, s = r < e && t6 < 0, a = e < r && t6 > 1; +function _u(r, e, t10, o) { + let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1; if (n || s || a) return y.makeZerosTypedArray(0, o); - let i = Math.abs(Math.ceil((e - r) / t6)), p = y.makeZerosTypedArray(i, o); - e < r && t6 === 1 && (t6 = -1), p[0] = r; + let i = Math.abs(Math.ceil((e - r) / t10)), p = y.makeZerosTypedArray(i, o); + e < r && t10 === 1 && (t10 = -1), p[0] = r; for (let u = 1; u < p.length; u++) - p[u] = p[u - 1] + t6; + p[u] = p[u - 1] + t10; return p; } -var hS = vr((r) => 1 / Math.sqrt(r)); -var L6 = vo(Vn, hS); -var _T = { kernelName: Vn, backendName: "cpu", kernelFunc: L6 }; -function Ma(r, e, t6, o, n, s, a, i, p, u) { +var NS = vr((r) => 1 / Math.sqrt(r)); +var nj = ko(zn, NS); +var FT = { kernelName: zn, backendName: "cpu", kernelFunc: nj }; +function Va(r, e, t10, o, n, s, a, i, p, u) { let c = [o / n, n], l = r.values, m = e.values; if (o === 0) - return le(t6, e.dtype); + return le(t10, e.dtype); let d = le(c, e.dtype); typeof p == "string" || typeof p == "number" ? d.values.fill(p) : typeof p == "boolean" && d.values.fill(+p); for (let f = 0; f < s; f++) { @@ -12354,43 +12343,43 @@ function Ma(r, e, t6, o, n, s, a, i, p, u) { h.push(b), g += b * i[x]; } if (g < 0 || g >= o / n) - throw new Error(`Invalid indices: ${h} does not index into ${t6}`); + throw new Error(`Invalid indices: ${h} does not index into ${t10}`); for (let x = 0; x < n; x++) u ? d.values[g * n + x] += m[f * n + x] : d.values[g * n + x] = e.rank === 0 ? m[0] : m[f * n + x]; } return d; } -var ET = vr((r) => 1 / (1 + Math.exp(-r))); -var gS = Ie(Un, (r) => 1 / (1 + Math.exp(-r))); -var $T = { kernelName: Un, backendName: "cpu", kernelFunc: gS }; -function vu(r, e, t6, o, n) { - let s = ut.isSliceContinous(o, e, t6), a = y.sizeFromShape(t6), i = y.computeStrides(o); +var OT = vr((r) => 1 / (1 + Math.exp(-r))); +var TS = we(Gn, (r) => 1 / (1 + Math.exp(-r))); +var PT = { kernelName: Gn, backendName: "cpu", kernelFunc: TS }; +function $u(r, e, t10, o, n) { + let s = ut.isSliceContinous(o, e, t10), a = y.sizeFromShape(t10), i = y.computeStrides(o); if (s) { let l = ut.computeFlatOffset(e, i); return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a); } - let p = n === "string" ? S.fromUint8ToStringArray(r) : r, u = le(o, n, p), c = le(t6, n); + let p = n === "string" ? S.fromUint8ToStringArray(r) : r, u = le(o, n, p), c = le(t10, n); for (let l = 0; l < c.size; ++l) { let m = c.indexToLoc(l), d = m.map((f, h) => f + e[h]); c.set(u.get(...d), ...m); } return n === "string" ? S.fromStringArrayToUint8(c.values) : c.values; } -function No(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o; - K(n, "slice"); +function To(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o; + j(n, "slice"); let [i, p] = ut.parseSliceParams(n, s, a); ut.assertParamsValid(n, i, p); - let u = t6.data.get(n.dataId).values, c = vu(u, i, p, n.shape, n.dtype); - return t6.makeTensorInfo(p, n.dtype, c); + let u = t10.data.get(n.dataId).values, c = $u(u, i, p, n.shape, n.dtype); + return t10.makeTensorInfo(p, n.dtype, c); } -var AT = { kernelName: _s, backendName: "cpu", kernelFunc: No }; -function gf(r, e, t6, o, n, s, a) { +var MT = { kernelName: Es, backendName: "cpu", kernelFunc: To }; +function gf(r, e, t10, o, n, s, a) { let i = e[0], p = s[0], u = new Array(p), c = new Array(i), l = e[1]; if (p === 0) { if (i !== 0) throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i)); - let g = y.getArrayFromDType(t6, 0), x = y.getArrayFromDType(n, 0); + let g = y.getArrayFromDType(t10, 0), x = y.getArrayFromDType(n, 0); return [g, [0, l], x, u, c]; } let m = true, d = 0, f = new Array(p).fill(0); @@ -12413,26 +12402,26 @@ function gf(r, e, t6, o, n, s, a) { c[b] = b; return [g, [i, l], x, u, c]; } else { - let g = f[p - 1], x = y.getArrayFromDType(t6, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0); + let g = f[p - 1], x = y.getArrayFromDType(t10, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0); for (let w = 0; w < i; ++w) { - let k = r[w * l], _ = C[k], $ = (k === 0 ? 0 : f[k - 1]) + _; + let k = r[w * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _; C[k]++; for (let A = 0; A < l; ++A) - x[$ * l + A] = r[w * l + A]; - b[$] = o[w], c[w] = $; + x[E * l + A] = r[w * l + A]; + b[E] = o[w], c[w] = E; } for (let w = 0; w < p; ++w) if (C[w] === 0) { let _ = w === 0 ? 0 : f[w - 1]; x[_ * l + 0] = w; - for (let $ = 1; $ < l; ++$) - x[_ * l + $] = 0; + for (let E = 1; E < l; ++E) + x[_ * l + E] = 0; b[_] = a; } return [x, [g, l], b, u, c]; } } -function xf(r, e, t6, o, n) { +function xf(r, e, t10, o, n) { let s = y.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1; for (let g = 0; g < i; ++g) { let x = n[g]; @@ -12468,7 +12457,7 @@ function xf(r, e, t6, o, n) { for (let g = i - 2; g >= 0; --g) f[g] = f[g + 1] * p[g + 1]; } - let h = y.getArrayFromDType(t6, a * i); + let h = y.getArrayFromDType(t10, a * i); for (let g = 0; g < a; ++g) { let x = 0; for (let b = 0; b < m; ++b) @@ -12478,13 +12467,13 @@ function xf(r, e, t6, o, n) { } return [h, [a, i], p]; } -function Yp(r, e, t6, o, n, s = false, a = 0) { +function Qp(r, e, t10, o, n, s = false, a = 0) { let i = o.length, p = [e[0], r.length / e[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0; if (l < 0) throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let m = e.slice(); m[0] = l; - let d = m.reduce((C, w) => C * w, 1), f = y.getArrayFromDType(t6, d); + let d = m.reduce((C, w) => C * w, 1), f = y.getArrayFromDType(t10, d); if (i === 0) return l > 0 && f.fill(a), [f, m]; if (l <= 0) @@ -12518,39 +12507,39 @@ function Yp(r, e, t6, o, n, s = false, a = 0) { } return x < l && f.fill(a, x * u, l * u), [f, m]; } -var RT = vr((r) => Math.sqrt(r)); -var B6 = Ie(Gn, (r) => Math.sqrt(r)); -var FT = { kernelName: Gn, backendName: "cpu", kernelFunc: B6 }; -var xS = Be((r, e) => { - let t6 = r - e; - return t6 * t6; +var LT = vr((r) => Math.sqrt(r)); +var sj = we(Hn, (r) => Math.sqrt(r)); +var BT = { kernelName: Hn, backendName: "cpu", kernelFunc: sj }; +var _S = Be((r, e) => { + let t10 = r - e; + return t10 * t10; }); -var V6 = Qe(Kn, xS); -var DT = { kernelName: Kn, backendName: "cpu", kernelFunc: V6 }; -function yf(r, e, t6, o) { +var aj = Qe(jn, _S); +var VT = { kernelName: jn, backendName: "cpu", kernelFunc: aj }; +function yf(r, e, t10, o) { let n = le(r, e.dtype); for (let s = 0; s < n.size; s++) { let a = n.indexToLoc(s), i = new Array(a.length); for (let p = 0; p < i.length; p++) - i[p] = a[p] * t6[p] + o[p]; + i[p] = a[p] * t10[p] + o[p]; n.set(e.get(...i), ...a); } return n; } -var yS = class { - constructor(e, t6, o, n, s, a) { - this.separator = y.encodeString(e), this.nGramWidths = t6, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a; +var $S = class { + constructor(e, t10, o, n, s, a) { + this.separator = y.encodeString(e), this.nGramWidths = t10, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a; } getPadWidth(e) { return Math.min(this.padWidth < 0 ? e - 1 : this.padWidth, e - 1); } - getNumNGrams(e, t6) { - let o = this.getPadWidth(t6); - return Math.max(0, e + 2 * o - t6 + 1); + getNumNGrams(e, t10) { + let o = this.getPadWidth(t10); + return Math.max(0, e + 2 * o - t10 + 1); } - createNGrams(e, t6, o, n, s, a) { + createNGrams(e, t10, o, n, s, a) { for (let i = 0; i < s; ++i) { - let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t6 + (u > 0 ? 0 : i - p), d = 0; + let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t10 + (u > 0 ? 0 : i - p), d = 0; d += u * this.leftPad.length; for (let b = 0; b < l; ++b) d += e[m + b].length; @@ -12573,17 +12562,17 @@ var yS = class { } } } - compute(e, t6) { - let o = e.length, n = t6.length; + compute(e, t10) { + let o = e.length, n = t10.length; if (n > 0) { - let p = t6[0]; + let p = t10[0]; if (p !== 0) throw new Error(`First split value must be 0, got ${p}`); for (let u = 1; u < n; ++u) { - let c = t6[u] >= p; - if (c = c && t6[u] <= o, !c) - throw new Error(`Invalid split value ${t6[u]}, must be in [${p}, ${o}]`); - p = t6[u]; + let c = t10[u] >= p; + if (c = c && t10[u] <= o, !c) + throw new Error(`Invalid split value ${t10[u]}, must be in [${p}, ${o}]`); + p = t10[u]; } if (p !== o) throw new Error(`Last split value must be data size. Expected ${o}, got ${p}`); @@ -12597,19 +12586,19 @@ var yS = class { } a[0] = 0; for (let p = 1; p <= s; ++p) { - let u = t6[p] - t6[p - 1], c = 0; + let u = t10[p] - t10[p - 1], c = 0; this.nGramWidths.forEach((l) => { c += this.getNumNGrams(u, l); }), this.preserveShort && u > 0 && c === 0 && (c = 1), a[p] = a[p - 1] + c; } let i = new Array(a[s]); for (let p = 0; p < s; ++p) { - let u = t6[p], c = a[p]; + let u = t10[p], c = a[p]; if (this.nGramWidths.forEach((l) => { - let m = t6[p + 1] - t6[p], d = this.getNumNGrams(m, l); + let m = t10[p + 1] - t10[p], d = this.getNumNGrams(m, l); this.createNGrams(e, u, i, c, d, l), c += d; }), this.preserveShort && c === a[p]) { - let l = t6[p + 1] - t6[p]; + let l = t10[p + 1] - t10[p]; if (l === 0) continue; let m = l + 2 * this.padWidth, d = 1; @@ -12619,10 +12608,10 @@ var yS = class { return [i, a]; } }; -function ku(r, e, t6, o, n, s, a, i) { - return new yS(t6, o, n, s, a, i).compute(r, e); +function Eu(r, e, t10, o, n, s, a, i) { + return new $S(t10, o, n, s, a, i).compute(r, e); } -function z6(r, e, t6, o) { +function ij(r, e, t10, o) { if (!r.length) return; if (e.length === 0) { @@ -12634,23 +12623,23 @@ function z6(r, e, t6, o) { let s = e[0], a = r.indexOf(s); for (; a !== -1; ) { let i = r.subarray(0, a); - (!t6 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s); + (!t10 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s); } - (!t6 || r.length !== 0) && o.push(r); + (!t10 || r.length !== 0) && o.push(r); return; } let n = 0; for (let s = 0; s < r.length + 1; s++) if (s === r.length || e.indexOf(r[s]) !== -1) { let a = r.subarray(n, s); - (!t6 || a.length !== 0) && o.push(a), n = s + 1; + (!t10 || a.length !== 0) && o.push(a), n = s + 1; } } -function Nu(r, e, t6) { +function Au(r, e, t10) { let o = r.length, n = [], s = 0, a = 0, i = new Array(o); for (let m = 0; m < o; ++m) { let d = n.length; - z6(r[m], e, t6, n); + ij(r[m], e, t10, n); let f = n.length - d; i[m] = f, s += f, a = Math.max(a, f); } @@ -12660,21 +12649,21 @@ function Nu(r, e, t6) { p[l * 2] = m, p[l * 2 + 1] = d, u[l] = n[l], ++l; return [p, u, c]; } -function Tu(r, e) { - let t6 = y.getArrayFromDType("int32", r.length); +function Ru(r, e) { + let t10 = y.getArrayFromDType("int32", r.length); for (let o = 0; o < r.length; ++o) - t6[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned(); - return t6; + t10[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned(); + return t10; } -var bS = Be((r, e) => r - e); -var W6 = qp((r, e, t6, o) => ({ real: r - t6, imag: e - o })); -var dl = Qe(Xn, bS, W6); -var OT = { kernelName: Xn, backendName: "cpu", kernelFunc: dl }; +var ES = Be((r, e) => r - e); +var uj = qp((r, e, t10, o) => ({ real: r - t10, imag: e - o })); +var gl = Qe(Yn, ES, uj); +var zT = { kernelName: Yn, backendName: "cpu", kernelFunc: gl }; function bf(r, e) { - let t6 = new Array(r.rank); - for (let n = 0; n < t6.length; n++) - t6[n] = r.shape[n] * e[n]; - let o = le(t6, r.dtype); + let t10 = new Array(r.rank); + for (let n = 0; n < t10.length; n++) + t10[n] = r.shape[n] * e[n]; + let o = le(t10, r.dtype); for (let n = 0; n < o.values.length; ++n) { let s = o.indexToLoc(n), a = new Array(r.rank); for (let p = 0; p < a.length; p++) @@ -12684,47 +12673,47 @@ function bf(r, e) { } return o; } -var fl = (r, e) => { - let t6 = e.value - r.value; - return t6 === 0 ? r.index - e.index : t6; +var xl = (r, e) => { + let t10 = e.value - r.value; + return t10 === 0 ? r.index - e.index : t10; }; -function PT(r, e, t6 = 0, o = r.length - 1) { - for (; o > t6; ) { - if (o - t6 > 600) { - let i = o - t6 + 1, p = e - t6 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t6, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l)); - PT(r, e, m, d); +function WT(r, e, t10 = 0, o = r.length - 1) { + for (; o > t10; ) { + if (o - t10 > 600) { + let i = o - t10 + 1, p = e - t10 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t10, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l)); + WT(r, e, m, d); } - let n = r[e], s = t6, a = o; - for (y.swap(r, t6, e), fl(r[o], n) > 0 && y.swap(r, t6, o); s < a; ) { - for (y.swap(r, s, a), s++, a--; fl(r[s], n) < 0; ) + let n = r[e], s = t10, a = o; + for (y.swap(r, t10, e), xl(r[o], n) > 0 && y.swap(r, t10, o); s < a; ) { + for (y.swap(r, s, a), s++, a--; xl(r[s], n) < 0; ) s = s + 1; - for (; fl(r[a], n) > 0; ) + for (; xl(r[a], n) > 0; ) a = a - 1; } - fl(r[t6], n) === 0 ? y.swap(r, t6, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t6 = a + 1), e <= a && (o = a - 1); + xl(r[t10], n) === 0 ? y.swap(r, t10, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1); } } -function Cf(r, e, t6, o, n) { - let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t6, a * o), u = y.getTypedArrayFromDType("int32", a * o); +function Cf(r, e, t10, o, n) { + let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t10, a * o), u = y.getTypedArrayFromDType("int32", a * o); for (let l = 0; l < a; l++) { let m = l * i, d = r.subarray(m, m + i), f = new Array(d.length); - d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (PT(f, o), f = f.slice(0, o)), n && f.sort(fl); + d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (WT(f, o), f = f.slice(0, o)), n && f.sort(xl); let h = l * o, g = p.subarray(h, h + o), x = u.subarray(h, h + o); for (let b = 0; b < o; b++) g[b] = f[b].value, x[b] = f[b].index; } let c = e.slice(); - return c[c.length - 1] = o, [le(c, t6, p), le(c, "int32", u)]; + return c[c.length - 1] = o, [le(c, t10, p), le(c, "int32", u)]; } -function Sf(r, e, t6, o) { - let n = y.parseAxisParam(e, t6)[0], s = [1, t6[0], 1]; +function Sf(r, e, t10, o) { + let n = y.parseAxisParam(e, t10)[0], s = [1, t10[0], 1]; for (let f = 0; f < n; f++) - s[0] *= t6[f]; - s[1] = t6[n]; - for (let f = n + 1; f < t6.length; f++) - s[2] *= t6[f]; - let a = {}, i = new Int32Array(t6[n]), p = new st(s, o, r), u = [], c = s[0] === 1 && s[2] === 1; - for (let f = 0; f < t6[n]; f++) { + s[0] *= t10[f]; + s[1] = t10[n]; + for (let f = n + 1; f < t10.length; f++) + s[2] *= t10[f]; + let a = {}, i = new Int32Array(t10[n]), p = new st(s, o, r), u = [], c = s[0] === 1 && s[2] === 1; + for (let f = 0; f < t10[n]; f++) { let h; if (c) h = r[f].toString(); @@ -12750,116 +12739,122 @@ function Sf(r, e, t6, o) { for (let x = 0; x < s[2]; x++) m.set(p.get(g, f, x), g, h, x); }); - let d = t6.slice(); + let d = t10.slice(); return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i }; } -var U6 = "4.1.0"; -Ci("cpu", () => new Oi(), 1); -var CS = Ie(en, (r) => r >= 0 ? r : Math.exp(r) - 1); -var MT = { kernelName: en, backendName: "cpu", kernelFunc: CS }; -function SS(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { alpha: s } = o; - K([n], "leakyRelu"); - let a = y.sizeFromShape(n.shape), i = t6.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a); +var pj = "4.2.0"; +Ti("cpu", () => new Pi(), 1); +var AS = we(Jo, (r) => r >= 0 ? r : Math.exp(r) - 1); +var UT = { kernelName: Jo, backendName: "cpu", kernelFunc: AS }; +function RS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o; + j([n], "leakyRelu"); + let a = y.sizeFromShape(n.shape), i = t10.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a); for (let u = 0; u < i.length; u++) p[u] = i[u] < 0 ? s * i[u] : i[u]; - return t6.makeTensorInfo(n.shape, "float32", p); + return t10.makeTensorInfo(n.shape, "float32", p); } -var LT = { kernelName: mn, backendName: "cpu", kernelFunc: SS }; -var G6 = Be((r, e) => r < 0 ? e * r : r); -function wS(r) { - let { inputs: e, backend: t6 } = r, { x: o, alpha: n } = e; - K([o, n], "prelu"); - let s = t6.data.get(o.dataId).values, a = t6.data.get(n.dataId).values, [i, p] = G6(o.shape, n.shape, s, a, "float32"); - return t6.makeTensorInfo(p, "float32", i); +var GT = { kernelName: ln, backendName: "cpu", kernelFunc: RS }; +var cj = Be((r, e) => r < 0 ? e * r : r); +function DS(r) { + let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e; + j([o, n], "prelu"); + let s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, [i, p] = cj(o.shape, n.shape, s, a, "float32"); + return t10.makeTensorInfo(p, "float32", i); } -var BT = { kernelName: Rn, backendName: "cpu", kernelFunc: wS }; -var IS = Ie(On, (r) => Math.max(0, r)); -var VT = { kernelName: On, backendName: "cpu", kernelFunc: IS }; -var vS = Ie(Ln, (r) => Math.min(Math.max(0, r), 6)); -var zT = { kernelName: Ln, backendName: "cpu", kernelFunc: vS }; -function _u(r, e, t6, o, n) { - if (t6 === "linear") - return ar({ inputs: { x: e }, backend: r }); - if (t6 === "relu") - return IS({ inputs: { x: e }, backend: r }); - if (t6 === "elu") - return CS({ inputs: { x: e }, backend: r }); - if (t6 === "relu6") - return vS({ inputs: { x: e }, backend: r }); - if (t6 === "prelu") - return wS({ inputs: { x: e, alpha: o }, backend: r }); - if (t6 === "leakyrelu") - return SS({ inputs: { x: e }, backend: r, attrs: { alpha: n } }); - if (t6 === "sigmoid") - return gS({ inputs: { x: e }, backend: r }); - throw new Error(`Activation ${t6} has not been implemented for the CPU backend.`); +var HT = { kernelName: Rn, backendName: "cpu", kernelFunc: DS }; +var FS = we(On, (r) => Math.max(0, r)); +var KT = { kernelName: On, backendName: "cpu", kernelFunc: FS }; +var OS = we(Ln, (r) => Math.min(Math.max(0, r), 6)); +var qT = { kernelName: Ln, backendName: "cpu", kernelFunc: OS }; +function Du(r, e, t10, o, n) { + if (t10 === "linear") + return ir({ inputs: { x: e }, backend: r }); + if (t10 === "relu") + return FS({ inputs: { x: e }, backend: r }); + if (t10 === "elu") + return AS({ inputs: { x: e }, backend: r }); + if (t10 === "relu6") + return OS({ inputs: { x: e }, backend: r }); + if (t10 === "prelu") + return DS({ inputs: { x: e, alpha: o }, backend: r }); + if (t10 === "leakyrelu") + return RS({ inputs: { x: e }, backend: r, attrs: { alpha: n } }); + if (t10 === "sigmoid") + return TS({ inputs: { x: e }, backend: r }); + throw new Error(`Activation ${t10} has not been implemented for the CPU backend.`); } -function Me(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); - y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t6.incRef(n.dataId); - let u = t6.data.get(n.dataId); +function Le(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); + y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t10.incRef(n.dataId); + let u = t10.data.get(n.dataId); if (u.complexTensorInfos != null) { let c = u.complexTensorInfos.real, l = u.complexTensorInfos.imag; c.shape = i, l.shape = i; } return { dataId: n.dataId, shape: i, dtype: n.dtype }; } -var WT = { kernelName: Ns, backendName: "cpu", kernelFunc: Me }; -function kS(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - K([n, s], "matMul"); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); +var jT = { kernelName: _s, backendName: "cpu", kernelFunc: Le }; +function PS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + j([n, s], "matMul"); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); - let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Me({ inputs: { x: n }, backend: t6, attrs: { shape: w } }), $ = Me({ inputs: { x: s }, backend: t6, attrs: { shape: k } }), A = a ? _.shape[1] : _.shape[2], R = a ? _.shape[2] : _.shape[1], D = i ? $.shape[1] : $.shape[2], P = Math.max(g, x), M = t6.data.get(_.dataId).values, L = t6.data.get($.dataId).values, W = y.computeStrides(_.shape), V = y.computeStrides($.shape), [U, q, H] = a ? [W[0], 1, W[1]] : [W[0], W[1], 1], [j, X, Z] = i ? [1, V[1], V[0]] : [V[1], 1, V[0]], ee = R * D, Y = le([P, R, D], _.dtype), J = Y.values, ie = t6.blockSize; - for (let pe = 0; pe < P; pe++) - for (let he = 0; he < R; he += ie) - for (let we = 0; we < D; we += ie) - for (let ve = 0; ve < A; ve += ie) { - let $e = Math.min(he + ie, R), Le = Math.min(we + ie, D), nt = Math.min(ve + ie, A); - for (let pt = he; pt < $e; pt++) - for (let Oe = we; Oe < Le; Oe++) { - let mt = 0; - for (let at = ve; at < nt; at++) { - let ft = Math.min(pe, g - 1) * U, wt = Math.min(pe, x - 1) * Z, Fr = M[ft + pt * q + at * H], Ot = L[at * j + Oe * X + wt]; - mt += Fr * Ot; + let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Le({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Le({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), A = a ? _.shape[1] : _.shape[2], R = a ? _.shape[2] : _.shape[1], D = i ? E.shape[1] : E.shape[2], P = Math.max(g, x), M = t10.data.get(_.dataId).values, L = t10.data.get(E.dataId).values, V = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, K, H] = a ? [V[0], 1, V[1]] : [V[0], V[1], 1], [q, Z, ee] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], oe = R * D, J = le([P, R, D], _.dtype), te = J.values, ie = t10.blockSize; + for (let ce = 0; ce < P; ce++) { + let ye = ce % g, Ne = ce % x; + for (let Ie = 0; Ie < R; Ie += ie) { + let Ae = Math.min(Ie + ie, R); + for (let Re = 0; Re < D; Re += ie) { + let ot = Math.min(Re + ie, D); + for (let pt = 0; pt < A; pt += ie) { + let ze = Math.min(pt + ie, A); + for (let ct = Ie; ct < Ae; ct++) + for (let at = Re; at < ot; at++) { + let ft = 0; + for (let ht = pt; ht < ze; ht++) { + let Rr = M[ye * U + ct * K + ht * H], Pt = L[ht * q + at * Z + Ne * ee]; + ft += Rr * Pt; } - J[pe * ee + (pt * D + Oe)] += mt; + te[ce * oe + (ct * D + at)] += ft; } } - return t6.disposeIntermediateTensorInfo(_), t6.disposeIntermediateTensorInfo($), t6.makeTensorInfo(C, Y.dtype, Y.values); + } + } + } + return t10.disposeIntermediateTensorInfo(_), t10.disposeIntermediateTensorInfo(E), t10.makeTensorInfo(C, J.dtype, J.values); } -var UT = { kernelName: Wo, backendName: "cpu", kernelFunc: kS }; -function H6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; - m = kS({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t6 }), a && (d = js({ inputs: { a: m, b: a }, backend: t6 }), h.push(m), m = d), c && (f = _u(t6, m, c, i, l), h.push(m), m = f); +var XT = { kernelName: zo, backendName: "cpu", kernelFunc: PS }; +function lj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; + m = PS({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t10 }), a && (d = Xs({ inputs: { a: m, b: a }, backend: t10 }), h.push(m), m = d), c && (f = Du(t10, m, c, i, l), h.push(m), m = f); for (let x of h) - t6.disposeIntermediateTensorInfo(x); + t10.disposeIntermediateTensorInfo(x); return m; } -var GT = { kernelName: fo, backendName: "cpu", kernelFunc: H6 }; -var q6 = Ie(sa, (r) => Math.acos(r)); -var HT = { kernelName: sa, backendName: "cpu", kernelFunc: q6 }; -var K6 = Ie(aa, (r) => Math.acosh(r)); -var qT = { kernelName: aa, backendName: "cpu", kernelFunc: K6 }; -function j6(r) { - let { inputs: e, backend: t6 } = r, o = e; - K(e, "addN"); - let n = o.map((i) => t6.data.get(i.dataId).values), s = le(o[0].shape, o[0].dtype), a = s.values; +var YT = { kernelName: ho, backendName: "cpu", kernelFunc: lj }; +var mj = we(aa, (r) => Math.acos(r)); +var QT = { kernelName: aa, backendName: "cpu", kernelFunc: mj }; +var dj = we(ia, (r) => Math.acosh(r)); +var ZT = { kernelName: ia, backendName: "cpu", kernelFunc: dj }; +function fj(r) { + let { inputs: e, backend: t10 } = r, o = e; + j(e, "addN"); + let n = o.map((i) => t10.data.get(i.dataId).values), s = le(o[0].shape, o[0].dtype), a = s.values; for (let i = 0; i < o.length; i++) { let p = n[i]; for (let u = 0; u < a.length; u++) a[u] += p[u]; } - return t6.makeTensorInfo(s.shape, s.dtype, s.values); + return t10.makeTensorInfo(s.shape, s.dtype, s.values); } -var KT = { kernelName: Mo, backendName: "cpu", kernelFunc: j6 }; -function X6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - K(n, "all"); +var JT = { kernelName: Po, backendName: "cpu", kernelFunc: fj }; +function hj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + j(n, "all"); let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("all", p, c.shape.length); - let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t6.data.get(c.dataId).values; + u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("all", p, c.shape.length); + let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; for (let w = 0; w < d; ++w) { @@ -12868,21 +12863,21 @@ function X6(r) { } f[x] = C; } - u != null && t6.disposeIntermediateTensorInfo(c); - let g = t6.makeTensorInfo(l, c.dtype, f); + u != null && t10.disposeIntermediateTensorInfo(c); + let g = t10.makeTensorInfo(l, c.dtype, f); if (a) { - let x = S.expandShapeToKeepDim(l, i), b = Me({ inputs: { x: g }, backend: t6, attrs: { shape: x } }); - return t6.disposeIntermediateTensorInfo(g), b; + let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); + return t10.disposeIntermediateTensorInfo(g), b; } return g; } -var jT = { kernelName: Lo, backendName: "cpu", kernelFunc: X6 }; -function Y6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - K(n, "any"); +var e2 = { kernelName: Mo, backendName: "cpu", kernelFunc: hj }; +function gj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + j(n, "any"); let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("any", p, c.shape.length); - let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t6.data.get(c.dataId).values; + u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("any", p, c.shape.length); + let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; for (let w = 0; w < d; ++w) { @@ -12891,21 +12886,21 @@ function Y6(r) { } f[x] = C; } - u != null && t6.disposeIntermediateTensorInfo(c); - let g = t6.makeTensorInfo(l, c.dtype, f); + u != null && t10.disposeIntermediateTensorInfo(c); + let g = t10.makeTensorInfo(l, c.dtype, f); if (a) { - let x = S.expandShapeToKeepDim(l, i), b = Me({ inputs: { x: g }, backend: t6, attrs: { shape: x } }); - return t6.disposeIntermediateTensorInfo(g), b; + let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); + return t10.disposeIntermediateTensorInfo(g), b; } return g; } -var XT = { kernelName: Bo, backendName: "cpu", kernelFunc: Y6 }; -function Q6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o; - K(n, "argMax"); +var t2 = { kernelName: Lo, backendName: "cpu", kernelFunc: gj }; +function xj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o; + j(n, "argMax"); let a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMax", a, p.shape.length); - let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t6.data.get(p.dataId).values; + i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMax", a, p.shape.length); + let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { let x = g * f, b = h[x], C = 0; for (let w = 0; w < f; ++w) { @@ -12914,15 +12909,15 @@ function Q6(r) { } d[g] = C; } - return u.forEach((g) => t6.disposeIntermediateTensorInfo(g)), t6.makeTensorInfo(c, "int32", d); + return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d); } -var YT = { kernelName: Vo, backendName: "cpu", kernelFunc: Q6 }; -function Z6(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o; - K(n, "argMin"); +var r2 = { kernelName: Bo, backendName: "cpu", kernelFunc: xj }; +function yj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o; + j(n, "argMin"); let a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMin", a, p.shape.length); - let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t6.data.get(p.dataId).values; + i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMin", a, p.shape.length); + let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { let x = g * f, b = h[x], C = 0; for (let w = 0; w < f; ++w) { @@ -12931,47 +12926,47 @@ function Z6(r) { } d[g] = C; } - return u.forEach((g) => t6.disposeIntermediateTensorInfo(g)), t6.makeTensorInfo(c, "int32", d); + return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d); } -var QT = { kernelName: Za, backendName: "cpu", kernelFunc: Z6 }; -var J6 = Ie(ia, (r) => Math.asin(r)); -var ZT = { kernelName: ia, backendName: "cpu", kernelFunc: J6 }; -var ej = Ie(ua, (r) => Math.asinh(r)); -var JT = { kernelName: ua, backendName: "cpu", kernelFunc: ej }; -var tj = Ie(pa, (r) => Math.atan(r)); -var e2 = { kernelName: pa, backendName: "cpu", kernelFunc: tj }; -var rj = Be((r, e) => Math.atan2(r, e)); -var oj = Qe(la, rj); -var t2 = { kernelName: la, backendName: "cpu", kernelFunc: oj }; -var nj = Ie(ca, (r) => Math.atanh(r)); -var r2 = { kernelName: ca, backendName: "cpu", kernelFunc: nj }; -function Zp(r, e, t6, o, n, s) { - let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = le(n.outShape, t6), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = n.outShape[3]; +var o2 = { kernelName: ei, backendName: "cpu", kernelFunc: yj }; +var bj = we(ua, (r) => Math.asin(r)); +var n2 = { kernelName: ua, backendName: "cpu", kernelFunc: bj }; +var Cj = we(pa, (r) => Math.asinh(r)); +var s2 = { kernelName: pa, backendName: "cpu", kernelFunc: Cj }; +var Sj = we(ca, (r) => Math.atan(r)); +var a2 = { kernelName: ca, backendName: "cpu", kernelFunc: Sj }; +var wj = Be((r, e) => Math.atan2(r, e)); +var Ij = Qe(ma, wj); +var i2 = { kernelName: ma, backendName: "cpu", kernelFunc: Ij }; +var vj = we(la, (r) => Math.atanh(r)); +var u2 = { kernelName: la, backendName: "cpu", kernelFunc: vj }; +function Jp(r, e, t10, o, n, s) { + let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = le(n.outShape, t10), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = n.outShape[3]; for (let w = 0; w < n.batchSize; ++w) { let k = w * x, _ = w * o[0]; - for (let $ = 0; $ < n.inChannels; ++$) + for (let E = 0; E < n.inChannels; ++E) for (let A = 0; A < n.outHeight; ++A) { let R = A * a - m, D = Math.max(0, R), P = Math.min(n.inHeight, c + R), M = k + A * b; for (let L = 0; L < n.outWidth; ++L) { - let W = L * i - d, V = Math.max(0, W), U = Math.min(n.inWidth, l + W), q = f, H = 0, j = 0; - for (let Z = D; Z < P; Z += p) { - let ee = _ + Z * o[1]; - for (let Y = V; Y < U; Y += u) { - let J = ee + Y * o[2], ie = r[J + $]; - s === "max" && ie > q ? q = ie : s === "avg" && (H += ie, j++); + let V = L * i - d, z = Math.max(0, V), U = Math.min(n.inWidth, l + V), K = f, H = 0, q = 0; + for (let ee = D; ee < P; ee += p) { + let oe = _ + ee * o[1]; + for (let J = z; J < U; J += u) { + let te = oe + J * o[2], ie = r[te + E]; + s === "max" && ie > K ? K = ie : s === "avg" && (H += ie, q++); } - if (isNaN(q)) + if (isNaN(K)) break; } - let X = M + L * C + $; - g[X] = s === "avg" ? H / j : q; + let Z = M + L * C + E; + g[Z] = s === "avg" ? H / q : K; } } } return h; } -function wf(r, e, t6, o, n = false, s = false) { - let a = le(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = le(e, t6, r); +function wf(r, e, t10, o, n = false, s = false) { + let a = le(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = le(e, t10, r); for (let g = 0; g < o.batchSize; ++g) for (let x = 0; x < o.inChannels; ++x) for (let b = 0; b < o.outHeight; ++b) { @@ -12980,15 +12975,15 @@ function wf(r, e, t6, o, n = false, s = false) { w += u; let k = Math.min(o.inHeight, l + C); for (let _ = 0; _ < o.outWidth; ++_) { - let $ = _ * p - f, A = $; + let E = _ * p - f, A = E; for (; A < 0; ) A += c; - let R = Math.min(o.inWidth, m + $), D = Number.NEGATIVE_INFINITY, P = -1; + let R = Math.min(o.inWidth, m + E), D = Number.NEGATIVE_INFINITY, P = -1; for (let M = w; M < k; M += u) { let L = M - C; - for (let W = A; W < R; W += c) { - let V = W - $, U = h.get(g, M, W, x); - U > D && (D = U, n ? P = s ? ((g * o.inHeight + M) * o.inWidth + W) * o.inChannels + x : (M * o.inWidth + W) * o.inChannels + x : P = L * m + V); + for (let V = A; V < R; V += c) { + let z = V - E, U = h.get(g, M, V, x); + U > D && (D = U, n ? P = s ? ((g * o.inHeight + M) * o.inWidth + V) * o.inChannels + x : (M * o.inWidth + V) * o.inChannels + x : P = L * m + z); } } a.set(P, g, b, _, x); @@ -12996,51 +12991,51 @@ function wf(r, e, t6, o, n = false, s = false) { } return a; } -function If(r, e, t6, o, n, s) { - let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = le(n.outShape, t6), w = C.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], $ = n.outShape[3] * n.outShape[4], A = n.outShape[4]; +function If(r, e, t10, o, n, s) { + let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = le(n.outShape, t10), w = C.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], A = n.outShape[4]; for (let R = 0; R < n.batchSize; ++R) { let D = R * k, P = R * o[0]; for (let M = 0; M < n.inChannels; ++M) for (let L = 0; L < n.outDepth; ++L) { - let W = L * a - h, V = W; - for (; V < 0; ) - V += u; - let U = Math.min(n.inDepth, m + W), q = D + L * _; + let V = L * a - h, z = V; + for (; z < 0; ) + z += u; + let U = Math.min(n.inDepth, m + V), K = D + L * _; for (let H = 0; H < n.outHeight; ++H) { - let j = H * i - g, X = j; - for (; X < 0; ) - X += c; - let Z = Math.min(n.inHeight, d + j), ee = q + H * $; - for (let Y = 0; Y < n.outWidth; ++Y) { - let J = Y * p - x, ie = J; + let q = H * i - g, Z = q; + for (; Z < 0; ) + Z += c; + let ee = Math.min(n.inHeight, d + q), oe = K + H * E; + for (let J = 0; J < n.outWidth; ++J) { + let te = J * p - x, ie = te; for (; ie < 0; ) ie += l; - let pe = Math.min(n.inWidth, f + J), he = ee + Y * A, we = b, ve = 0, $e = 0; - for (let nt = V; nt < U; nt += u) { - let pt = P + nt * o[1]; - for (let Oe = X; Oe < Z; Oe += c) { - let mt = pt + Oe * o[2]; - for (let at = ie; at < pe; at += l) { - let ft = mt + at * o[3], wt = r[ft + M]; - if (s === "max" && wt > we ? we = wt : s === "avg" && (ve += wt, $e++), isNaN(we)) + let ce = Math.min(n.inWidth, f + te), ye = oe + J * A, Ne = b, Ie = 0, Ae = 0; + for (let ot = z; ot < U; ot += u) { + let pt = P + ot * o[1]; + for (let ze = Z; ze < ee; ze += c) { + let ct = pt + ze * o[2]; + for (let at = ie; at < ce; at += l) { + let ft = ct + at * o[3], ht = r[ft + M]; + if (s === "max" && ht > Ne ? Ne = ht : s === "avg" && (Ie += ht, Ae++), isNaN(Ne)) break; } - if (isNaN(we)) + if (isNaN(Ne)) break; } - if (isNaN(we)) + if (isNaN(Ne)) break; } - let Le = he + M; - w[Le] = s === "avg" ? ve / $e : we; + let Re = ye + M; + w[Re] = s === "avg" ? Ie / Math.max(Ae, 1) : Ne; } } } } return C; } -function o2(r, e) { - let t6 = le(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left; +function p2(r, e) { + let t10 = le(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left; for (let h = 0; h < e.batchSize; ++h) for (let g = 0; g < e.inChannels; ++g) for (let x = 0; x < e.outDepth; ++x) { @@ -13049,298 +13044,298 @@ function o2(r, e) { C += a; let w = Math.min(e.inDepth, u + b); for (let k = 0; k < e.outHeight; ++k) { - let _ = k * n - d, $ = _; - for (; $ < 0; ) - $ += i; + let _ = k * n - d, E = _; + for (; E < 0; ) + E += i; let A = Math.min(e.inHeight, c + _); for (let R = 0; R < e.outWidth; ++R) { let D = R * s - f, P = D; for (; P < 0; ) P += p; - let M = Math.min(e.inWidth, l + D), L = Number.NEGATIVE_INFINITY, W = -1; - for (let V = C; V < w; V += a) { - let U = V - b; - for (let q = $; q < A; q += i) { - let H = q - _; - for (let j = P; j < M; j += p) { - let X = j - D, Z = r.get(h, V, q, j, g); - Z >= L && (L = Z, W = U * c * l + H * c + X); + let M = Math.min(e.inWidth, l + D), L = Number.NEGATIVE_INFINITY, V = -1; + for (let z = C; z < w; z += a) { + let U = z - b; + for (let K = E; K < A; K += i) { + let H = K - _; + for (let q = P; q < M; q += p) { + let Z = q - D, ee = r.get(h, z, K, q, g); + ee >= L && (L = ee, V = U * c * l + H * c + Z); } } } - t6.set(W, h, x, k, R, g); + t10.set(V, h, x, k, R, g); } } } - return t6; + return t10; } -function sj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e; - K(n, "avgPool"); +function kj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; + j(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = S.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = ar({ inputs: { x: n }, backend: t6 }); + l = ir({ inputs: { x: n }, backend: t10 }); else { - let m = t6.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Zp(m, n.shape, n.dtype, d, c, "avg"); - l = t6.makeTensorInfo(c.outShape, n.dtype, f.values); + let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Jp(m, n.shape, n.dtype, d, c, "avg"); + l = t10.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var n2 = { kernelName: zo, backendName: "cpu", kernelFunc: sj }; -function aj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; - K(n, "avgPool3d"); - let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t6.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); - return t6.makeTensorInfo(m.shape, "float32", m.values); +var c2 = { kernelName: Vo, backendName: "cpu", kernelFunc: kj }; +function Nj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; + j(n, "avgPool3d"); + let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); + return t10.makeTensorInfo(m.shape, "float32", m.values); } -var s2 = { kernelName: ip, backendName: "cpu", kernelFunc: aj }; -function ij(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; - K([n, s], "avgPool3DGrad"); - let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, w = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, $ = w - 1 - c.padInfo.front, A = _ - 1 - c.padInfo.left, R = k - 1 - c.padInfo.top, D = le(s.shape, "float32"), P = 1 / (f * h * g), M = t6.bufferSync(n); +var l2 = { kernelName: dp, backendName: "cpu", kernelFunc: Nj }; +function Tj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; + j([n, s], "avgPool3DGrad"); + let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, w = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = w - 1 - c.padInfo.front, A = _ - 1 - c.padInfo.left, R = k - 1 - c.padInfo.top, D = le(s.shape, "float32"), P = 1 / (f * h * g), M = t10.bufferSync(n); for (let L = 0; L < c.batchSize; ++L) - for (let W = 0; W < c.inChannels; ++W) - for (let V = 0; V < c.inDepth; ++V) + for (let V = 0; V < c.inChannels; ++V) + for (let z = 0; z < c.inDepth; ++z) for (let U = 0; U < c.inHeight; ++U) - for (let q = 0; q < c.inWidth; ++q) { - let H = V - $, j = U - R, X = q - A, Z = 0; - for (let ee = 0; ee < w; ee += x) { - let Y = (H + ee) / l; - if (!(Y < 0 || Y >= c.outDepth || Math.floor(Y) !== Y)) - for (let J = 0; J < k; J += b) { - let ie = (j + J) / m; + for (let K = 0; K < c.inWidth; ++K) { + let H = z - E, q = U - R, Z = K - A, ee = 0; + for (let oe = 0; oe < w; oe += x) { + let J = (H + oe) / l; + if (!(J < 0 || J >= c.outDepth || Math.floor(J) !== J)) + for (let te = 0; te < k; te += b) { + let ie = (q + te) / m; if (!(ie < 0 || ie >= c.outHeight || Math.floor(ie) !== ie)) - for (let pe = 0; pe < _; pe += C) { - let he = (X + pe) / d; - if (he < 0 || he >= c.outWidth || Math.floor(he) !== he) + for (let ce = 0; ce < _; ce += C) { + let ye = (Z + ce) / d; + if (ye < 0 || ye >= c.outWidth || Math.floor(ye) !== ye) continue; - let we = M.get(L, Y, ie, he, W); - Z += we; + let Ne = M.get(L, J, ie, ye, V); + ee += Ne; } } } - D.set(Z * P, L, V, U, q, W); + D.set(ee * P, L, z, U, K, V); } - return t6.makeTensorInfo(D.shape, D.dtype, D.values); + return t10.makeTensorInfo(D.shape, D.dtype, D.values); } -var a2 = { kernelName: Im, backendName: "cpu", kernelFunc: ij }; -function uj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, a = s; - K([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, w = x - 1 - c.padInfo.top, k = le(a.shape, "float32"), _ = 1 / (d * f), $ = t6.data.get(n.dataId).values, A = le(n.shape, "float32", $); +var m2 = { kernelName: vm, backendName: "cpu", kernelFunc: Tj }; +function _j(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; + j([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, w = x - 1 - c.padInfo.top, k = le(a.shape, "float32"), _ = 1 / (d * f), E = t10.data.get(n.dataId).values, A = le(n.shape, "float32", E); for (let R = 0; R < c.batchSize; ++R) for (let D = 0; D < c.inChannels; ++D) for (let P = 0; P < c.inHeight; ++P) for (let M = 0; M < c.inWidth; ++M) { - let L = P - w, W = M - C, V = 0; + let L = P - w, V = M - C, z = 0; for (let U = 0; U < x; U += h) { - let q = (L + U) / l; - if (!(q < 0 || q >= c.outHeight || Math.floor(q) !== q)) + let K = (L + U) / l; + if (!(K < 0 || K >= c.outHeight || Math.floor(K) !== K)) for (let H = 0; H < b; H += g) { - let j = (W + H) / m; - if (j < 0 || j >= c.outWidth || Math.floor(j) !== j) + let q = (V + H) / m; + if (q < 0 || q >= c.outWidth || Math.floor(q) !== q) continue; - let X = A.get(R, q, j, D); - V += X; + let Z = A.get(R, K, q, D); + z += Z; } } - k.set(V * _, R, P, M, D); + k.set(z * _, R, P, M, D); } - return t6.makeTensorInfo(k.shape, k.dtype, k.values); + return t10.makeTensorInfo(k.shape, k.dtype, k.values); } -var i2 = { kernelName: wm, backendName: "cpu", kernelFunc: uj }; -function pj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e; - y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), K([n, i, p, s, a], "batchNorm"); +var d2 = { kernelName: mp, backendName: "cpu", kernelFunc: _j }; +function $j(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e; + y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), j([n, i, p, s, a], "batchNorm"); let { varianceEpsilon: u } = o; u == null && (u = 1e-3); - let c = t6.data.get(n.dataId).values, l = t6.data.get(i.dataId).values, m = t6.data.get(p.dataId).values, d = s ? t6.data.get(s.dataId).values : new Float32Array([1]), f = a ? t6.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, C = l.length, w = 0, k = 0, _ = 0, $ = 0; + let c = t10.data.get(n.dataId).values, l = t10.data.get(i.dataId).values, m = t10.data.get(p.dataId).values, d = s ? t10.data.get(s.dataId).values : new Float32Array([1]), f = a ? t10.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, C = l.length, w = 0, k = 0, _ = 0, E = 0; for (let A = 0; A < c.length; ++A) - h[A] = f[w++] + (c[A] - l[k++]) * d[_++] / Math.sqrt(m[$++] + u), w >= g && (w = 0), k >= C && (k = 0), _ >= x && (_ = 0), $ >= b && ($ = 0); - return t6.makeTensorInfo(n.shape, n.dtype, h); + h[A] = f[w++] + (c[A] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), w >= g && (w = 0), k >= C && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0); + return t10.makeTensorInfo(n.shape, n.dtype, h); } -var u2 = { kernelName: an, backendName: "cpu", kernelFunc: pj }; -function cj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; - K([n], "batchToSpaceND"); - let i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Me({ inputs: { x: n }, backend: t6, attrs: { shape: p } }), f = Ct({ inputs: { x: d }, backend: t6, attrs: { perm: u } }), h = Me({ inputs: { x: f }, backend: t6, attrs: { shape: c } }), g = No({ inputs: { x: h }, backend: t6, attrs: { begin: l, size: m } }); - return t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(f), t6.disposeIntermediateTensorInfo(h), g; +var f2 = { kernelName: sn, backendName: "cpu", kernelFunc: $j }; +function Ej(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; + j([n], "batchToSpaceND"); + let i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Le({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = St({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Le({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = To({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } }); + return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g; } -var p2 = { kernelName: xs, backendName: "cpu", kernelFunc: cj }; -function lj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t6.data.get(n.dataId).values, p = t6.data.get(s.dataId).values, u = Kp(i, p, s.dtype, s.shape, a); - return t6.makeTensorInfo([a], s.dtype, u); +var h2 = { kernelName: bs, backendName: "cpu", kernelFunc: Ej }; +function Aj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = jp(i, p, s.dtype, s.shape, a); + return t10.makeTensorInfo([a], s.dtype, u); } -var c2 = { kernelName: Ja, backendName: "cpu", kernelFunc: lj }; -function mj(r) { - let { inputs: e, backend: t6 } = r, { s0: o, s1: n } = e, s = t6.data.get(o.dataId).values, a = t6.data.get(n.dataId).values, i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); - return t6.makeTensorInfo([i.length], "int32", Int32Array.from(i)); +var g2 = { kernelName: ti, backendName: "cpu", kernelFunc: Aj }; +function Rj(r) { + let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); + return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var l2 = { kernelName: up, backendName: "cpu", kernelFunc: mj }; -var dj = Ie(lo, (r, e) => { - let t6 = e; - return r > t6.clipValueMax ? t6.clipValueMax : r < t6.clipValueMin ? t6.clipValueMin : r; +var x2 = { kernelName: fp, backendName: "cpu", kernelFunc: Rj }; +var Dj = we(lo, (r, e) => { + let t10 = e; + return r > t10.clipValueMax ? t10.clipValueMax : r < t10.clipValueMin ? t10.clipValueMin : r; }); -var m2 = { kernelName: lo, backendName: "cpu", kernelFunc: dj }; -var fj = (r) => { - let { x: e } = r.inputs, t6 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t6.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t6.data.get(s.dataId).values, p = t6.data.get(a.dataId).values; +var y2 = { kernelName: lo, backendName: "cpu", kernelFunc: Dj }; +var Fj = (r) => { + let { x: e } = r.inputs, t10 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values; for (let u = 0; u < i.length; u++) { let c = i[u], l = p[u]; o[u] = Math.hypot(c, l); } - return t6.makeOutput(o, e.shape, "float32"); + return t10.makeOutput(o, e.shape, "float32"); }; -var d2 = { kernelName: pp, backendName: "cpu", kernelFunc: fj }; -function Xs(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.data.get(o.dataId).complexTensorInfos.imag, s = t6.data.get(n.dataId).values; - return t6.makeTensorInfo(n.shape, n.dtype, s); +var b2 = { kernelName: hp, backendName: "cpu", kernelFunc: Fj }; +function Ys(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.imag, s = t10.data.get(n.dataId).values; + return t10.makeTensorInfo(n.shape, n.dtype, s); } -var f2 = { kernelName: si, backendName: "cpu", kernelFunc: Xs }; -function Pi(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape); +var C2 = { kernelName: ci, backendName: "cpu", kernelFunc: Ys }; +function Mi(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape); S.assertParamsConsistent(a, s); let i = S.computeOutShape(e.map((h) => h.shape), s); if (y.sizeFromShape(i) === 0) - return t6.makeTensorInfo(i, e[0].dtype, []); + return t10.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((h) => y.sizeFromShape(h.shape) > 0); if (p.length === 1) - return ar({ inputs: { x: p[0] }, backend: t6 }); + return ir({ inputs: { x: p[0] }, backend: t10 }); if (p[0].dtype === "complex64") { - let h = p.map((w) => wo({ inputs: { input: w }, backend: t6 })), g = p.map((w) => Xs({ inputs: { input: w }, backend: t6 })), x = Pi({ inputs: h, backend: t6, attrs: { axis: s } }), b = Pi({ inputs: g, backend: t6, attrs: { axis: s } }), C = Ht({ inputs: { real: x, imag: b }, backend: t6 }); - return h.forEach((w) => t6.disposeIntermediateTensorInfo(w)), g.forEach((w) => t6.disposeIntermediateTensorInfo(w)), t6.disposeIntermediateTensorInfo(x), t6.disposeIntermediateTensorInfo(b), C; + let h = p.map((w) => Io({ inputs: { input: w }, backend: t10 })), g = p.map((w) => Ys({ inputs: { input: w }, backend: t10 })), x = Mi({ inputs: h, backend: t10, attrs: { axis: s } }), b = Mi({ inputs: g, backend: t10, attrs: { axis: s } }), C = Ht({ inputs: { real: x, imag: b }, backend: t10 }); + return h.forEach((w) => t10.disposeIntermediateTensorInfo(w)), g.forEach((w) => t10.disposeIntermediateTensorInfo(w)), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), C; } let u = p.map((h) => { let x = [-1, y.sizeFromShape(h.shape.slice(s))]; - return Me({ inputs: { x: h }, backend: t6, attrs: { shape: x } }); - }), c = u.map((h) => ({ vals: t6.data.get(h.dataId).values, shape: h.shape })); + return Le({ inputs: { x: h }, backend: t10, attrs: { shape: x } }); + }), c = u.map((h) => ({ vals: t10.data.get(h.dataId).values, shape: h.shape })); i = S.computeOutShape(u.map((h) => h.shape), 1); - let l = u[0].shape[0] === 1, m = Su(c, i, e[0].dtype, l), d = S.computeOutShape(p.map((h) => h.shape), s), f = t6.makeTensorInfo(d, e[0].dtype, m); - return u.forEach((h) => t6.disposeIntermediateTensorInfo(h)), f; + let l = u[0].shape[0] === 1, m = Nu(c, i, e[0].dtype, l), d = S.computeOutShape(p.map((h) => h.shape), s), f = t10.makeTensorInfo(d, e[0].dtype, m); + return u.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f; } -var h2 = { kernelName: ys, backendName: "cpu", kernelFunc: Pi }; -function NS(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; - K([n, s], "conv2d"); - let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", w = new st(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), $ = k[0], A = C ? k[1] : k[2], R = C ? k[2] : 1, D = C ? 1 : k[1], P = w.strides[0], M = C ? w.strides[1] : w.strides[2], L = C ? w.strides[2] : 1, W = C ? 1 : w.strides[1], V = t6.data.get(n.dataId).values, U = t6.data.get(s.dataId).values, q = w.values; +var S2 = { kernelName: Cs, backendName: "cpu", kernelFunc: Mi }; +function MS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; + j([n, s], "conv2d"); + let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", w = new st(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], A = C ? k[1] : k[2], R = C ? k[2] : 1, D = C ? 1 : k[1], P = w.strides[0], M = C ? w.strides[1] : w.strides[2], L = C ? w.strides[2] : 1, V = C ? 1 : w.strides[1], z = t10.data.get(n.dataId).values, U = t10.data.get(s.dataId).values, K = w.values; for (let H = 0; H < m.batchSize; ++H) { - let j = H * $, X = H * P; - for (let Z = 0; Z < m.outHeight; ++Z) { - let ee = X + Z * M, Y = Z * m.strideHeight - b; - for (let J = 0; J < d; ++J) { - let ie = Y + J * h; + let q = H * E, Z = H * P; + for (let ee = 0; ee < m.outHeight; ++ee) { + let oe = Z + ee * M, J = ee * m.strideHeight - b; + for (let te = 0; te < d; ++te) { + let ie = J + te * h; if (ie < 0 || ie >= m.inHeight) continue; - let pe = J * _[0], he = j + ie * A; - for (let we = 0; we < m.outWidth; ++we) { - let ve = ee + we * L, $e = we * m.strideWidth - x; - for (let Le = 0; Le < f; ++Le) { - let nt = $e + Le * g; - if (nt < 0 || nt >= m.inWidth) + let ce = te * _[0], ye = q + ie * A; + for (let Ne = 0; Ne < m.outWidth; ++Ne) { + let Ie = oe + Ne * L, Ae = Ne * m.strideWidth - x; + for (let Re = 0; Re < f; ++Re) { + let ot = Ae + Re * g; + if (ot < 0 || ot >= m.inWidth) continue; - let pt = pe + Le * _[1], Oe = he + nt * R, mt = pt; + let pt = ce + Re * _[1], ze = ye + ot * R, ct = pt; for (let at = 0; at < m.inChannels; ++at) { - let ft = V[Oe + at * D]; - for (let wt = 0; wt < m.outChannels; ++wt) - q[ve + wt * W] += ft * U[mt + wt]; - mt += m.outChannels; + let ft = z[ze + at * D]; + for (let ht = 0; ht < m.outChannels; ++ht) + K[Ie + ht * V] += ft * U[ct + ht]; + ct += m.outChannels; } } } } } } - return t6.makeTensorInfo(w.shape, w.dtype, q); + return t10.makeTensorInfo(w.shape, w.dtype, K); } -var g2 = { kernelName: Go, backendName: "cpu", kernelFunc: NS }; -function hj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; - K([n, s], "conv2dBackpropFilter"); - let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new st(m.filterShape, "float32"), C = m.padInfo.left, w = m.padInfo.top, k = t6.data.get(n.dataId).values, _ = t6.data.get(s.dataId).values, $ = new st(n.shape, n.dtype, k), A = new st(s.shape, s.dtype, _); +var w2 = { kernelName: Uo, backendName: "cpu", kernelFunc: MS }; +function Oj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; + j([n, s], "conv2dBackpropFilter"); + let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new st(m.filterShape, "float32"), C = m.padInfo.left, w = m.padInfo.top, k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = new st(n.shape, n.dtype, k), A = new st(s.shape, s.dtype, _); for (let R = 0; R < h; ++R) { let D = Math.max(0, Math.ceil((w - R) / d)), P = Math.min(m.outHeight, (m.inHeight + w - R) / d); for (let M = 0; M < g; ++M) { - let L = Math.max(0, Math.ceil((C - M) / f)), W = Math.min(m.outWidth, (m.inWidth + C - M) / f); - for (let V = 0; V < m.inChannels; ++V) + let L = Math.max(0, Math.ceil((C - M) / f)), V = Math.min(m.outWidth, (m.inWidth + C - M) / f); + for (let z = 0; z < m.inChannels; ++z) for (let U = 0; U < m.outChannels; ++U) { - let q = 0; + let K = 0; for (let H = 0; H < m.batchSize; ++H) - for (let j = D; j < P; ++j) { - let X = R + j * d - w; - for (let Z = L; Z < W; ++Z) { - let ee = M + Z * f - C; - x ? q += $.get(H, X, ee, V) * A.get(H, j, Z, U) : q += $.get(H, V, X, ee) * A.get(H, U, j, Z); + for (let q = D; q < P; ++q) { + let Z = R + q * d - w; + for (let ee = L; ee < V; ++ee) { + let oe = M + ee * f - C; + x ? K += E.get(H, Z, oe, z) * A.get(H, q, ee, U) : K += E.get(H, z, Z, oe) * A.get(H, U, q, ee); } } - b.set(q, R, M, V, U); + b.set(K, R, M, z, U); } } } - return t6.makeTensorInfo(b.shape, b.dtype, b.values); + return t10.makeTensorInfo(b.shape, b.dtype, b.values); } -var x2 = { kernelName: cp, backendName: "cpu", kernelFunc: hj }; -function gj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; - K([n, s], "conv2dBackpropInput"); - let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = S.convertConv2DDataFormat(u), f = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new st(f.inShape, "float32"), g = h.values, x = t6.data.get(n.dataId).values, b = t6.data.get(s.dataId).values, [C, w, k] = l, { batchSize: _, filterHeight: $, filterWidth: A, inChannels: R, inHeight: D, inWidth: P, outChannels: M, outHeight: L, outWidth: W, strideHeight: V, strideWidth: U } = f; +var I2 = { kernelName: oi, backendName: "cpu", kernelFunc: Oj }; +function Pj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; + j([n, s], "conv2dBackpropInput"); + let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = S.convertConv2DDataFormat(u), f = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new st(f.inShape, "float32"), g = h.values, x = t10.data.get(n.dataId).values, b = t10.data.get(s.dataId).values, [C, w, k] = l, { batchSize: _, filterHeight: E, filterWidth: A, inChannels: R, inHeight: D, inWidth: P, outChannels: M, outHeight: L, outWidth: V, strideHeight: z, strideWidth: U } = f; d = f.dataFormat; - let q = $ - 1 - f.padInfo.top, H = A - 1 - f.padInfo.left, j = d === "channelsLast", X = h.strides[0], Z = j ? h.strides[1] : h.strides[2], ee = j ? h.strides[2] : 1, Y = j ? 1 : h.strides[1], J = m[0], ie = j ? m[1] : m[2], pe = j ? m[2] : 1, he = j ? 1 : m[1]; - for (let we = 0; we < _; ++we) - for (let ve = 0; ve < R; ++ve) - for (let $e = 0; $e < D; ++$e) { - let Le = $e - q, nt = Math.max(0, Math.ceil(Le / V)), pt = Math.min(L, ($ + Le) / V); - for (let Oe = 0; Oe < P; ++Oe) { - let mt = Oe - H, at = Math.max(0, Math.ceil(mt / U)), ft = Math.min(W, (A + mt) / U), wt = 0; - for (let Ot = nt; Ot < pt; ++Ot) { - let Kr = Ot * V - Le; + let K = E - 1 - f.padInfo.top, H = A - 1 - f.padInfo.left, q = d === "channelsLast", Z = h.strides[0], ee = q ? h.strides[1] : h.strides[2], oe = q ? h.strides[2] : 1, J = q ? 1 : h.strides[1], te = m[0], ie = q ? m[1] : m[2], ce = q ? m[2] : 1, ye = q ? 1 : m[1]; + for (let Ne = 0; Ne < _; ++Ne) + for (let Ie = 0; Ie < R; ++Ie) + for (let Ae = 0; Ae < D; ++Ae) { + let Re = Ae - K, ot = Math.max(0, Math.ceil(Re / z)), pt = Math.min(L, (E + Re) / z); + for (let ze = 0; ze < P; ++ze) { + let ct = ze - H, at = Math.max(0, Math.ceil(ct / U)), ft = Math.min(V, (A + ct) / U), ht = 0; + for (let Pt = ot; Pt < pt; ++Pt) { + let qr = Pt * z - Re; for (let er = at; er < ft; ++er) { - let Nt = er * U - mt, tr = J * we + ie * Ot + pe * er, rr = C * ($ - 1 - Kr) + w * (A - 1 - Nt) + k * ve; + let Nt = er * U - ct, tr = te * Ne + ie * Pt + ce * er, rr = C * (E - 1 - qr) + w * (A - 1 - Nt) + k * Ie; for (let jr = 0; jr < M; ++jr) { - let Xr = x[tr + he * jr], pr = b[rr + jr]; - wt += Xr * pr; + let Xr = x[tr + ye * jr], cr = b[rr + jr]; + ht += Xr * cr; } } } - let Fr = X * we + Z * $e + ee * Oe + Y * ve; - g[Fr] = wt; + let Rr = Z * Ne + ee * Ae + oe * ze + J * Ie; + g[Rr] = ht; } } - return t6.makeTensorInfo(h.shape, h.dtype, h.values); + return t10.makeTensorInfo(h.shape, h.dtype, h.values); } -var y2 = { kernelName: Ho, backendName: "cpu", kernelFunc: gj }; -function xj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; - K([n, s], "conv3d"); - let u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, C = g.top, w = new st(u.outShape, n.dtype), k = t6.data.get(n.dataId).values, _ = t6.data.get(s.dataId).values, $ = w.values, A = y.computeStrides(n.shape), R = y.computeStrides(s.shape); +var v2 = { kernelName: Go, backendName: "cpu", kernelFunc: Pj }; +function Mj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o; + j([n, s], "conv3d"); + let u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, C = g.top, w = new st(u.outShape, n.dtype), k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = w.values, A = y.computeStrides(n.shape), R = y.computeStrides(s.shape); for (let D = 0; D < u.batchSize; ++D) { let P = D * A[0], M = D * w.strides[0]; for (let L = 0; L < u.outDepth; ++L) { - let W = M + L * w.strides[1], V = L * u.strideDepth - x; + let V = M + L * w.strides[1], z = L * u.strideDepth - x; for (let U = 0; U < c; ++U) { - let q = V + U * d; - if (q < 0 || q >= u.inDepth) + let K = z + U * d; + if (K < 0 || K >= u.inDepth) continue; - let H = U * R[0], j = P + q * A[1]; - for (let X = 0; X < u.outHeight; ++X) { - let Z = W + X * w.strides[2], ee = X * u.strideHeight - C; - for (let Y = 0; Y < l; ++Y) { - let J = ee + Y * f; - if (J < 0 || J >= u.inHeight) + let H = U * R[0], q = P + K * A[1]; + for (let Z = 0; Z < u.outHeight; ++Z) { + let ee = V + Z * w.strides[2], oe = Z * u.strideHeight - C; + for (let J = 0; J < l; ++J) { + let te = oe + J * f; + if (te < 0 || te >= u.inHeight) continue; - let ie = H + Y * R[1], pe = j + J * A[2]; - for (let he = 0; he < u.outWidth; ++he) { - let we = Z + he * u.outChannels, ve = he * u.strideWidth - b; - for (let $e = 0; $e < m; ++$e) { - let Le = ve + $e * h; - if (Le < 0 || Le >= u.inWidth) + let ie = H + J * R[1], ce = q + te * A[2]; + for (let ye = 0; ye < u.outWidth; ++ye) { + let Ne = ee + ye * u.outChannels, Ie = ye * u.strideWidth - b; + for (let Ae = 0; Ae < m; ++Ae) { + let Re = Ie + Ae * h; + if (Re < 0 || Re >= u.inWidth) continue; - let nt = ie + $e * R[2], pt = pe + Le * u.inChannels, Oe = nt; - for (let mt = 0; mt < u.inChannels; ++mt) { - let at = k[pt + mt]; + let ot = ie + Ae * R[2], pt = ce + Re * u.inChannels, ze = ot; + for (let ct = 0; ct < u.inChannels; ++ct) { + let at = k[pt + ct]; for (let ft = 0; ft < u.outChannels; ++ft) - $[we + ft] += at * _[Oe + ft]; - Oe += u.outChannels; + E[Ne + ft] += at * _[ze + ft]; + ze += u.outChannels; } } } @@ -13349,151 +13344,151 @@ function xj(r) { } } } - return t6.makeTensorInfo(w.shape, w.dtype, w.values); + return t10.makeTensorInfo(w.shape, w.dtype, w.values); } -var b2 = { kernelName: lp, backendName: "cpu", kernelFunc: xj }; -function yj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; - K([n, s], "conv3dBackpropFilterV2"); - let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new st(l.filterShape, "float32"), C = b.values, [w, k, _, $] = b.strides, A = t6.data.get(s.dataId).values, [R, D, P, M] = c, L = t6.data.get(n.dataId).values, [W, V, U, q] = u, H = l.padInfo.front, j = l.padInfo.left, X = l.padInfo.top; - for (let Z = 0; Z < h; ++Z) { - let ee = Math.max(0, Math.ceil((H - Z) / m)), Y = Math.min(l.outDepth, (l.inDepth + H - Z) / m), J = Z * w; +var k2 = { kernelName: gp, backendName: "cpu", kernelFunc: Mj }; +function Lj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o; + j([n, s], "conv3dBackpropFilterV2"); + let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new st(l.filterShape, "float32"), C = b.values, [w, k, _, E] = b.strides, A = t10.data.get(s.dataId).values, [R, D, P, M] = c, L = t10.data.get(n.dataId).values, [V, z, U, K] = u, H = l.padInfo.front, q = l.padInfo.left, Z = l.padInfo.top; + for (let ee = 0; ee < h; ++ee) { + let oe = Math.max(0, Math.ceil((H - ee) / m)), J = Math.min(l.outDepth, (l.inDepth + H - ee) / m), te = ee * w; for (let ie = 0; ie < g; ++ie) { - let pe = Math.max(0, Math.ceil((X - ie) / d)), he = Math.min(l.outHeight, (l.inHeight + X - ie) / d), we = ie * k + J; - for (let ve = 0; ve < x; ++ve) { - let $e = Math.max(0, Math.ceil((j - ve) / f)), Le = Math.min(l.outWidth, (l.inWidth + j - ve) / f), nt = ve * _ + we; + let ce = Math.max(0, Math.ceil((Z - ie) / d)), ye = Math.min(l.outHeight, (l.inHeight + Z - ie) / d), Ne = ie * k + te; + for (let Ie = 0; Ie < x; ++Ie) { + let Ae = Math.max(0, Math.ceil((q - Ie) / f)), Re = Math.min(l.outWidth, (l.inWidth + q - Ie) / f), ot = Ie * _ + Ne; for (let pt = 0; pt < l.inChannels; ++pt) { - let Oe = pt * $ + nt; - for (let mt = 0; mt < l.outChannels; ++mt) { + let ze = pt * E + ot; + for (let ct = 0; ct < l.outChannels; ++ct) { let at = 0; for (let ft = 0; ft < l.batchSize; ++ft) { - let wt = ft * W, Fr = ft * R; - for (let Ot = ee; Ot < Y; ++Ot) { - let er = (Z + Ot * m - H) * V + wt, Nt = Ot * D + Fr; - for (let tr = pe; tr < he; ++tr) { - let jr = (ie + tr * d - X) * U + er, Xr = tr * P + Nt; - for (let pr = $e; pr < Le; ++pr) { - let Fo = (ve + pr * f - j) * q + jr, Ka = pr * M + Xr; - at += L[Fo + pt] * A[Ka + mt]; + let ht = ft * V, Rr = ft * R; + for (let Pt = oe; Pt < J; ++Pt) { + let er = (ee + Pt * m - H) * z + ht, Nt = Pt * D + Rr; + for (let tr = ce; tr < ye; ++tr) { + let jr = (ie + tr * d - Z) * U + er, Xr = tr * P + Nt; + for (let cr = Ae; cr < Re; ++cr) { + let Do = (Ie + cr * f - q) * K + jr, hs = cr * M + Xr; + at += L[Do + pt] * A[hs + ct]; } } } } - C[Oe + mt] = at; + C[ze + ct] = at; } } } } } - return t6.makeTensorInfo(b.shape, b.dtype, b.values); + return t10.makeTensorInfo(b.shape, b.dtype, b.values); } -var C2 = { kernelName: vm, backendName: "cpu", kernelFunc: yj }; -function bj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; - K([n], "conv3dBackpropInputV2"); - let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(p, s.shape, i, 1, a), m = new st(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t6.data.get(n.dataId).values, [C, w, k, _] = u, $ = t6.data.get(s.dataId).values, [A, R, D, P] = c, { batchSize: M, filterDepth: L, filterHeight: W, filterWidth: V, inChannels: U, inDepth: q, inHeight: H, inWidth: j, outChannels: X, outDepth: Z, outHeight: ee, outWidth: Y, strideDepth: J, strideHeight: ie, strideWidth: pe } = l, he = L - 1 - l.padInfo.front, we = W - 1 - l.padInfo.top, ve = V - 1 - l.padInfo.left; - for (let $e = 0; $e < M; ++$e) - for (let Le = 0; Le < U; ++Le) - for (let nt = 0; nt < q; ++nt) { - let pt = nt - he, Oe = Math.max(0, Math.ceil(pt / J)), mt = Math.min(Z, (L + pt) / J); +var N2 = { kernelName: km, backendName: "cpu", kernelFunc: Lj }; +function Bj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o; + j([n], "conv3dBackpropInputV2"); + let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(p, s.shape, i, 1, a), m = new st(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t10.data.get(n.dataId).values, [C, w, k, _] = u, E = t10.data.get(s.dataId).values, [A, R, D, P] = c, { batchSize: M, filterDepth: L, filterHeight: V, filterWidth: z, inChannels: U, inDepth: K, inHeight: H, inWidth: q, outChannels: Z, outDepth: ee, outHeight: oe, outWidth: J, strideDepth: te, strideHeight: ie, strideWidth: ce } = l, ye = L - 1 - l.padInfo.front, Ne = V - 1 - l.padInfo.top, Ie = z - 1 - l.padInfo.left; + for (let Ae = 0; Ae < M; ++Ae) + for (let Re = 0; Re < U; ++Re) + for (let ot = 0; ot < K; ++ot) { + let pt = ot - ye, ze = Math.max(0, Math.ceil(pt / te)), ct = Math.min(ee, (L + pt) / te); for (let at = 0; at < H; ++at) { - let ft = at - we, wt = Math.max(0, Math.ceil(ft / ie)), Fr = Math.min(ee, (W + ft) / ie); - for (let Ot = 0; Ot < j; ++Ot) { - let Kr = Ot - ve, er = Math.max(0, Math.ceil(Kr / pe)), Nt = Math.min(Y, (V + Kr) / pe), tr = 0; - for (let rr = Oe; rr < mt; ++rr) { - let jr = rr * J - pt; - for (let Xr = wt; Xr < Fr; ++Xr) { - let pr = Xr * ie - ft; - for (let Js = er; Js < Nt; ++Js) { - let Fo = Js * pe - Kr, Ka = C * $e + w * rr + k * Xr + _ * Js, Kt = A * (L - 1 - jr) + R * (W - 1 - pr) + D * (V - 1 - Fo) + P * Le; - for (let ea = 0; ea < X; ++ea) { - let Ac = b[Ka + ea], Rc = $[Kt + ea]; - tr += Ac * Rc; + let ft = at - Ne, ht = Math.max(0, Math.ceil(ft / ie)), Rr = Math.min(oe, (V + ft) / ie); + for (let Pt = 0; Pt < q; ++Pt) { + let qr = Pt - Ie, er = Math.max(0, Math.ceil(qr / ce)), Nt = Math.min(J, (z + qr) / ce), tr = 0; + for (let rr = ze; rr < ct; ++rr) { + let jr = rr * te - pt; + for (let Xr = ht; Xr < Rr; ++Xr) { + let cr = Xr * ie - ft; + for (let ea = er; ea < Nt; ++ea) { + let Do = ea * ce - qr, hs = C * Ae + w * rr + k * Xr + _ * ea, qt = A * (L - 1 - jr) + R * (V - 1 - cr) + D * (z - 1 - Do) + P * Re; + for (let ta = 0; ta < Z; ++ta) { + let Dc = b[hs + ta], Fc = E[qt + ta]; + tr += Dc * Fc; } } } } - d[f * $e + h * nt + g * at + x * Ot + Le] = tr; + d[f * Ae + h * ot + g * at + x * Pt + Re] = tr; } } } - return t6.makeTensorInfo(m.shape, m.dtype, m.values); + return t10.makeTensorInfo(m.shape, m.dtype, m.values); } -var S2 = { kernelName: mp, backendName: "cpu", kernelFunc: bj }; -var Cj = Ie(qo, (r) => Math.cos(r)); -var w2 = { kernelName: qo, backendName: "cpu", kernelFunc: Cj }; -var Sj = Ie(Ko, (r) => Math.cosh(r)); -var I2 = { kernelName: Ko, backendName: "cpu", kernelFunc: Sj }; -function wj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = le([f, h, g, d], "float32"), b = t6.data.get(s.dataId).values, C = t6.data.get(a.dataId).values, w = t6.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); - for (let $ = 0; $ < f; $++) { - let A = $ * 4, R = b[A], D = b[A + 1], P = b[A + 2], M = b[A + 3], L = C[$]; +var T2 = { kernelName: xp, backendName: "cpu", kernelFunc: Bj }; +var Vj = we(Ho, (r) => Math.cos(r)); +var _2 = { kernelName: Ho, backendName: "cpu", kernelFunc: Vj }; +var zj = we(Ko, (r) => Math.cosh(r)); +var $2 = { kernelName: Ko, backendName: "cpu", kernelFunc: zj }; +function Wj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = le([f, h, g, d], "float32"), b = t10.data.get(s.dataId).values, C = t10.data.get(a.dataId).values, w = t10.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); + for (let E = 0; E < f; E++) { + let A = E * 4, R = b[A], D = b[A + 1], P = b[A + 2], M = b[A + 3], L = C[E]; if (L >= c) continue; - let W = h > 1 ? (P - R) * (l - 1) / (h - 1) : 0, V = g > 1 ? (M - D) * (m - 1) / (g - 1) : 0; + let V = h > 1 ? (P - R) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - D) * (m - 1) / (g - 1) : 0; for (let U = 0; U < h; U++) { - let q = h > 1 ? R * (l - 1) + U * W : 0.5 * (R + P) * (l - 1); - if (q < 0 || q > l - 1) { + let K = h > 1 ? R * (l - 1) + U * V : 0.5 * (R + P) * (l - 1); + if (K < 0 || K > l - 1) { for (let H = 0; H < g; H++) - for (let j = 0; j < d; j++) { - let X = j + H * _[2] + U * _[1] + $ * _[0]; - x.values[X] = u; + for (let q = 0; q < d; q++) { + let Z = q + H * _[2] + U * _[1] + E * _[0]; + x.values[Z] = u; } continue; } if (p === "bilinear") { - let H = Math.floor(q), j = Math.ceil(q), X = q - H; - for (let Z = 0; Z < g; Z++) { - let ee = g > 1 ? D * (m - 1) + Z * V : 0.5 * (D + M) * (m - 1); - if (ee < 0 || ee > m - 1) { - for (let pe = 0; pe < d; pe++) { - let he = pe + Z * _[2] + U * _[1] + $ * _[0]; - x.values[he] = u; + let H = Math.floor(K), q = Math.ceil(K), Z = K - H; + for (let ee = 0; ee < g; ee++) { + let oe = g > 1 ? D * (m - 1) + ee * z : 0.5 * (D + M) * (m - 1); + if (oe < 0 || oe > m - 1) { + for (let ce = 0; ce < d; ce++) { + let ye = ce + ee * _[2] + U * _[1] + E * _[0]; + x.values[ye] = u; } continue; } - let Y = Math.floor(ee), J = Math.ceil(ee), ie = ee - Y; - for (let pe = 0; pe < d; pe++) { - let he = pe + Y * k[2] + H * k[1] + L * k[0], we = w[he]; - he = pe + J * k[2] + H * k[1] + L * k[0]; - let ve = w[he]; - he = pe + Y * k[2] + j * k[1] + L * k[0]; - let $e = w[he]; - he = pe + J * k[2] + j * k[1] + L * k[0]; - let Le = w[he], nt = we + (ve - we) * ie, pt = $e + (Le - $e) * ie; - he = pe + Z * _[2] + U * _[1] + $ * _[0], x.values[he] = nt + (pt - nt) * X; + let J = Math.floor(oe), te = Math.ceil(oe), ie = oe - J; + for (let ce = 0; ce < d; ce++) { + let ye = ce + J * k[2] + H * k[1] + L * k[0], Ne = w[ye]; + ye = ce + te * k[2] + H * k[1] + L * k[0]; + let Ie = w[ye]; + ye = ce + J * k[2] + q * k[1] + L * k[0]; + let Ae = w[ye]; + ye = ce + te * k[2] + q * k[1] + L * k[0]; + let Re = w[ye], ot = Ne + (Ie - Ne) * ie, pt = Ae + (Re - Ae) * ie; + ye = ce + ee * _[2] + U * _[1] + E * _[0], x.values[ye] = ot + (pt - ot) * Z; } } } else for (let H = 0; H < g; ++H) { - let j = g > 1 ? D * (m - 1) + H * V : 0.5 * (D + M) * (m - 1); - if (j < 0 || j > m - 1) { - for (let ee = 0; ee < d; ee++) { - let Y = ee + H * _[2] + U * _[1] + $ * _[0]; - x.values[Y] = u; + let q = g > 1 ? D * (m - 1) + H * z : 0.5 * (D + M) * (m - 1); + if (q < 0 || q > m - 1) { + for (let oe = 0; oe < d; oe++) { + let J = oe + H * _[2] + U * _[1] + E * _[0]; + x.values[J] = u; } continue; } - let X = Math.round(j), Z = Math.round(q); - for (let ee = 0; ee < d; ee++) { - let Y = ee + X * k[2] + Z * k[1] + L * k[0], J = ee + H * _[2] + U * _[1] + $ * _[0]; - x.values[J] = w[Y]; + let Z = Math.round(q), ee = Math.round(K); + for (let oe = 0; oe < d; oe++) { + let J = oe + Z * k[2] + ee * k[1] + L * k[0], te = oe + H * _[2] + U * _[1] + E * _[0]; + x.values[te] = w[J]; } } } } - return t6.makeTensorInfo(x.shape, x.dtype, x.values); + return t10.makeTensorInfo(x.shape, x.dtype, x.values); } -var v2 = { kernelName: Yo, backendName: "cpu", kernelFunc: wj }; -function Ij(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - K(n, "cumprod"); +var E2 = { kernelName: Xo, backendName: "cpu", kernelFunc: Wj }; +function Uj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + j(n, "cumprod"); let p = S.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: p } })); + p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } })); let c = S.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); - let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t6.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; + let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; for (let x = 0; x < d.length; x += f) for (let b = 0; b < f; b++) { let C = h(x, b); @@ -13504,23 +13499,23 @@ function Ij(r) { m[C] = a ? d[w] * m[w] : d[C] * m[w]; } } - let g = t6.makeTensorInfo(u.shape, l, m); + let g = t10.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = S.getUndoAxesPermutation(p), b = Ct({ inputs: { x: g }, backend: t6, attrs: { perm: x } }); - return t6.disposeIntermediateTensorInfo(g), t6.disposeIntermediateTensorInfo(u), b; + let x = S.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } }); + return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b; } return g; } -var k2 = { kernelName: jo, backendName: "cpu", kernelFunc: Ij }; -function vj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - K(n, "cumsum"); +var A2 = { kernelName: qo, backendName: "cpu", kernelFunc: Uj }; +function Gj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + j(n, "cumsum"); let p = S.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: p } })); + p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } })); let c = S.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); - let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t6.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; + let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b; for (let x = 0; x < d.length; x += f) for (let b = 0; b < f; b++) { let C = h(x, b); @@ -13531,356 +13526,356 @@ function vj(r) { m[C] = a ? d[w] + m[w] : d[C] + m[w]; } } - let g = t6.makeTensorInfo(u.shape, l, m); + let g = t10.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = S.getUndoAxesPermutation(p), b = Ct({ inputs: { x: g }, backend: t6, attrs: { perm: x } }); - return t6.disposeIntermediateTensorInfo(g), t6.disposeIntermediateTensorInfo(u), b; + let x = S.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } }); + return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b; } return g; } -var N2 = { kernelName: Xo, backendName: "cpu", kernelFunc: vj }; -function kj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; +var R2 = { kernelName: jo, backendName: "cpu", kernelFunc: Gj }; +function Hj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = t6.data.get(n.dataId).values, u = t6.data.get(s.dataId).values, c = Kp(p, u, s.dtype, s.shape, a); - return t6.makeTensorInfo([a], s.dtype, c); + let p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = jp(p, u, s.dtype, s.shape, a); + return t10.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = t6.bufferSync(n), u = t6.bufferSync(s), c = uf(p, u, a, i); - return t6.makeTensorInfo(c.shape, s.dtype, c.values); + let p = t10.bufferSync(n), u = t10.bufferSync(s), c = uf(p, u, a, i); + return t10.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var T2 = { kernelName: ti, backendName: "cpu", kernelFunc: kj }; -function Nj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o; +var D2 = { kernelName: ni, backendName: "cpu", kernelFunc: Hj }; +function Kj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o; y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`); - let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t6.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0; + let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t10.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0; for (let x = 0; x < i; ++x) for (let b = 0; b < l; ++b) { let C = Math.floor(b / s), w = b % s; for (let k = 0; k < m; ++k) { - let _ = Math.floor(k / s), $ = k % s, A = (w * s + $) * d; + let _ = Math.floor(k / s), E = k % s, A = (w * s + E) * d; for (let R = 0; R < d; ++R) { let P = R + A + c * (_ + u * (C + p * x)); h[g++] = f[P]; } } } - return t6.makeTensorInfo([i, l, m, d], n.dtype, h); + return t10.makeTensorInfo([i, l, m, d], n.dtype, h); } -var _2 = { kernelName: Qo, backendName: "cpu", kernelFunc: Nj }; -function TS(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; - K([n, s], "depthwiseConv2DNative"); +var F2 = { kernelName: Yo, backendName: "cpu", kernelFunc: Kj }; +function LS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; + j([n, s], "depthwiseConv2DNative"); let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p; m == null && (m = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`); - let d = S.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, C = b.left, w = b.top, k = d.outChannels / d.inChannels, _ = new st(d.outShape, n.dtype), $ = t6.data.get(n.dataId).values, A = t6.data.get(s.dataId).values, R = _.values; + let d = S.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, C = b.left, w = b.top, k = d.outChannels / d.inChannels, _ = new st(d.outShape, n.dtype), E = t10.data.get(n.dataId).values, A = t10.data.get(s.dataId).values, R = _.values; for (let D = 0; D < d.batchSize; ++D) { let P = D * c[0], M = D * _.strides[0]; for (let L = 0; L < d.outHeight; ++L) { - let W = M + L * _.strides[1], V = L * d.strideHeight - w; + let V = M + L * _.strides[1], z = L * d.strideHeight - w; for (let U = 0; U < f; ++U) { - let q = V + U * g; - if (q < 0 || q >= d.inHeight) + let K = z + U * g; + if (K < 0 || K >= d.inHeight) continue; - let H = U * l[0], j = P + q * c[1]; - for (let X = 0; X < d.outWidth; ++X) { - let Z = W + X * _.strides[2], ee = X * d.strideWidth - C; - for (let Y = 0; Y < h; ++Y) { - let J = ee + Y * x; - if (J < 0 || J >= d.inWidth) + let H = U * l[0], q = P + K * c[1]; + for (let Z = 0; Z < d.outWidth; ++Z) { + let ee = V + Z * _.strides[2], oe = Z * d.strideWidth - C; + for (let J = 0; J < h; ++J) { + let te = oe + J * x; + if (te < 0 || te >= d.inWidth) continue; - let ie = H + Y * l[1], pe = j + J * d.inChannels, he = Z, we = ie; - for (let ve = 0; ve < d.inChannels; ++ve) { - let $e = $[pe + ve]; - for (let Le = 0; Le < k; ++Le) - R[he + Le] += $e * A[we + Le]; - he += k, we += k; + let ie = H + J * l[1], ce = q + te * d.inChannels, ye = ee, Ne = ie; + for (let Ie = 0; Ie < d.inChannels; ++Ie) { + let Ae = E[ce + Ie]; + for (let Re = 0; Re < k; ++Re) + R[ye + Re] += Ae * A[Ne + Re]; + ye += k, Ne += k; } } } } } } - return t6.makeTensorInfo(_.shape, _.dtype, _.values); + return t10.makeTensorInfo(_.shape, _.dtype, _.values); } -var E2 = { kernelName: Zo, backendName: "cpu", kernelFunc: TS }; -function Tj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; - K([n, s], "depthwiseConv2dNativeBackpropFilter"); - let l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new st(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, w = t6.data.get(n.dataId).values, k = new st(n.shape, n.dtype, w), _ = t6.data.get(s.dataId).values, $ = new st(s.shape, s.dtype, _); +var O2 = { kernelName: Qo, backendName: "cpu", kernelFunc: LS }; +function qj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; + j([n, s], "depthwiseConv2dNativeBackpropFilter"); + let l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new st(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, w = t10.data.get(n.dataId).values, k = new st(n.shape, n.dtype, w), _ = t10.data.get(s.dataId).values, E = new st(s.shape, s.dtype, _); for (let A = 0; A < f; ++A) { let R = Math.max(0, Math.ceil((b - A) / m)), D = Math.min(l.outHeight, (l.inHeight + b - A) / m); for (let P = 0; P < h; ++P) { let M = Math.max(0, Math.ceil((x - P) / d)), L = Math.min(l.outWidth, (l.inWidth + x - P) / d); - for (let W = 0; W < l.outChannels; ++W) { - let V = Math.trunc(W / C), U = W % C, q = 0; + for (let V = 0; V < l.outChannels; ++V) { + let z = Math.trunc(V / C), U = V % C, K = 0; for (let H = 0; H < l.batchSize; ++H) - for (let j = R; j < D; ++j) { - let X = A + j * m - b; - for (let Z = M; Z < L; ++Z) { - let ee = P + Z * d - x; - q += k.get(H, X, ee, V) * $.get(H, j, Z, W); + for (let q = R; q < D; ++q) { + let Z = A + q * m - b; + for (let ee = M; ee < L; ++ee) { + let oe = P + ee * d - x; + K += k.get(H, Z, oe, z) * E.get(H, q, ee, V); } } - g.set(q, A, P, V, U); + g.set(K, A, P, z, U); } } } - return t6.makeTensorInfo(g.shape, g.dtype, g.values); + return t10.makeTensorInfo(g.shape, g.dtype, g.values); } -var $2 = { kernelName: dp, backendName: "cpu", kernelFunc: Tj }; -function _j(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; - K([n, s], "depthwiseConv2DNativeBackpropInput"); - let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new st(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = t6.data.get(n.dataId).values, [w, k, _] = l, $ = t6.data.get(s.dataId).values, [A, R, D] = m, { batchSize: P, filterHeight: M, filterWidth: L, inChannels: W, inHeight: V, inWidth: U, outChannels: q, outHeight: H, outWidth: j, strideHeight: X, strideWidth: Z } = d, ee = M - 1 - d.padInfo.top, Y = L - 1 - d.padInfo.left, J = q / W; +var P2 = { kernelName: yp, backendName: "cpu", kernelFunc: qj }; +function jj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; + j([n, s], "depthwiseConv2DNativeBackpropInput"); + let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new st(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = t10.data.get(n.dataId).values, [w, k, _] = l, E = t10.data.get(s.dataId).values, [A, R, D] = m, { batchSize: P, filterHeight: M, filterWidth: L, inChannels: V, inHeight: z, inWidth: U, outChannels: K, outHeight: H, outWidth: q, strideHeight: Z, strideWidth: ee } = d, oe = M - 1 - d.padInfo.top, J = L - 1 - d.padInfo.left, te = K / V; for (let ie = 0; ie < P; ++ie) - for (let pe = 0; pe < W; ++pe) - for (let he = 0; he < V; ++he) { - let we = he - ee, ve = Math.max(0, Math.ceil(we / X)), $e = Math.min(H, (M + we) / X); - for (let Le = 0; Le < U; ++Le) { - let nt = Le - Y, pt = Math.max(0, Math.ceil(nt / Z)), Oe = Math.min(j, (L + nt) / Z), mt = 0; - for (let at = ve; at < $e; ++at) { - let ft = at * X - we; - for (let wt = pt; wt < Oe; ++wt) { - let Fr = wt * Z - nt, Ot = w * ie + k * at + _ * wt, Kr = A * (M - 1 - ft) + R * (L - 1 - Fr) + D * pe; - for (let er = 0; er < J; ++er) { - let Nt = pe * J + er, tr = C[Ot + Nt], rr = $[Kr + er]; - mt += tr * rr; + for (let ce = 0; ce < V; ++ce) + for (let ye = 0; ye < z; ++ye) { + let Ne = ye - oe, Ie = Math.max(0, Math.ceil(Ne / Z)), Ae = Math.min(H, (M + Ne) / Z); + for (let Re = 0; Re < U; ++Re) { + let ot = Re - J, pt = Math.max(0, Math.ceil(ot / ee)), ze = Math.min(q, (L + ot) / ee), ct = 0; + for (let at = Ie; at < Ae; ++at) { + let ft = at * Z - Ne; + for (let ht = pt; ht < ze; ++ht) { + let Rr = ht * ee - ot, Pt = w * ie + k * at + _ * ht, qr = A * (M - 1 - ft) + R * (L - 1 - Rr) + D * ce; + for (let er = 0; er < te; ++er) { + let Nt = ce * te + er, tr = C[Pt + Nt], rr = E[qr + er]; + ct += tr * rr; } } } - h[g * ie + x * he + b * Le + pe] = mt; + h[g * ie + x * ye + b * Re + ce] = ct; } } - return t6.makeTensorInfo(f.shape, f.dtype, f.values); + return t10.makeTensorInfo(f.shape, f.dtype, f.values); } -var A2 = { kernelName: fp, backendName: "cpu", kernelFunc: _j }; -function Ej(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t6.data.get(o.dataId).values, a = le([n, n], o.dtype), i = a.values; +var M2 = { kernelName: bp, backendName: "cpu", kernelFunc: jj }; +function Xj(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.data.get(o.dataId).values, a = le([n, n], o.dtype), i = a.values; for (let u = 0; u < s.length; u++) i[u * n + u] = s[u]; let p = [...o.shape, ...o.shape]; - return t6.makeTensorInfo(p, a.dtype, a.values); + return t10.makeTensorInfo(p, a.dtype, a.values); } -var R2 = { kernelName: hp, backendName: "cpu", kernelFunc: Ej }; -var F2 = { kernelName: gp, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t6 }) => { - let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t6, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: w, strideWidth: k, filterHeight: _, filterWidth: $, dilationHeight: A, dilationWidth: R, outShape: D } = S.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), P = y.sizeFromShape(D), M = D.length, L = y.getArrayFromDType(o.dtype, P); - for (let V = 0; V < d; ++V) +var L2 = { kernelName: si, backendName: "cpu", kernelFunc: Xj }; +var B2 = { kernelName: ai, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { + let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t10, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: w, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: A, dilationWidth: R, outShape: D } = S.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), P = y.sizeFromShape(D), M = D.length, L = y.getArrayFromDType(o.dtype, P); + for (let z = 0; z < d; ++z) for (let U = 0; U < x; ++U) { - let q = U * w - C.top; + let K = U * w - C.top; for (let H = 0; H < b; ++H) { - let j = H * k - C.left; - for (let X = 0; X < g; ++X) { - let Z = Number.MIN_SAFE_INTEGER; - for (let Y = 0; Y < _; ++Y) { - let J = q + Y * A; - if (J >= 0 && J < f) - for (let ie = 0; ie < $; ++ie) { - let pe = j + ie * R; - if (pe >= 0 && pe < h) { - let he = y.locToIndex([V, J, pe, X], c, y.computeStrides(o.shape)), we = y.locToIndex([Y, ie, X], m, y.computeStrides(n.shape)), ve = u[he] + l[we]; - ve > Z && (Z = ve); + let q = H * k - C.left; + for (let Z = 0; Z < g; ++Z) { + let ee = Number.MIN_SAFE_INTEGER; + for (let J = 0; J < _; ++J) { + let te = K + J * A; + if (te >= 0 && te < f) + for (let ie = 0; ie < E; ++ie) { + let ce = q + ie * R; + if (ce >= 0 && ce < h) { + let ye = y.locToIndex([z, te, ce, Z], c, y.computeStrides(o.shape)), Ne = y.locToIndex([J, ie, Z], m, y.computeStrides(n.shape)), Ie = u[ye] + l[Ne]; + Ie > ee && (ee = Ie); } } } - let ee = y.locToIndex([V, U, H, X], M, y.computeStrides(D)); - L[ee] = Z; + let oe = y.locToIndex([z, U, H, Z], M, y.computeStrides(D)); + L[oe] = ee; } } } return { dataId: p.write(y.toTypedArray(L, o.dtype), D, o.dtype), shape: D, dtype: o.dtype }; } }; -var D2 = { kernelName: bb, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t6 }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t6, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: $, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === R.length, () => `Error in ${bb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`); +var V2 = { kernelName: $b, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { + let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === R.length, () => `Error in ${$b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`); let D = y.toNestedArray(R, u.data.get(s.dataId).values), P = y.makeZerosNestedTypedArray(n.shape, n.dtype); for (let L = 0; L < m; ++L) - for (let W = 0; W < g; ++W) { - let V = W * C - b.top; + for (let V = 0; V < g; ++V) { + let z = V * C - b.top; for (let U = 0; U < x; ++U) { - let q = U * w - b.left; + let K = U * w - b.left; for (let H = 0; H < h; ++H) { - let j = Number.MIN_SAFE_INTEGER, X = 0, Z = 0; - for (let ee = 0; ee < k; ++ee) { - let Y = V + ee * $; - if (Y >= 0 && Y < d) - for (let J = 0; J < _; ++J) { - let ie = q + J * A; + let q = Number.MIN_SAFE_INTEGER, Z = 0, ee = 0; + for (let oe = 0; oe < k; ++oe) { + let J = z + oe * E; + if (J >= 0 && J < d) + for (let te = 0; te < _; ++te) { + let ie = K + te * A; if (ie >= 0 && ie < f) { - let pe = c[L][Y][ie][H] + l[ee][J][H]; - pe > j && (j = pe, X = ee, Z = J); + let ce = c[L][J][ie][H] + l[oe][te][H]; + ce > q && (q = ce, Z = oe, ee = te); } } } - P[X][Z][H] += D[L][W][U][H]; + P[Z][ee][H] += D[L][V][U][H]; } } } return { dataId: u.write(y.toTypedArray(P, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype }; } }; -var O2 = { kernelName: yb, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t6 }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t6, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: $, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === R.length, () => `Error in ${yb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`); +var z2 = { kernelName: _b, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => { + let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === R.length, () => `Error in ${_b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`); let D = y.toNestedArray(R, u.data.get(s.dataId).values), P = y.makeZerosNestedTypedArray(o.shape, o.dtype); for (let L = 0; L < m; ++L) - for (let W = 0; W < g; ++W) { - let V = W * C - b.top; + for (let V = 0; V < g; ++V) { + let z = V * C - b.top; for (let U = 0; U < x; ++U) { - let q = U * w - b.left; + let K = U * w - b.left; for (let H = 0; H < h; ++H) { - let j = Number.MIN_SAFE_INTEGER, X = V < 0 ? 0 : V, Z = q < 0 ? 0 : q; - for (let ee = 0; ee < k; ++ee) { - let Y = V + ee * $; - if (Y >= 0 && Y < d) - for (let J = 0; J < _; ++J) { - let ie = q + J * A; + let q = Number.MIN_SAFE_INTEGER, Z = z < 0 ? 0 : z, ee = K < 0 ? 0 : K; + for (let oe = 0; oe < k; ++oe) { + let J = z + oe * E; + if (J >= 0 && J < d) + for (let te = 0; te < _; ++te) { + let ie = K + te * A; if (ie >= 0 && ie < f) { - let pe = c[L][Y][ie][H] + l[ee][J][H]; - pe > j && (j = pe, X = Y, Z = ie); + let ce = c[L][J][ie][H] + l[oe][te][H]; + ce > q && (q = ce, Z = J, ee = ie); } } } - P[L][X][Z][H] += D[L][W][U][H]; + P[L][Z][ee][H] += D[L][V][U][H]; } } } return { dataId: u.write(y.toTypedArray(P, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -function La(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - K(n, "sum"); +function za(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + j(n, "sum"); let i; - n.dtype === "bool" ? i = Io({ inputs: { x: n }, backend: t6, attrs: { dtype: "int32" } }) : i = ar({ inputs: { x: n }, backend: t6 }); + n.dtype === "bool" ? i = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "int32" } }) : i = ir({ inputs: { x: n }, backend: t10 }); let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = S.getAxesPermutation(u, p), l = u, m = i; - c != null && (m = Ct({ inputs: { x: i }, backend: t6, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, p)), S.assertAxesAreInnerMostDims("sum", l, m.shape.length); - let [d, f] = S.computeOutAndReduceShapes(m.shape, l), h = S.upcastType(m.dtype, "int32"), g = Hp(t6, d, h), x = y.sizeFromShape(f), b = t6.data.get(g.dataId).values, C = t6.data.get(m.dataId).values; + c != null && (m = St({ inputs: { x: i }, backend: t10, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, p)), S.assertAxesAreInnerMostDims("sum", l, m.shape.length); + let [d, f] = S.computeOutAndReduceShapes(m.shape, l), h = S.upcastType(m.dtype, "int32"), g = Kp(t10, d, h), x = y.sizeFromShape(f), b = t10.data.get(g.dataId).values, C = t10.data.get(m.dataId).values; for (let w = 0; w < b.length; ++w) { let k = w * x, _ = 0; - for (let $ = 0; $ < x; ++$) - _ += C[k + $]; + for (let E = 0; E < x; ++E) + _ += C[k + E]; b[w] = _; } if (a) { let w = S.expandShapeToKeepDim(g.shape, u), k = g; - g = Me({ inputs: { x: g }, backend: t6, attrs: { shape: w } }), t6.disposeIntermediateTensorInfo(k); + g = Le({ inputs: { x: g }, backend: t10, attrs: { shape: w } }), t10.disposeIntermediateTensorInfo(k); } - return t6.disposeIntermediateTensorInfo(i), c != null && t6.disposeIntermediateTensorInfo(m), g; + return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g; } -var P2 = { kernelName: Hn, backendName: "cpu", kernelFunc: La }; -function $j(r) { - let { inputs: e, backend: t6, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); +var W2 = { kernelName: Kn, backendName: "cpu", kernelFunc: za }; +function Yj(r) { + let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); S.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C; - S.isIdentityPermutation(x) ? C = s[g] : (C = Ct({ inputs: { x: s[g] }, backend: t6, attrs: { perm: x } }), f.push(C)); + S.isIdentityPermutation(x) ? C = s[g] : (C = St({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C)); let w = C.shape.slice(); for (let k = 0; k < b.length; ++k) w.splice(b[k], 0, 1); - y.arraysEqual(C.shape, w) || (C = Me({ inputs: { x: C }, backend: t6, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = wu({ inputs: { a: C, b: m }, backend: t6 }), f.push(m)); + y.arraysEqual(C.shape, w) || (C = Le({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = Tu({ inputs: { a: C, b: m }, backend: t10 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = La({ inputs: { x: m }, backend: t6, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = za({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t6.disposeIntermediateTensorInfo(h); + h !== m && t10.disposeIntermediateTensorInfo(h); return m; } -var M2 = { kernelName: ri, backendName: "cpu", kernelFunc: $j }; -function Aj(r) { - let { inputs: e, backend: t6 } = r, { dy: o, y: n } = e; - K([o, n], "eluGrad"); - let s = new Float32Array(y.sizeFromShape(n.shape)), a = t6.data.get(n.dataId).values, i = t6.data.get(o.dataId).values; +var U2 = { kernelName: ii, backendName: "cpu", kernelFunc: Yj }; +function Qj(r) { + let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e; + j([o, n], "eluGrad"); + let s = new Float32Array(y.sizeFromShape(n.shape)), a = t10.data.get(n.dataId).values, i = t10.data.get(o.dataId).values; for (let p = 0; p < a.length; ++p) { let u = a[p]; u >= 1 ? s[p] = i[p] : s[p] = i[p] * (u + 1); } - return t6.makeTensorInfo(n.shape, "float32", s); + return t10.makeTensorInfo(n.shape, "float32", s); } -var L2 = { kernelName: km, backendName: "cpu", kernelFunc: Aj }; -var Rj = S.ERF_P; -var Fj = S.ERF_A1; -var Dj = S.ERF_A2; -var Oj = S.ERF_A3; -var Pj = S.ERF_A4; -var Mj = S.ERF_A5; -var Lj = Ie(ma, (r) => { - let e = Math.sign(r), t6 = Math.abs(r), o = 1 / (1 + Rj * t6); - return e * (1 - ((((Mj * o + Pj) * o + Oj) * o + Dj) * o + Fj) * o * Math.exp(-t6 * t6)); +var G2 = { kernelName: Nm, backendName: "cpu", kernelFunc: Qj }; +var Zj = S.ERF_P; +var Jj = S.ERF_A1; +var eX = S.ERF_A2; +var tX = S.ERF_A3; +var rX = S.ERF_A4; +var oX = S.ERF_A5; +var nX = we(da, (r) => { + let e = Math.sign(r), t10 = Math.abs(r), o = 1 / (1 + Zj * t10); + return e * (1 - ((((oX * o + rX) * o + tX) * o + eX) * o + Jj) * o * Math.exp(-t10 * t10)); }); -var B2 = { kernelName: ma, backendName: "cpu", kernelFunc: Lj }; -function Jp(r) { - let { inputs: e, backend: t6, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; - return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Me({ inputs: { x: n }, backend: t6, attrs: { shape: i } }); +var H2 = { kernelName: da, backendName: "cpu", kernelFunc: nX }; +function ec(r) { + let { inputs: e, backend: t10, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; + return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Le({ inputs: { x: n }, backend: t10, attrs: { shape: i } }); } -var V2 = { kernelName: bs, backendName: "cpu", kernelFunc: Jp }; -var Bj = Be((r, e) => r / e); -var hl = Qe(Jo, Bj); -var gl = { kernelName: Jo, backendName: "cpu", kernelFunc: hl }; -function vf(r, e, t6) { - let o = r.shape, n = o[0], s = o[1], a = t6.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); +var K2 = { kernelName: Ss, backendName: "cpu", kernelFunc: ec }; +var sX = Be((r, e) => r / e); +var yl = Qe(Zo, sX); +var bl = { kernelName: Zo, backendName: "cpu", kernelFunc: yl }; +function vf(r, e, t10) { + let o = r.shape, n = o[0], s = o[1], a = t10.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); for (let g = 0; g < n; g++) { - let x = No({ inputs: { x: i }, backend: t6, attrs: { begin: [g, 0], size: [1, s] } }), b = No({ inputs: { x: p }, backend: t6, attrs: { begin: [g, 0], size: [1, s] } }), C = Ht({ inputs: { real: x, imag: b }, backend: t6 }), { real: w, imag: k } = Vj(C, e, t6), _ = S.mergeRealAndImagArrays(w, k); - for (let $ = 0; $ < s; $++) { - let A = S.getComplexWithIndex(_, $); - l[g * s + $] = A.real, m[g * s + $] = A.imag; + let x = To({ inputs: { x: i }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), b = To({ inputs: { x: p }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), C = Ht({ inputs: { real: x, imag: b }, backend: t10 }), { real: w, imag: k } = aX(C, e, t10), _ = S.mergeRealAndImagArrays(w, k); + for (let E = 0; E < s; E++) { + let A = S.getComplexWithIndex(_, E); + l[g * s + E] = A.real, m[g * s + E] = A.imag; } - t6.disposeIntermediateTensorInfo(x), t6.disposeIntermediateTensorInfo(b), t6.disposeIntermediateTensorInfo(C); + t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), t10.disposeIntermediateTensorInfo(C); } - let d = t6.makeTensorInfo(u, "float32", l), f = t6.makeTensorInfo(u, "float32", m), h = Ht({ inputs: { real: d, imag: f }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(f), h; + let d = t10.makeTensorInfo(u, "float32", l), f = t10.makeTensorInfo(u, "float32", m), h = Ht({ inputs: { real: d, imag: f }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), h; } -function Vj(r, e, t6) { - let o = y.sizeFromShape(r.shape), n = t6.data.get(r.dataId), s = t6.data.get(n.complexTensorInfos.real.dataId).values, a = t6.data.get(n.complexTensorInfos.imag.dataId).values; - if (zj(o)) { - let i = _S(s, a, o, e, t6), p = [r.shape[0], r.shape[1]]; +function aX(r, e, t10) { + let o = y.sizeFromShape(r.shape), n = t10.data.get(r.dataId), s = t10.data.get(n.complexTensorInfos.real.dataId).values, a = t10.data.get(n.complexTensorInfos.imag.dataId).values; + if (iX(o)) { + let i = BS(s, a, o, e, t10), p = [r.shape[0], r.shape[1]]; if (e) { - let u = t6.makeTensorInfo(p, "float32", i.real), c = t6.makeTensorInfo(p, "float32", i.imag), l = t6.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = ar({ inputs: { x: l }, backend: t6 }), d = gl.kernelFunc({ inputs: { a: u, b: l }, backend: t6 }), f = gl.kernelFunc({ inputs: { a: c, b: m }, backend: t6 }), h = t6.data.get(d.dataId).values, g = t6.data.get(f.dataId).values; - return t6.disposeIntermediateTensorInfo(u), t6.disposeIntermediateTensorInfo(c), t6.disposeIntermediateTensorInfo(l), t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(f), { real: h, imag: g }; + let u = t10.makeTensorInfo(p, "float32", i.real), c = t10.makeTensorInfo(p, "float32", i.imag), l = t10.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = ir({ inputs: { x: l }, backend: t10 }), d = bl.kernelFunc({ inputs: { a: u, b: l }, backend: t10 }), f = bl.kernelFunc({ inputs: { a: c, b: m }, backend: t10 }), h = t10.data.get(d.dataId).values, g = t10.data.get(f.dataId).values; + return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), { real: h, imag: g }; } return i; } else { - let i = S.mergeRealAndImagArrays(s, a), p = Wj(i, o, e); + let i = S.mergeRealAndImagArrays(s, a), p = uX(i, o, e); return S.splitRealAndImagArrays(p); } } -function zj(r) { +function iX(r) { return (r & r - 1) === 0; } -function _S(r, e, t6, o, n) { - if (t6 === 1) +function BS(r, e, t10, o, n) { + if (t10 === 1) return { real: r, imag: e }; - let s = S.mergeRealAndImagArrays(r, e), a = t6 / 2, i = S.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Ht({ inputs: { real: l, imag: m }, backend: n }), f = S.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), w = Ht({ inputs: { real: b, imag: C }, backend: n }), k = _S(p, u, a, o, n), _ = k.real, $ = k.imag, A = [_.length], R = n.makeTensorInfo(A, "float32", _), D = n.makeTensorInfo(A, "float32", $), P = Ht({ inputs: { real: R, imag: D }, backend: n }), M = _S(h, g, a, o, n), L = M.real, W = M.imag, V = [L.length], U = n.makeTensorInfo(V, "float32", L), q = n.makeTensorInfo(V, "float32", W), H = Ht({ inputs: { real: U, imag: q }, backend: n }), j = S.exponents(t6, o), X = [j.real.length], Z = n.makeTensorInfo(X, "float32", j.real), ee = n.makeTensorInfo(X, "float32", j.imag), Y = Ht({ inputs: { real: Z, imag: ee }, backend: n }), J = wu({ inputs: { a: Y, b: H }, backend: n }), ie = js({ inputs: { a: P, b: J }, backend: n }), pe = dl({ inputs: { a: P, b: J }, backend: n }), he = wo({ inputs: { input: ie }, backend: n }), we = wo({ inputs: { input: pe }, backend: n }), ve = Xs({ inputs: { input: ie }, backend: n }), $e = Xs({ inputs: { input: pe }, backend: n }), Le = Pi({ inputs: [he, we], backend: n, attrs: { axis: 0 } }), nt = Pi({ inputs: [ve, $e], backend: n, attrs: { axis: 0 } }), pt = n.data.get(Le.dataId).values, Oe = n.data.get(nt.dataId).values; - return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(R), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(Z), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(Y), n.disposeIntermediateTensorInfo(J), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(pe), n.disposeIntermediateTensorInfo(he), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(we), n.disposeIntermediateTensorInfo($e), n.disposeIntermediateTensorInfo(Le), n.disposeIntermediateTensorInfo(nt), { real: pt, imag: Oe }; + let s = S.mergeRealAndImagArrays(r, e), a = t10 / 2, i = S.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Ht({ inputs: { real: l, imag: m }, backend: n }), f = S.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), w = Ht({ inputs: { real: b, imag: C }, backend: n }), k = BS(p, u, a, o, n), _ = k.real, E = k.imag, A = [_.length], R = n.makeTensorInfo(A, "float32", _), D = n.makeTensorInfo(A, "float32", E), P = Ht({ inputs: { real: R, imag: D }, backend: n }), M = BS(h, g, a, o, n), L = M.real, V = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), K = n.makeTensorInfo(z, "float32", V), H = Ht({ inputs: { real: U, imag: K }, backend: n }), q = S.exponents(t10, o), Z = [q.real.length], ee = n.makeTensorInfo(Z, "float32", q.real), oe = n.makeTensorInfo(Z, "float32", q.imag), J = Ht({ inputs: { real: ee, imag: oe }, backend: n }), te = Tu({ inputs: { a: J, b: H }, backend: n }), ie = Xs({ inputs: { a: P, b: te }, backend: n }), ce = gl({ inputs: { a: P, b: te }, backend: n }), ye = Io({ inputs: { input: ie }, backend: n }), Ne = Io({ inputs: { input: ce }, backend: n }), Ie = Ys({ inputs: { input: ie }, backend: n }), Ae = Ys({ inputs: { input: ce }, backend: n }), Re = Mi({ inputs: [ye, Ne], backend: n, attrs: { axis: 0 } }), ot = Mi({ inputs: [Ie, Ae], backend: n, attrs: { axis: 0 } }), pt = n.data.get(Re.dataId).values, ze = n.data.get(ot.dataId).values; + return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(R), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(K), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(J), n.disposeIntermediateTensorInfo(te), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(ce), n.disposeIntermediateTensorInfo(ye), n.disposeIntermediateTensorInfo(Ie), n.disposeIntermediateTensorInfo(Ne), n.disposeIntermediateTensorInfo(Ae), n.disposeIntermediateTensorInfo(Re), n.disposeIntermediateTensorInfo(ot), { real: pt, imag: ze }; } -function Wj(r, e, t6) { +function uX(r, e, t10) { let o = new Float32Array(e * 2); for (let n = 0; n < e; n++) { let s = 0, a = 0; for (let i = 0; i < e; i++) { - let p = S.exponent(n * i, e, t6), u = S.getComplexWithIndex(r, i); + let p = S.exponent(n * i, e, t10), u = S.getComplexWithIndex(r, i); s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real; } - t6 && (s /= e, a /= e), S.assignToTypedArray(o, s, a, n); + t10 && (s /= e, a /= e), S.assignToTypedArray(o, s, a, n); } return o; } -function Uj(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Me({ inputs: { x: o }, backend: t6, attrs: { shape: [a, s] } }), p = vf(i, false, t6), u = Me({ inputs: { x: p }, backend: t6, attrs: { shape: o.shape } }); - return t6.disposeIntermediateTensorInfo(i), t6.disposeIntermediateTensorInfo(p), u; +function pX(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Le({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = vf(i, false, t10), u = Le({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } }); + return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u; } -var z2 = { kernelName: oi, backendName: "cpu", kernelFunc: Uj }; -function xl(r) { - let { backend: e, attrs: t6 } = r, { shape: o, value: n, dtype: s } = t6, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); - return Gj(i, n, a), e.makeTensorInfo(o, a, i); +var q2 = { kernelName: ui, backendName: "cpu", kernelFunc: pX }; +function Cl(r) { + let { backend: e, attrs: t10 } = r, { shape: o, value: n, dtype: s } = t10, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); + return cX(i, n, a), e.makeTensorInfo(o, a, i); } -var W2 = { kernelName: Cs, backendName: "cpu", kernelFunc: xl }; -function Gj(r, e, t6) { +var j2 = { kernelName: ws, backendName: "cpu", kernelFunc: Cl }; +function cX(r, e, t10) { r.fill(e); } -var U2 = { kernelName: on, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { image: o } = r, n = t6, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; +var X2 = { kernelName: rn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { image: o } = r, n = t10, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; for (let m = 0; m < a; m++) { let d = m * p * i * u; for (let f = 0; f < i; f++) { @@ -13890,8 +13885,8 @@ var U2 = { kernelName: on, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: for (let b = 0; b < u; b++) { let C = Math.round(p - g - 1), w = d + h + x + b, k = c[w]; if (C >= 0 && C < p) { - let _ = C * u, $ = d + h + _ + b; - k = c[$]; + let _ = C * u, E = d + h + _ + b; + k = c[E]; } s[w] = k; } @@ -13900,97 +13895,97 @@ var U2 = { kernelName: on, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: } return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -var Hj = Be((r, e) => Math.floor(r / e)); -var qj = Qe(sn, Hj, null, "int32"); -var G2 = { kernelName: sn, backendName: "cpu", kernelFunc: qj }; -function Kj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = NS({ inputs: { x: n, filter: s }, backend: t6, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +var lX = Be((r, e) => Math.floor(r / e)); +var mX = Qe(nn, lX, null, "int32"); +var Y2 = { kernelName: nn, backendName: "cpu", kernelFunc: mX }; +function dX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = MS({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; if (c === "NCHW" && a.shape.length === 1 && a.shape[0] !== 1) { - let x = Me({ inputs: { x: a }, backend: t6, attrs: { shape: [a.shape[0], 1, 1] } }); - h = js({ inputs: { a: h, b: x }, backend: t6 }), t6.disposeIntermediateTensorInfo(x); + let x = Le({ inputs: { x: a }, backend: t10, attrs: { shape: [a.shape[0], 1, 1] } }); + h = Xs({ inputs: { a: h, b: x }, backend: t10 }), t10.disposeIntermediateTensorInfo(x); } else - h = js({ inputs: { a: h, b: a }, backend: t6 }); - t6.disposeIntermediateTensorInfo(g); + h = Xs({ inputs: { a: h, b: a }, backend: t10 }); + t10.disposeIntermediateTensorInfo(g); } if (d) { let g = h; if (c === "NCHW" && d === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) { - let x = Me({ inputs: { x: i }, backend: t6, attrs: { shape: [i.shape[0], 1, 1] } }); - h = _u(t6, h, d, x, f), t6.disposeIntermediateTensorInfo(x); + let x = Le({ inputs: { x: i }, backend: t10, attrs: { shape: [i.shape[0], 1, 1] } }); + h = Du(t10, h, d, x, f), t10.disposeIntermediateTensorInfo(x); } else - h = _u(t6, h, d, i, f); - t6.disposeIntermediateTensorInfo(g); + h = Du(t10, h, d, i, f); + t10.disposeIntermediateTensorInfo(g); } return h; } -var H2 = { kernelName: ho, backendName: "cpu", kernelFunc: Kj }; -function jj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = TS({ inputs: { x: n, filter: s }, backend: t6, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +var Q2 = { kernelName: go, backendName: "cpu", kernelFunc: dX }; +function fX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = LS({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; - h = js({ inputs: { a: h, b: a }, backend: t6 }), t6.disposeIntermediateTensorInfo(g); + h = Xs({ inputs: { a: h, b: a }, backend: t10 }), t10.disposeIntermediateTensorInfo(g); } if (d) { let g = h; - h = _u(t6, h, d, i, f), t6.disposeIntermediateTensorInfo(g); + h = Du(t10, h, d, i, f), t10.disposeIntermediateTensorInfo(g); } return h; } -var q2 = { kernelName: go, backendName: "cpu", kernelFunc: jj }; -function Xj(r) { - let { inputs: e, backend: t6 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = S.prepareAndValidate(o, n); +var Z2 = { kernelName: xo, backendName: "cpu", kernelFunc: fX }; +function hX(r) { + let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = S.prepareAndValidate(o, n); if (u === 0) - return t6.makeTensorInfo(p, o.dtype, []); - let m = t6.data.get(n.dataId).values, d = t6.bufferSync(o), f = pf(m, d, o.dtype, u, i, c, l, o.shape, s); - return t6.makeTensorInfo(p, o.dtype, f.values); + return t10.makeTensorInfo(p, o.dtype, []); + let m = t10.data.get(n.dataId).values, d = t10.bufferSync(o), f = pf(m, d, o.dtype, u, i, c, l, o.shape, s); + return t10.makeTensorInfo(p, o.dtype, f.values); } -var K2 = { kernelName: un, backendName: "cpu", kernelFunc: Xj }; -function Yj(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o; - K([n, s], "gatherV2"); - let p = y.parseAxisParam(a, n.shape)[0], u = t6.data.get(s.dataId).values, c = n.shape[p]; +var J2 = { kernelName: an, backendName: "cpu", kernelFunc: hX }; +function gX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o; + j([n, s], "gatherV2"); + let p = y.parseAxisParam(a, n.shape)[0], u = t10.data.get(s.dataId).values, c = n.shape[p]; for (let w = 0; w < u.length; ++w) { let k = u[w]; y.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`); } let l = i; i == null && (l = 0); - let m = y.sizeFromShape(s.shape), d = S.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Me({ inputs: { x: n }, backend: t6, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Me({ inputs: { x: s }, backend: t6, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t6.bufferSync(h), b = t6.bufferSync(f), C = cf(b, x, g); - return t6.disposeIntermediateTensorInfo(f), t6.disposeIntermediateTensorInfo(h), t6.makeTensorInfo(d.outputShape, C.dtype, C.values); + let m = y.sizeFromShape(s.shape), d = S.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Le({ inputs: { x: n }, backend: t10, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Le({ inputs: { x: s }, backend: t10, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t10.bufferSync(h), b = t10.bufferSync(f), C = cf(b, x, g); + return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.makeTensorInfo(d.outputShape, C.dtype, C.values); } -var j2 = { kernelName: Ss, backendName: "cpu", kernelFunc: Yj }; -function Qj(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Me({ inputs: { x: o }, backend: t6, attrs: { shape: [a, s] } }), p = vf(i, true, t6), u = Me({ inputs: { x: p }, backend: t6, attrs: { shape: o.shape } }); - return t6.disposeIntermediateTensorInfo(i), t6.disposeIntermediateTensorInfo(p), u; +var e_ = { kernelName: Is, backendName: "cpu", kernelFunc: gX }; +function xX(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Le({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = vf(i, true, t10), u = Le({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } }); + return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u; } -var X2 = { kernelName: ni, backendName: "cpu", kernelFunc: Qj }; -var Zj = Ie(fa, (r) => Number.isFinite(r) ? 1 : 0, "bool"); -var Y2 = { kernelName: fa, backendName: "cpu", kernelFunc: Zj }; -var Jj = Ie(ha, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool"); -var Q2 = { kernelName: ha, backendName: "cpu", kernelFunc: Jj }; -var eX = Ie(ln, (r) => Number.isNaN(r) ? 1 : 0, "bool"); -var Z2 = { kernelName: ln, backendName: "cpu", kernelFunc: eX }; -function tX(r) { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, num: s } = t6, a = lf(o, n, s); +var t_ = { kernelName: pi, backendName: "cpu", kernelFunc: xX }; +var yX = we(ha, (r) => Number.isFinite(r) ? 1 : 0, "bool"); +var r_ = { kernelName: ha, backendName: "cpu", kernelFunc: yX }; +var bX = we(ga, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool"); +var o_ = { kernelName: ga, backendName: "cpu", kernelFunc: bX }; +var CX = we(cn, (r) => Number.isNaN(r) ? 1 : 0, "bool"); +var n_ = { kernelName: cn, backendName: "cpu", kernelFunc: CX }; +function SX(r) { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = lf(o, n, s); return e.makeTensorInfo([a.length], "float32", a); } -var J2 = { kernelName: xp, backendName: "cpu", kernelFunc: tX }; -var rX = Ie(ga, (r) => Math.log1p(r)); -var e_ = { kernelName: ga, backendName: "cpu", kernelFunc: rX }; -var oX = Be((r, e) => r && e); -var nX = Qe(gn, oX, null, "bool"); -var t_ = { kernelName: gn, backendName: "cpu", kernelFunc: nX }; -var sX = Ie(xn, (r) => r ? 0 : 1, "bool"); -var r_ = { kernelName: xn, backendName: "cpu", kernelFunc: sX }; -var aX = Be((r, e) => r || e); -var iX = Qe(xa, aX, null, "bool"); -var o_ = { kernelName: xa, backendName: "cpu", kernelFunc: iX }; -function uX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; - K(n, "LRN"); - let u = n.shape[3], c = u - 1, l = t6.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m); +var s_ = { kernelName: li, backendName: "cpu", kernelFunc: SX }; +var wX = we(xa, (r) => Math.log1p(r)); +var a_ = { kernelName: xa, backendName: "cpu", kernelFunc: wX }; +var IX = Be((r, e) => r && e); +var vX = Qe(hn, IX, null, "bool"); +var i_ = { kernelName: hn, backendName: "cpu", kernelFunc: vX }; +var kX = we(gn, (r) => r ? 0 : 1, "bool"); +var u_ = { kernelName: gn, backendName: "cpu", kernelFunc: kX }; +var NX = Be((r, e) => r || e); +var TX = Qe(xn, NX, null, "bool"); +var p_ = { kernelName: xn, backendName: "cpu", kernelFunc: TX }; +function _X(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; + j(n, "LRN"); + let u = n.shape[3], c = u - 1, l = t10.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m); function f(h) { let g = h % u, x = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), C = 0; for (; x <= b; x++) { @@ -14003,149 +13998,149 @@ function uX(r) { let g = f(h), x = l[h] * Math.pow(a + i * g, -p); d[h] = x; } - return t6.makeTensorInfo(n.shape, n.dtype, d); + return t10.makeTensorInfo(n.shape, n.dtype, d); } -var n_ = { kernelName: yp, backendName: "cpu", kernelFunc: uX }; -function pX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; - K(a, "LRNGrad"); - let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t6.data.get(a.dataId).values, f = t6.data.get(n.dataId).values, h = t6.data.get(s.dataId).values, g = new Float32Array(l), x = l; +var c_ = { kernelName: mi, backendName: "cpu", kernelFunc: _X }; +function $X(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; + j(a, "LRNGrad"); + let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t10.data.get(a.dataId).values, f = t10.data.get(n.dataId).values, h = t10.data.get(s.dataId).values, g = new Float32Array(l), x = l; for (let b = 0; b < x; b++) { let C = b % m, w = b - C + Math.max(0, C - i), k = b - C + Math.min(m, C + i + 1), _ = 0; - for (let $ = w; $ < k; $++) - _ += Math.pow(f[$], 2); + for (let E = w; E < k; E++) + _ += Math.pow(f[E], 2); _ = u * _ + p; - for (let $ = w; $ < k; $++) { - let A = -2 * u * c * f[$] * h[b] / _; - b === $ && (A += Math.pow(_, -c)), A *= d[b], g[$] += A; + for (let E = w; E < k; E++) { + let A = -2 * u * c * f[E] * h[b] / _; + b === E && (A += Math.pow(_, -c)), A *= d[b], g[E] += A; } } - return t6.makeTensorInfo(a.shape, n.dtype, g); + return t10.makeTensorInfo(a.shape, n.dtype, g); } -var s_ = { kernelName: Nm, backendName: "cpu", kernelFunc: pX }; -function ES(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t6, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = S.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; +var l_ = { kernelName: Tm, backendName: "cpu", kernelFunc: $X }; +function VS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t10, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = S.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; if (m != null) { let w = new Array(u); for (let k = 0; k < w.length; k++) w[k] = p[m[k]]; - d = jp(d, p, n.dtype, m, w), l = S.getInnerMostAxes(l.length, u), p = w; + d = Xp(d, p, n.dtype, m, w), l = S.getInnerMostAxes(l.length, u), p = w; } - K(n, "max"), S.assertAxesAreInnerMostDims("max", l, u); + j(n, "max"), S.assertAxesAreInnerMostDims("max", l, u); let [f, h] = S.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = mf(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f; return a && (C = S.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype }; } -var a_ = { kernelName: yn, backendName: "cpu", kernelFunc: ES }; -function cX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e; - K(n, "maxPool"); +var m_ = { kernelName: yn, backendName: "cpu", kernelFunc: VS }; +function EX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; + j(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = S.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = ar({ inputs: { x: n }, backend: t6 }); + l = ir({ inputs: { x: n }, backend: t10 }); else { - let m = t6.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Zp(m, n.shape, n.dtype, d, c, "max"); - l = t6.makeTensorInfo(c.outShape, n.dtype, f.values); + let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Jp(m, n.shape, n.dtype, d, c, "max"); + l = t10.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var i_ = { kernelName: Cn, backendName: "cpu", kernelFunc: cX }; -function lX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; - K(n, "maxPool3d"); - let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t6.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); - return t6.makeTensorInfo(m.shape, "float32", m.values); +var d_ = { kernelName: Cn, backendName: "cpu", kernelFunc: EX }; +function AX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; + j(n, "maxPool3d"); + let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); + return t10.makeTensorInfo(m.shape, "float32", m.values); } -var u_ = { kernelName: bp, backendName: "cpu", kernelFunc: lX }; -function mX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; - K([n, s], "maxPool3DGrad"); - let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = t6.bufferSync(s), m = o2(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, w = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, $ = k - 1 - c.padInfo.left, A = w - 1 - c.padInfo.top, R = le(s.shape, "float32"), D = t6.bufferSync(n); +var f_ = { kernelName: Cp, backendName: "cpu", kernelFunc: AX }; +function RX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; + j([n, s], "maxPool3DGrad"); + let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.bufferSync(s), m = p2(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, w = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, A = w - 1 - c.padInfo.top, R = le(s.shape, "float32"), D = t10.bufferSync(n); for (let P = 0; P < c.batchSize; ++P) for (let M = 0; M < c.inChannels; ++M) for (let L = 0; L < c.inDepth; ++L) - for (let W = 0; W < c.inHeight; ++W) - for (let V = 0; V < c.inWidth; ++V) { - let U = L - _, q = W - A, H = V - $, j = 0; - for (let X = 0; X < C; X += g) { - let Z = (U + X) / d; - if (!(Z < 0 || Z >= c.outDepth || Math.floor(Z) !== Z)) - for (let ee = 0; ee < w; ee += x) { - let Y = (q + ee) / f; - if (!(Y < 0 || Y >= c.outHeight || Math.floor(Y) !== Y)) - for (let J = 0; J < k; J += b) { - let ie = (H + J) / h; + for (let V = 0; V < c.inHeight; ++V) + for (let z = 0; z < c.inWidth; ++z) { + let U = L - _, K = V - A, H = z - E, q = 0; + for (let Z = 0; Z < C; Z += g) { + let ee = (U + Z) / d; + if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee)) + for (let oe = 0; oe < w; oe += x) { + let J = (K + oe) / f; + if (!(J < 0 || J >= c.outHeight || Math.floor(J) !== J)) + for (let te = 0; te < k; te += b) { + let ie = (H + te) / h; if (ie < 0 || ie >= c.outWidth || Math.floor(ie) !== ie) continue; - let pe = C * w * k - 1 - m.get(P, Z, Y, ie, M), he = X * w * k + ee * k + J, we = pe === he ? 1 : 0; - if (we === 0) + let ce = C * w * k - 1 - m.get(P, ee, J, ie, M), ye = Z * w * k + oe * k + te, Ne = ce === ye ? 1 : 0; + if (Ne === 0) continue; - let ve = D.get(P, Z, Y, ie, M); - j += ve * we; + let Ie = D.get(P, ee, J, ie, M); + q += Ie * Ne; } } } - R.set(j, P, L, W, V, M); + R.set(q, P, L, V, z, M); } - return t6.makeTensorInfo(R.shape, R.dtype, R.values); + return t10.makeTensorInfo(R.shape, R.dtype, R.values); } -var p_ = { kernelName: _m, backendName: "cpu", kernelFunc: mX }; -function dX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; - K([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = t6.data.get(i.dataId).values, f = le(m.outShape, i.dtype, wf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, w = m.effectiveFilterWidth, k = w - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, $ = le(i.shape, "float32"), A = t6.data.get(n.dataId).values, R = le(n.shape, "float32", A); +var h_ = { kernelName: $m, backendName: "cpu", kernelFunc: RX }; +function DX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; + j([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = t10.data.get(i.dataId).values, f = le(m.outShape, i.dtype, wf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, w = m.effectiveFilterWidth, k = w - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = le(i.shape, "float32"), A = t10.data.get(n.dataId).values, R = le(n.shape, "float32", A); for (let D = 0; D < m.batchSize; ++D) for (let P = 0; P < m.inChannels; ++P) for (let M = 0; M < m.inHeight; ++M) for (let L = 0; L < m.inWidth; ++L) { - let W = M - _, V = L - k, U = 0; - for (let q = 0; q < C; q += x) { - let H = (W + q) / h; + let V = M - _, z = L - k, U = 0; + for (let K = 0; K < C; K += x) { + let H = (V + K) / h; if (!(H < 0 || H >= m.outHeight || Math.floor(H) !== H)) - for (let j = 0; j < w; j += b) { - let X = (V + j) / g; - if (X < 0 || X >= m.outWidth || Math.floor(X) !== X) + for (let q = 0; q < w; q += b) { + let Z = (z + q) / g; + if (Z < 0 || Z >= m.outWidth || Math.floor(Z) !== Z) continue; - let Z = C * w - 1 - f.get(D, H, X, P), ee = q * w + j, Y = Z === ee ? 1 : 0; - if (Y === 0) + let ee = C * w - 1 - f.get(D, H, Z, P), oe = K * w + q, J = ee === oe ? 1 : 0; + if (J === 0) continue; - let J = R.get(D, H, X, P); - U += J * Y; + let te = R.get(D, H, Z, P); + U += te * J; } } - $.set(U, D, M, L, P); + E.set(U, D, M, L, P); } - return t6.makeTensorInfo($.shape, $.dtype, $.values); + return t10.makeTensorInfo(E.shape, E.dtype, E.values); } -var c_ = { kernelName: Tm, backendName: "cpu", kernelFunc: dX }; -function l_(r, e, t6, o, n) { - let s = y.computeStrides(e), a = Zp(r, e, t6, s, n, "max"), i = wf(r, e, t6, n, true, o); +var g_ = { kernelName: _m, backendName: "cpu", kernelFunc: DX }; +function x_(r, e, t10, o, n) { + let s = y.computeStrides(e), a = Jp(r, e, t10, s, n, "max"), i = wf(r, e, t10, n, true, o); return [a.values, i.values]; } -var m_ = { kernelName: Cp, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t6; - K(o, "MaxPoolWithArgmax"); - let u = p.data.get(o.dataId).values, c = S.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = l_(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); +var y_ = { kernelName: Sp, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10; + j(o, "MaxPoolWithArgmax"); + let u = p.data.get(o.dataId).values, c = S.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = x_(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); return [{ dataId: d, shape: c.outShape, dtype: o.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }]; } }; -function fX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = S.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t6.makeTensorInfo([], "float32", new Float32Array([c])); +function FX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = S.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t10.makeTensorInfo([], "float32", new Float32Array([c])); l.push(m); - let d = Io({ inputs: { x: n }, backend: t6, attrs: { dtype: "float32" } }); + let d = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }); l.push(d); - let f = hl({ inputs: { a: d, b: m }, backend: t6 }); + let f = yl({ inputs: { a: d, b: m }, backend: t10 }); l.push(f); - let h = La({ inputs: { x: f }, backend: t6, attrs: { axis: s, keepDims: a } }); - return l.forEach((g) => t6.disposeIntermediateTensorInfo(g)), h; + let h = za({ inputs: { x: f }, backend: t10, attrs: { axis: s, keepDims: a } }); + return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), h; } -var d_ = { kernelName: Sn, backendName: "cpu", kernelFunc: fX }; -function hX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - K(n, "min"); +var b_ = { kernelName: Sn, backendName: "cpu", kernelFunc: FX }; +function OX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + j(n, "min"); let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = Ct({ inputs: { x: n }, backend: t6, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", p, c.shape.length); - let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t6.data.get(c.dataId).values; + u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", p, c.shape.length); + let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; for (let w = 0; w < d; ++w) { @@ -14154,55 +14149,55 @@ function hX(r) { } f[x] = C; } - u != null && t6.disposeIntermediateTensorInfo(c); - let g = t6.makeTensorInfo(l, c.dtype, f); + u != null && t10.disposeIntermediateTensorInfo(c); + let g = t10.makeTensorInfo(l, c.dtype, f); if (a) { - let x = S.expandShapeToKeepDim(l, i), b = Me({ inputs: { x: g }, backend: t6, attrs: { shape: x } }); - return t6.disposeIntermediateTensorInfo(g), b; + let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } }); + return t10.disposeIntermediateTensorInfo(g), b; } return g; } -var f_ = { kernelName: wn, backendName: "cpu", kernelFunc: hX }; -function gX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o; - K(n, "mirrorPad"); - let i = s.map((C, w) => C[0] + n.shape[w] + C[1]), p = s.map((C) => C[0]), u = s.map((C, w) => C[0] + n.shape[w]), c = a === "reflect" ? 0 : 1, l = t6.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f); +var C_ = { kernelName: wn, backendName: "cpu", kernelFunc: OX }; +function PX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o; + j(n, "mirrorPad"); + let i = s.map((C, w) => C[0] + n.shape[w] + C[1]), p = s.map((C) => C[0]), u = s.map((C, w) => C[0] + n.shape[w]), c = a === "reflect" ? 0 : 1, l = t10.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f); for (let C = 0; C < f; C++) { let w = y.indexToLoc(C, h, g); for (let _ = 0; _ < h; _++) w[_] < p[_] ? w[_] = p[_] * 2 - w[_] - c : w[_] >= u[_] && (w[_] = (u[_] - 1) * 2 - w[_] + c); - w = w.map((_, $) => _ - p[$]); + w = w.map((_, E) => _ - p[E]); let k = y.locToIndex(w, m, d); x[C] = l[k]; } - return { dataId: t6.write(x, i, n.dtype), shape: i, dtype: n.dtype }; + return { dataId: t10.write(x, i, n.dtype), shape: i, dtype: n.dtype }; } -var h_ = { kernelName: vn, backendName: "cpu", kernelFunc: gX }; -var xX = Be((r, e) => { - let t6 = r % e; - return r < 0 && e < 0 || r >= 0 && e >= 0 ? t6 : (t6 + e) % e; +var S_ = { kernelName: vn, backendName: "cpu", kernelFunc: PX }; +var MX = Be((r, e) => { + let t10 = r % e; + return r < 0 && e < 0 || r >= 0 && e >= 0 ? t10 : (t10 + e) % e; }); -var yX = Qe(ya, xX); -var g_ = { kernelName: ya, backendName: "cpu", kernelFunc: yX }; -var y_ = rp(gC()); -function $S(r) { - let { inputs: e, backend: t6, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s; +var LX = Qe(ya, MX); +var w_ = { kernelName: ya, backendName: "cpu", kernelFunc: LX }; +var v_ = ap(gC()); +function zS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s; if (i === -1 && (i = a - 1), i !== a - 1) throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`); - let p = y.parseAxisParam([i], n.shape), u = ES({ inputs: { x: n }, backend: t6, attrs: { reductionIndices: p, keepDims: false } }), c = S.expandShapeToKeepDim(u.shape, p), l = Me({ inputs: { x: u }, backend: t6, attrs: { shape: c } }), m = dl({ inputs: { a: n, b: l }, backend: t6 }), d = rS({ inputs: { x: m }, backend: t6 }), f = La({ inputs: { x: d }, backend: t6, attrs: { axis: p, keepDims: false } }), h = Me({ inputs: { x: f }, backend: t6, attrs: { shape: c } }), g = hl({ inputs: { a: d, b: h }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(u), t6.disposeIntermediateTensorInfo(l), t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(f), t6.disposeIntermediateTensorInfo(h), g; + let p = y.parseAxisParam([i], n.shape), u = VS({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: p, keepDims: false } }), c = S.expandShapeToKeepDim(u.shape, p), l = Le({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), m = gl({ inputs: { a: n, b: l }, backend: t10 }), d = dS({ inputs: { x: m }, backend: t10 }), f = za({ inputs: { x: d }, backend: t10, attrs: { axis: p, keepDims: false } }), h = Le({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = yl({ inputs: { a: d, b: h }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g; } -var x_ = { kernelName: qn, backendName: "cpu", kernelFunc: $S }; -function bX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; - K(n, "multinomial"); - let p = i ? n : $S({ inputs: { logits: n }, backend: t6, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t6.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); +var I_ = { kernelName: qn, backendName: "cpu", kernelFunc: zS }; +function BX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o; + j(n, "multinomial"); + let p = i ? n : zS({ inputs: { logits: n }, backend: t10, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t10.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); for (let f = 0; f < u; ++f) { let h = f * c, g = new Float32Array(c - 1); g[0] = l[h]; for (let C = 1; C < g.length; ++C) g[C] = g[C - 1] + l[h + C]; - let x = y_.alea(a.toString()), b = f * s; + let x = v_.alea(a.toString()), b = f * s; for (let C = 0; C < s; ++C) { let w = x(); d[b + C] = g.length; @@ -14213,332 +14208,332 @@ function bX(r) { } } } - return i || t6.disposeIntermediateTensorInfo(p), t6.makeTensorInfo(m, "int32", d); + return i || t10.disposeIntermediateTensorInfo(p), t10.makeTensorInfo(m, "int32", d); } -var b_ = { kernelName: Sp, backendName: "cpu", kernelFunc: bX }; -var CX = Lt.nonMaxSuppressionV3Impl; -function SX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; - K(n, "NonMaxSuppression"); - let u = t6.data.get(n.dataId).values, c = t6.data.get(s.dataId).values, { selectedIndices: l } = CX(u, c, a, i, p); - return t6.makeTensorInfo([l.length], "int32", new Int32Array(l)); +var k_ = { kernelName: wp, backendName: "cpu", kernelFunc: BX }; +var VX = Vt.nonMaxSuppressionV3Impl; +function zX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; + j(n, "NonMaxSuppression"); + let u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, { selectedIndices: l } = VX(u, c, a, i, p); + return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var C_ = { kernelName: Tn, backendName: "cpu", kernelFunc: SX }; -var wX = Lt.nonMaxSuppressionV4Impl; -function IX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; - K(n, "NonMaxSuppressionPadded"); - let c = t6.data.get(n.dataId).values, l = t6.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = wX(c, l, a, i, p, u); - return [t6.makeTensorInfo([m.length], "int32", new Int32Array(m)), t6.makeTensorInfo([], "int32", new Int32Array([d]))]; +var N_ = { kernelName: Tn, backendName: "cpu", kernelFunc: zX }; +var WX = Vt.nonMaxSuppressionV4Impl; +function UX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; + j(n, "NonMaxSuppressionPadded"); + let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = WX(c, l, a, i, p, u); + return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var S_ = { kernelName: ba, backendName: "cpu", kernelFunc: IX }; -var vX = Lt.nonMaxSuppressionV5Impl; -function kX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; - K(n, "NonMaxSuppressionWithScore"); - let c = t6.data.get(n.dataId).values, l = t6.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = vX(c, l, m, d, f, h); - return [t6.makeTensorInfo([g.length], "int32", new Int32Array(g)), t6.makeTensorInfo([x.length], "float32", new Float32Array(x))]; +var T_ = { kernelName: ba, backendName: "cpu", kernelFunc: UX }; +var GX = Vt.nonMaxSuppressionV5Impl; +function HX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; + j(n, "NonMaxSuppressionWithScore"); + let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = GX(c, l, m, d, f, h); + return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var w_ = { kernelName: _n, backendName: "cpu", kernelFunc: kX }; -function NX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o; - K(n, "oneHot"); +var __ = { kernelName: _n, backendName: "cpu", kernelFunc: HX }; +function KX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o; + j(n, "oneHot"); let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a); c.fill(p); - let l = t6.data.get(n.dataId).values; + let l = t10.data.get(n.dataId).values; for (let m = 0; m < u; ++m) l[m] >= 0 && l[m] < a && (c[m * a + l[m]] = i); - return t6.makeTensorInfo([...n.shape, a], s, c); + return t10.makeTensorInfo([...n.shape, a], s, c); } -var I_ = { kernelName: En, backendName: "cpu", kernelFunc: NX }; -function yl(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var $_ = { kernelName: $n, backendName: "cpu", kernelFunc: KX }; +function Sl(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("zerosLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = wo({ inputs: { input: o }, backend: t6 }), s = yl({ inputs: { x: n }, backend: t6 }), a = Xs({ inputs: { input: o }, backend: t6 }), i = yl({ inputs: { x: a }, backend: t6 }), p = Ht({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(n), t6.disposeIntermediateTensorInfo(s), t6.disposeIntermediateTensorInfo(a), t6.disposeIntermediateTensorInfo(i), p; + let n = Io({ inputs: { input: o }, backend: t10 }), s = Sl({ inputs: { x: n }, backend: t10 }), a = Ys({ inputs: { input: o }, backend: t10 }), i = Sl({ inputs: { x: a }, backend: t10 }), p = Ht({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; } else - return xl({ backend: t6, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); + return Cl({ backend: t10, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); } -var v_ = { kernelName: Fs, backendName: "cpu", kernelFunc: yl }; -function k_(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var E_ = { kernelName: Os, backendName: "cpu", kernelFunc: Sl }; +function A_(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = wo({ inputs: { input: o }, backend: t6 }), s = k_({ inputs: { x: n }, backend: t6 }), a = Xs({ inputs: { input: o }, backend: t6 }), i = yl({ inputs: { x: a }, backend: t6 }), p = Ht({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(n), t6.disposeIntermediateTensorInfo(s), t6.disposeIntermediateTensorInfo(a), t6.disposeIntermediateTensorInfo(i), p; + let n = Io({ inputs: { input: o }, backend: t10 }), s = A_({ inputs: { x: n }, backend: t10 }), a = Ys({ inputs: { input: o }, backend: t10 }), i = Sl({ inputs: { x: a }, backend: t10 }), p = Ht({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; } else - return xl({ backend: t6, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); + return Cl({ backend: t10, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); } -var N_ = { kernelName: Is, backendName: "cpu", kernelFunc: k_ }; -function AS(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o; +var R_ = { kernelName: ks, backendName: "cpu", kernelFunc: A_ }; +function WS(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return Jp({ inputs: { input: e[0] }, backend: t6, attrs: { dim: n } }); + return ec({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = Jp({ inputs: { input: c }, backend: t6, attrs: { dim: n } }); + let l = ec({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); return i.push(l), l; - }), u = Pi({ inputs: p, backend: t6, attrs: { axis: n } }); - return i.forEach((c) => t6.disposeIntermediateTensorInfo(c)), u; + }), u = Mi({ inputs: p, backend: t10, attrs: { axis: n } }); + return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u; } -var T_ = { kernelName: vs, backendName: "cpu", kernelFunc: AS }; -function TX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; - K(n, "pad"); - let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = t6.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d); +var D_ = { kernelName: Ns, backendName: "cpu", kernelFunc: WS }; +function qX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; + j(n, "pad"); + let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = t10.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d); a !== 0 && g.fill(a); for (let b = 0; b < c; b++) { - let w = y.indexToLoc(b, l, m).map((_, $) => _ + p[$]), k = y.locToIndex(w, f, h); + let w = y.indexToLoc(b, l, m).map((_, E) => _ + p[E]), k = y.locToIndex(w, f, h); g[k] = u[b]; } - return { dataId: t6.write(g, i, n.dtype), shape: i, dtype: n.dtype }; + return { dataId: t10.write(g, i, n.dtype), shape: i, dtype: n.dtype }; } -var kf = { kernelName: $n, backendName: "cpu", kernelFunc: TX }; -var _X = Be((r, e) => Math.pow(r, e)); -var EX = Qe(An, _X); -var __ = { kernelName: An, backendName: "cpu", kernelFunc: EX }; -function $X(r) { - let { inputs: e, backend: t6, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t6.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t6.data.get(s.dataId).values, l = t6.data.get(a.dataId).values, [m, d, f] = df(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t6.makeTensorInfo([x.length], "int32", x)), g = t6.makeTensorInfo(f, s.dtype, d); +var kf = { kernelName: En, backendName: "cpu", kernelFunc: qX }; +var jX = Be((r, e) => Math.pow(r, e)); +var XX = Qe(An, jX); +var F_ = { kernelName: An, backendName: "cpu", kernelFunc: XX }; +function YX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, d, f] = df(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var E_ = { kernelName: wp, backendName: "cpu", kernelFunc: $X }; -function AX(r) { - let { inputs: e, backend: t6 } = r, { starts: o, limits: n, deltas: s } = e, a = t6.data.get(o.dataId).values, i = t6.data.get(n.dataId).values, p = t6.data.get(s.dataId).values, [u, c] = ff(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t6.makeTensorInfo([u.length], "int32", u), m = t6.makeTensorInfo([c.length], o.dtype, c); +var O_ = { kernelName: Ip, backendName: "cpu", kernelFunc: YX }; +function QX(r) { + let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = ff(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var $_ = { kernelName: Ip, backendName: "cpu", kernelFunc: AX }; -function RX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t6.data.get(n.dataId).values, c = t6.data.get(s.dataId).values, l = t6.data.get(a.dataId).values, m = i.map((g) => t6.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = hf(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); - return t6.makeTensorInfo(f, s.dtype, h); +var P_ = { kernelName: vp, backendName: "cpu", kernelFunc: QX }; +function ZX(r) { + let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, m = i.map((g) => t10.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = hf(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); + return t10.makeTensorInfo(f, s.dtype, h); } -var A_ = { kernelName: vp, backendName: "cpu", kernelFunc: RX }; -function FX(r) { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, dtype: s, step: a } = t6, i = Iu(o, n, a, s); +var M_ = { kernelName: kp, backendName: "cpu", kernelFunc: ZX }; +function JX(r) { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, dtype: s, step: a } = t10, i = _u(o, n, a, s); return e.makeTensorInfo([i.length], s, i); } -var R_ = { kernelName: ks, backendName: "cpu", kernelFunc: FX }; -var DX = Ie(Dn, (r) => 1 / r); -var F_ = { kernelName: Dn, backendName: "cpu", kernelFunc: DX }; -function OX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; - K(n, "resizeBilinear"); - let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t6.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, w = x[0] / b[0], k = x[1] / b[1]; +var L_ = { kernelName: Ts, backendName: "cpu", kernelFunc: JX }; +var e5 = we(Fn, (r) => 1 / r); +var B_ = { kernelName: Fn, backendName: "cpu", kernelFunc: e5 }; +function t5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; + j(n, "resizeBilinear"); + let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, w = x[0] / b[0], k = x[1] / b[1]; for (let _ = 0; _ < l; _++) - for (let $ = 0; $ < u; $++) { + for (let E = 0; E < u; E++) { let A; - a ? A = w * ($ + 0.5) - 0.5 : A = w * $; + a ? A = w * (E + 0.5) - 0.5 : A = w * E; let R = Math.max(0, Math.floor(A)), D = A - R, P = Math.min(m - 1, Math.ceil(A)), M = _ * p[0] + R * p[1], L = _ * p[0] + P * p[1]; - for (let W = 0; W < c; W++) { - let V; - a ? V = k * (W + 0.5) - 0.5 : V = k * W; - let U = Math.max(0, Math.floor(V)), q = V - U, H = Math.min(d - 1, Math.ceil(V)), j = M + U * p[2], X = L + U * p[2], Z = M + H * p[2], ee = L + H * p[2]; - for (let Y = 0; Y < f; Y++) { - let J = h[j + Y], ie = h[X + Y], pe = h[Z + Y], he = h[ee + Y], we = J + (pe - J) * q, ve = ie + (he - ie) * q, $e = we + (ve - we) * D; - g[C++] = $e; + for (let V = 0; V < c; V++) { + let z; + a ? z = k * (V + 0.5) - 0.5 : z = k * V; + let U = Math.max(0, Math.floor(z)), K = z - U, H = Math.min(d - 1, Math.ceil(z)), q = M + U * p[2], Z = L + U * p[2], ee = M + H * p[2], oe = L + H * p[2]; + for (let J = 0; J < f; J++) { + let te = h[q + J], ie = h[Z + J], ce = h[ee + J], ye = h[oe + J], Ne = te + (ce - te) * K, Ie = ie + (ye - ie) * K, Ae = Ne + (Ie - Ne) * D; + g[C++] = Ae; } } } - return t6.makeTensorInfo([l, u, c, f], "float32", g); + return t10.makeTensorInfo([l, u, c, f], "float32", g); } -var D_ = { kernelName: Mn, backendName: "cpu", kernelFunc: OX }; -function PX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; - K([s, n], "resizeBilinearGrad"); - let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], C = t6.data.get(s.dataId).values, w = 0; +var V_ = { kernelName: Mn, backendName: "cpu", kernelFunc: t5 }; +function r5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; + j([s, n], "resizeBilinearGrad"); + let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], C = t10.data.get(s.dataId).values, w = 0; for (let k = 0; k < p; k++) { let _ = k * i[0]; - for (let $ = 0; $ < m; $++) { - let A = $ * x, R = Math.floor(A), D = Math.min(Math.ceil(A), u - 1), P = _ + R * i[1], M = _ + D * i[1], L = A - R, W = 1 - L; - for (let V = 0; V < d; V++) { - let U = V * b, q = Math.floor(U), H = Math.min(Math.ceil(U), c - 1), j = U - q, X = 1 - j, Z = P + q * i[2], ee = P + H * i[2], Y = M + q * i[2], J = M + H * i[2], ie = W * X, pe = W * j, he = L * X, we = L * j; - for (let ve = 0; ve < l; ve++) { - let $e = C[w++]; - f[Z + ve] += $e * ie, f[ee + ve] += $e * pe, f[Y + ve] += $e * he, f[J + ve] += $e * we; + for (let E = 0; E < m; E++) { + let A = E * x, R = Math.floor(A), D = Math.min(Math.ceil(A), u - 1), P = _ + R * i[1], M = _ + D * i[1], L = A - R, V = 1 - L; + for (let z = 0; z < d; z++) { + let U = z * b, K = Math.floor(U), H = Math.min(Math.ceil(U), c - 1), q = U - K, Z = 1 - q, ee = P + K * i[2], oe = P + H * i[2], J = M + K * i[2], te = M + H * i[2], ie = V * Z, ce = V * q, ye = L * Z, Ne = L * q; + for (let Ie = 0; Ie < l; Ie++) { + let Ae = C[w++]; + f[ee + Ie] += Ae * ie, f[oe + Ie] += Ae * ce, f[J + Ie] += Ae * ye, f[te + Ie] += Ae * Ne; } } } } - return t6.makeTensorInfo([p, c, u, l], "float32", f); + return t10.makeTensorInfo([p, c, u, l], "float32", f); } -var O_ = { kernelName: $m, backendName: "cpu", kernelFunc: PX }; -function MX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; - K(n, "resizeNearestNeighbor"); - let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t6.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = x[0] / b[0], w = x[1] / b[1], k = 0; +var z_ = { kernelName: Am, backendName: "cpu", kernelFunc: r5 }; +function o5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o; + j(n, "resizeNearestNeighbor"); + let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = x[0] / b[0], w = x[1] / b[1], k = 0; for (let _ = 0; _ < l; _++) { - let $ = _ * p[0]; + let E = _ * p[0]; for (let A = 0; A < u; A++) { let R = a ? C * (A + 0.5) : C * A, D = Math.min(m - 1, s ? Math.round(R) : Math.floor(R)); a && (D = Math.max(0, D)); - let P = $ + D * p[1]; + let P = E + D * p[1]; for (let M = 0; M < c; M++) { - let L = a ? w * (M + 0.5) : w * M, W = Math.min(d - 1, s ? Math.round(L) : Math.floor(L)); - a && (W = Math.max(0, W)); - let V = P + W * p[2]; + let L = a ? w * (M + 0.5) : w * M, V = Math.min(d - 1, s ? Math.round(L) : Math.floor(L)); + a && (V = Math.max(0, V)); + let z = P + V * p[2]; for (let U = 0; U < f; U++) { - let q = h[V + U]; - g[k++] = q; + let K = h[z + U]; + g[k++] = K; } } } } - return t6.makeTensorInfo([l, u, c, f], n.dtype, g); + return t10.makeTensorInfo([l, u, c, f], n.dtype, g); } -var P_ = { kernelName: Pn, backendName: "cpu", kernelFunc: MX }; -function LX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; - K([s, n], "resizeNearestNeighborGrad"); - let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t6.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], C = x[0] / b[0], w = x[1] / b[1], k = 1 / C, _ = 1 / w, $ = Math.ceil(k) * 2 + 2, A = Math.ceil(_) * 2 + 2; +var W_ = { kernelName: Pn, backendName: "cpu", kernelFunc: o5 }; +function n5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o; + j([s, n], "resizeNearestNeighborGrad"); + let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t10.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], C = x[0] / b[0], w = x[1] / b[1], k = 1 / C, _ = 1 / w, E = Math.ceil(k) * 2 + 2, A = Math.ceil(_) * 2 + 2; for (let R = 0; R < u; R++) { let D = R * i[0]; for (let P = 0; P < c; P++) { - let M = D + P * i[1], L = Math.floor(P * k), W = Math.floor(L - $ / 2); - for (let V = 0; V < l; V++) { - let U = M + V * i[2], q = Math.floor(V * _), H = Math.floor(q - A / 2); - for (let j = 0; j < m; j++) { - let X = 0; - for (let Z = 0; Z < $; Z++) { - let ee = Z + W; - if (ee < 0 || ee >= d) + let M = D + P * i[1], L = Math.floor(P * k), V = Math.floor(L - E / 2); + for (let z = 0; z < l; z++) { + let U = M + z * i[2], K = Math.floor(z * _), H = Math.floor(K - A / 2); + for (let q = 0; q < m; q++) { + let Z = 0; + for (let ee = 0; ee < E; ee++) { + let oe = ee + V; + if (oe < 0 || oe >= d) continue; - let Y = D + ee * p[1], J = ee * C, ie = Math.min(c - 1, a ? Math.round(J) : Math.floor(J)); + let J = D + oe * p[1], te = oe * C, ie = Math.min(c - 1, a ? Math.round(te) : Math.floor(te)); if (P === ie) - for (let pe = 0; pe < A; pe++) { - let he = pe + H; - if (he < 0 || he >= f) + for (let ce = 0; ce < A; ce++) { + let ye = ce + H; + if (ye < 0 || ye >= f) continue; - let we = Y + he * p[2], ve = he * w, $e = Math.min(l - 1, a ? Math.round(ve) : Math.floor(ve)); - V === $e && (X += g[we + j]); + let Ne = J + ye * p[2], Ie = ye * w, Ae = Math.min(l - 1, a ? Math.round(Ie) : Math.floor(Ie)); + z === Ae && (Z += g[Ne + q]); } } - h[U + j] = X; + h[U + q] = Z; } } } } - return t6.makeTensorInfo(n.shape, n.dtype, h); + return t10.makeTensorInfo(n.shape, n.dtype, h); } -var M_ = { kernelName: Em, backendName: "cpu", kernelFunc: LX }; -function BX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dims: s } = o; - K(n, "reverse"); +var U_ = { kernelName: Em, backendName: "cpu", kernelFunc: n5 }; +function s5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o; + j(n, "reverse"); let a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return ar({ inputs: { x: n }, backend: t6 }); - let p = new st(n.shape, n.dtype), u = t6.bufferSync(n); + return ir({ inputs: { x: n }, backend: t10 }); + let p = new st(n.shape, n.dtype), u = t10.bufferSync(n); for (let c = 0; c < p.size; c++) { let l = p.indexToLoc(c), m = l.slice(); i.forEach((d) => m[d] = n.shape[d] - 1 - m[d]), p.set(u.get(...m), ...l); } - return t6.makeTensorInfo(p.shape, p.dtype, p.values); + return t10.makeTensorInfo(p.shape, p.dtype, p.values); } -var L_ = { kernelName: Bn, backendName: "cpu", kernelFunc: BX }; -var B_ = { kernelName: es, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t6, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = S.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; +var G_ = { kernelName: Bn, backendName: "cpu", kernelFunc: s5 }; +var H_ = { kernelName: ts, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = S.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; for (let w = 0; w < u; w++) { let k = w * l * c * m; for (let _ = 0; _ < c; _++) { - let $ = _ * (l * m); + let E = _ * (l * m); for (let A = 0; A < l; A++) { let R = A * m; for (let D = 0; D < m; D++) { - let P = [u, _, A, D], M = P[2], L = P[1], W = (M - d) * x - (L - f) * g, V = (M - d) * g + (L - f) * x; - W = Math.round(W + d), V = Math.round(V + f); + let P = [u, _, A, D], M = P[2], L = P[1], V = (M - d) * x - (L - f) * g, z = (M - d) * g + (L - f) * x; + V = Math.round(V + d), z = Math.round(z + f); let U = s; - if (typeof s != "number" && (D === 3 ? U = h : U = s[D]), W >= 0 && W < l && V >= 0 && V < c) { - let H = V * (l * m), j = W * m, X = k + H + j + D; - U = b[X]; + if (typeof s != "number" && (D === 3 ? U = h : U = s[D]), V >= 0 && V < l && z >= 0 && z < c) { + let H = z * (l * m), q = V * m, Z = k + H + q + D; + U = b[Z]; } - let q = k + $ + R + D; - p[q] = U; + let K = k + E + R + D; + p[K] = U; } } } } return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -var VX = Ie(Ca, (r) => { +var a5 = we(Vn, (r) => { let e = Math.floor(r); return r - e < 0.5 ? Math.floor(r) : r - e > 0.5 ? Math.ceil(r) : e % 2 === 0 ? e : e + 1; }); -var V_ = { kernelName: Ca, backendName: "cpu", kernelFunc: VX }; -function zX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = true, d = t6.bufferSync(n), f = t6.bufferSync(s), h = Ma(d, f, a, l, u, p, i, c, 0, m); - return t6.makeTensorInfo(a, h.dtype, h.values); +var K_ = { kernelName: Vn, backendName: "cpu", kernelFunc: a5 }; +function i5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = true, d = t10.bufferSync(n), f = t10.bufferSync(s), h = Va(d, f, a, l, u, p, i, c, 0, m); + return t10.makeTensorInfo(a, h.dtype, h.values); } -var z_ = { kernelName: zn, backendName: "cpu", kernelFunc: zX }; -function WX(r, e) { - let t6 = 0, o = r.length, n = 0; - for (; t6 < o; ) - n = Math.floor((t6 + o) / 2), r[n] < e ? t6 = n + 1 : o = n; +var q_ = { kernelName: Wn, backendName: "cpu", kernelFunc: i5 }; +function u5(r, e) { + let t10 = 0, o = r.length, n = 0; + for (; t10 < o; ) + n = Math.floor((t10 + o) / 2), r[n] < e ? t10 = n + 1 : o = n; return o; } -function UX(r, e) { - let t6 = 0, o = r.length, n = 0; - for (; t6 < o; ) - n = Math.floor((t6 + o) / 2), r[n] <= e ? t6 = n + 1 : o = n; +function p5(r, e) { + let t10 = 0, o = r.length, n = 0; + for (; t10 < o; ) + n = Math.floor((t10 + o) / 2), r[n] <= e ? t10 = n + 1 : o = n; return o; } -function W_(r, e, t6, o, n, s) { - let a = y.getArrayFromDType("int32", t6 * n); - for (let i = 0; i < t6; ++i) { +function j_(r, e, t10, o, n, s) { + let a = y.getArrayFromDType("int32", t10 * n); + for (let i = 0; i < t10; ++i) { let p = r.slice(i * o, (i + 1) * o), u = i * n; for (let c = 0; c < n; ++c) - a[u + c] = s === "left" ? WX(p, e[c + u]) : UX(p, e[c + u]); + a[u + c] = s === "left" ? u5(p, e[c + u]) : p5(p, e[c + u]); } return a; } -function GX(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t6.data.get(n.dataId).values, p = t6.data.get(s.dataId).values, u = W_(i, p, n.shape[0], n.shape[1], s.shape[1], a); - return t6.makeTensorInfo(s.shape, "int32", u); +function c5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = j_(i, p, n.shape[0], n.shape[1], s.shape[1], a); + return t10.makeTensorInfo(s.shape, "int32", u); } -var U_ = { kernelName: ii, backendName: "cpu", kernelFunc: GX }; -function HX(r) { - let { inputs: e, backend: t6 } = r, { condition: o, t: n, e: s } = e; - K([o, n, s], "select"); - let a = o.shape.length, i = t6.data.get(o.dataId).values, p = t6.data.get(n.dataId).values, u = t6.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); +var X_ = { kernelName: fi, backendName: "cpu", kernelFunc: c5 }; +function l5(r) { + let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e; + j([o, n, s], "select"); + let a = o.shape.length, i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); for (let f = 0; f < i.length; f++) for (let h = 0; h < d; h++) i[f] === 1 ? l[m++] = p[f] : l[m++] = u[f]; - return t6.makeTensorInfo(n.shape, c, l); + return t10.makeTensorInfo(n.shape, c, l); } -var G_ = { kernelName: Ts, backendName: "cpu", kernelFunc: HX }; -var qX = S.SELU_SCALEALPHA; -var KX = S.SELU_SCALE; -var jX = Ie(Xi, (r) => r >= 0 ? KX * r : qX * (Math.exp(r) - 1)); -var H_ = { kernelName: Xi, backendName: "cpu", kernelFunc: jX }; -var XX = Ie(Yi, (r) => r < 0 ? -1 : r > 0 ? 1 : 0); -var q_ = { kernelName: Yi, backendName: "cpu", kernelFunc: XX }; -var YX = Ie(Wn, (r) => Math.sin(r)); -var K_ = { kernelName: Wn, backendName: "cpu", kernelFunc: YX }; -var QX = Ie(Sa, (r) => Math.sinh(r)); -var j_ = { kernelName: Sa, backendName: "cpu", kernelFunc: QX }; -var ZX = 11920928955078125e-23; -var X_ = Math.log(ZX) + 2; -var JX = Ie(Qi, (r) => { - let e = r > -X_, t6 = r < X_, o = Math.exp(r), n; - return t6 ? n = o : e ? n = r : n = Math.log(1 + o), n; +var Y_ = { kernelName: $s, backendName: "cpu", kernelFunc: l5 }; +var m5 = S.SELU_SCALEALPHA; +var d5 = S.SELU_SCALE; +var f5 = we(Ca, (r) => r >= 0 ? d5 * r : m5 * (Math.exp(r) - 1)); +var Q_ = { kernelName: Ca, backendName: "cpu", kernelFunc: f5 }; +var h5 = we(wa, (r) => r < 0 ? -1 : r > 0 ? 1 : 0); +var Z_ = { kernelName: wa, backendName: "cpu", kernelFunc: h5 }; +var g5 = we(Un, (r) => Math.sin(r)); +var J_ = { kernelName: Un, backendName: "cpu", kernelFunc: g5 }; +var x5 = we(Sa, (r) => Math.sinh(r)); +var e$ = { kernelName: Sa, backendName: "cpu", kernelFunc: x5 }; +var y5 = 11920928955078125e-23; +var t$ = Math.log(y5) + 2; +var b5 = we(Ia, (r) => { + let e = r > -t$, t10 = r < t$, o = Math.exp(r), n; + return t10 ? n = o : e ? n = r : n = Math.log(1 + o), n; }); -var Y_ = { kernelName: Qi, backendName: "cpu", kernelFunc: JX }; -function e5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; - K([n], "spaceToBatchND"); +var r$ = { kernelName: Ia, backendName: "cpu", kernelFunc: b5 }; +function C5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; + j([n], "spaceToBatchND"); let i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = kf.kernelFunc({ inputs: { x: n }, backend: t6, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Me({ inputs: { x: u }, backend: t6, attrs: { shape: c } }), b = Ct({ inputs: { x: h }, backend: t6, attrs: { perm: l } }), k = Me({ inputs: { x: b }, backend: t6, attrs: { shape: m } }); - return t6.disposeIntermediateTensorInfo(u), t6.disposeIntermediateTensorInfo(h), t6.disposeIntermediateTensorInfo(b), k; + let u = kf.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Le({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = St({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Le({ inputs: { x: b }, backend: t10, attrs: { shape: m } }); + return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(b), k; } -var Q_ = { kernelName: Es, backendName: "cpu", kernelFunc: e5 }; -function t5(r) { - let { inputs: e, backend: t6 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; +var o$ = { kernelName: As, backendName: "cpu", kernelFunc: C5 }; +function S5(r) { + let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -14551,12 +14546,12 @@ function t5(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = t6.data.get(o.dataId).values, p = t6.data.get(n.dataId).values, u = t6.data.get(s.dataId).values, c = t6.data.get(a.dataId).values[0], [l, m, d, f, h] = gf(i, o.shape, o.dtype, p, n.dtype, u, c); - return [t6.makeTensorInfo(m, o.dtype, l), t6.makeTensorInfo([m[0]], n.dtype, d), t6.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t6.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; + let i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = t10.data.get(a.dataId).values[0], [l, m, d, f, h] = gf(i, o.shape, o.dtype, p, n.dtype, u, c); + return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var Z_ = { kernelName: ui, backendName: "cpu", kernelFunc: t5 }; -function r5(r) { - let { inputs: e, backend: t6 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; +var n$ = { kernelName: hi, backendName: "cpu", kernelFunc: S5 }; +function w5(r) { + let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -14565,12 +14560,12 @@ function r5(r) { ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(t6.data.get(n.dataId).values), i = t6.data.get(o.dataId).values, p = Array.from(t6.data.get(s.dataId).values), [u, c, l] = xf(i, o.shape, o.dtype, a, p); - return [t6.makeTensorInfo(c, o.dtype, u), t6.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; + let a = Array.from(t10.data.get(n.dataId).values), i = t10.data.get(o.dataId).values, p = Array.from(t10.data.get(s.dataId).values), [u, c, l] = xf(i, o.shape, o.dtype, a, p); + return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var J_ = { kernelName: wa, backendName: "cpu", kernelFunc: r5 }; -function o5(r) { - let { inputs: e, backend: t6 } = r, { data: o, indices: n, segmentIds: s } = e; +var s$ = { kernelName: va, backendName: "cpu", kernelFunc: w5 }; +function I5(r) { + let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -14581,12 +14576,12 @@ function o5(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = t6.data.get(o.dataId).values, i = t6.data.get(n.dataId).values, p = t6.data.get(s.dataId).values, [u, c] = Yp(a, o.shape, o.dtype, i, p, true); - return t6.makeTensorInfo(c, o.dtype, u); + let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = Qp(a, o.shape, o.dtype, i, p, true); + return t10.makeTensorInfo(c, o.dtype, u); } -var eE = { kernelName: pi, backendName: "cpu", kernelFunc: o5 }; -function n5(r) { - let { inputs: e, backend: t6 } = r, { data: o, indices: n, segmentIds: s } = e; +var a$ = { kernelName: gi, backendName: "cpu", kernelFunc: I5 }; +function v5(r) { + let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -14597,233 +14592,233 @@ function n5(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = t6.data.get(o.dataId).values, i = t6.data.get(n.dataId).values, p = t6.data.get(s.dataId).values, [u, c] = Yp(a, o.shape, o.dtype, i, p); - return t6.makeTensorInfo(c, o.dtype, u); + let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = Qp(a, o.shape, o.dtype, i, p); + return t10.makeTensorInfo(c, o.dtype, u); } -var tE = { kernelName: ci, backendName: "cpu", kernelFunc: n5 }; -function s5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false, f = t6.bufferSync(n), h; +var i$ = { kernelName: xi, backendName: "cpu", kernelFunc: v5 }; +function k5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false, f = t10.bufferSync(n), h; switch (s.dtype) { case "bool": { - let g = t6.bufferSync(s), x = Boolean(t6.data.get(a.dataId).values[0]); - h = Ma(f, g, i, m, c, u, p, l, x, d); + let g = t10.bufferSync(s), x = Boolean(t10.data.get(a.dataId).values[0]); + h = Va(f, g, i, m, c, u, p, l, x, d); break; } case "float32": { - let g = t6.bufferSync(s), x = t6.data.get(a.dataId).values[0]; - h = Ma(f, g, i, m, c, u, p, l, x, d); + let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0]; + h = Va(f, g, i, m, c, u, p, l, x, d); break; } case "int32": { - let g = t6.bufferSync(s), x = t6.data.get(a.dataId).values[0]; - h = Ma(f, g, i, m, c, u, p, l, x, d); + let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0]; + h = Va(f, g, i, m, c, u, p, l, x, d); break; } case "string": { - let g = t6.bufferSync(s), x = y.decodeString(t6.data.get(a.dataId).values[0]); - h = Ma(f, g, i, m, c, u, p, l, x, d); + let g = t10.bufferSync(s), x = y.decodeString(t10.data.get(a.dataId).values[0]); + h = Va(f, g, i, m, c, u, p, l, x, d); break; } default: throw new Error(`Unsupported type ${s.dtype}`); } - return t6.makeTensorInfo(i, h.dtype, h.values); + return t10.makeTensorInfo(i, h.dtype, h.values); } -var rE = { kernelName: li, backendName: "cpu", kernelFunc: s5 }; -function a5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var u$ = { kernelName: yi, backendName: "cpu", kernelFunc: k5 }; +function N5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; - let d = No({ inputs: { x: n }, backend: t6, attrs: { begin: u, size: m } }); + let d = To({ inputs: { x: n }, backend: t10, attrs: { begin: u, size: m } }); return u[i] += l, d; }); } -var oE = { kernelName: $s, backendName: "cpu", kernelFunc: a5 }; -var nE = { kernelName: mi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { x: t6 } = r, o = e; - K(t6, "square"); - let n = o.data.get(t6.dataId).values, s = new Float32Array(n.length); +var p$ = { kernelName: Rs, backendName: "cpu", kernelFunc: N5 }; +var c$ = { kernelName: bi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { x: t10 } = r, o = e; + j(t10, "square"); + let n = o.data.get(t10.dataId).values, s = new Float32Array(n.length); for (let i = 0; i < n.length; ++i) { let p = n[i]; s[i] = p * p; } - return { dataId: o.write(s, t6.shape, t6.dtype), shape: t6.shape, dtype: t6.dtype }; + return { dataId: o.write(s, t10.shape, t10.dtype), shape: t10.shape, dtype: t10.dtype }; } }; -var i5 = Ie(Ds, (r, e) => { - let t6 = e; - return isNaN(r) ? NaN : r > 0 ? 1 : t6.alpha; +var T5 = we(fo, (r, e) => { + let t10 = e; + return isNaN(r) ? NaN : r > 0 ? 1 : t10.alpha; }); -var sE = { kernelName: Ds, backendName: "cpu", kernelFunc: i5 }; -function u5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; - K(n, "stridedSlice"); +var l$ = { kernelName: fo, backendName: "cpu", kernelFunc: T5 }; +function _5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; + j(n, "stridedSlice"); let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = Me({ inputs: { x: n }, backend: t6, attrs: { shape: f } }); + k = Le({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ut.computeOutShape(b, C, w), $ = No({ inputs: { x: n }, backend: t6, attrs: { begin: b, size: _ } }); - k = Me({ inputs: { x: $ }, backend: t6, attrs: { shape: f } }), t6.disposeIntermediateTensorInfo($); + let _ = ut.computeOutShape(b, C, w), E = To({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } }); + k = Le({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(E); } else { - let _ = t6.bufferSync(n), $ = yf(d, _, w, b); - k = t6.makeTensorInfo(f, $.dtype, $.values); + let _ = t10.bufferSync(n), E = yf(d, _, w, b); + k = t10.makeTensorInfo(f, E.dtype, E.values); } return k; } -var aE = { kernelName: jn, backendName: "cpu", kernelFunc: u5 }; -function p5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t6.data.get(c.dataId).values, d = t6.data.get(l.dataId).values, [f, h] = ku(m, d, n, s, a, i, p, u); - return [t6.makeTensorInfo([f.length], "string", f), t6.makeTensorInfo(l.shape, "int32", h)]; +var m$ = { kernelName: Xn, backendName: "cpu", kernelFunc: _5 }; +function $5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.data.get(c.dataId).values, d = t10.data.get(l.dataId).values, [f, h] = Eu(m, d, n, s, a, i, p, u); + return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; } -var iE = { kernelName: As, backendName: "cpu", kernelFunc: p5 }; -function c5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; +var d$ = { kernelName: Ds, backendName: "cpu", kernelFunc: $5 }; +function E5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = t6.data.get(s.dataId).values, p = t6.data.get(a.dataId).values[0], [u, c, l] = Nu(i, p, n), m = c.length; - return [t6.makeTensorInfo([m, 2], "int32", u), t6.makeTensorInfo([m], "string", c), t6.makeTensorInfo([2], "int32", new Int32Array(l))]; + let i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values[0], [u, c, l] = Au(i, p, n), m = c.length; + return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var uE = { kernelName: di, backendName: "cpu", kernelFunc: c5 }; -function l5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; +var f$ = { kernelName: Ci, backendName: "cpu", kernelFunc: E5 }; +function A5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = t6.data.get(s.dataId).values, i = Tu(a, n); - return t6.makeTensorInfo(s.shape, "int32", i); + let a = t10.data.get(s.dataId).values, i = Ru(a, n); + return t10.makeTensorInfo(s.shape, "int32", i); } -var pE = { kernelName: fi, backendName: "cpu", kernelFunc: l5 }; -var m5 = Ie(Yn, (r) => Math.tan(r)); -var cE = { kernelName: Yn, backendName: "cpu", kernelFunc: m5 }; -var d5 = Ie(Qn, (r) => Math.tanh(r)); -var lE = { kernelName: Qn, backendName: "cpu", kernelFunc: d5 }; -function f5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reps: s } = o; - K(n, "tile"); - let a = bf(t6.bufferSync(n), s); - return t6.makeTensorInfo(a.shape, a.dtype, a.values); +var h$ = { kernelName: Si, backendName: "cpu", kernelFunc: A5 }; +var R5 = we(Qn, (r) => Math.tan(r)); +var g$ = { kernelName: Qn, backendName: "cpu", kernelFunc: R5 }; +var D5 = we(Zn, (r) => Math.tanh(r)); +var x$ = { kernelName: Zn, backendName: "cpu", kernelFunc: D5 }; +function F5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; + j(n, "tile"); + let a = bf(t10.bufferSync(n), s); + return t10.makeTensorInfo(a.shape, a.dtype, a.values); } -var mE = { kernelName: to, backendName: "cpu", kernelFunc: f5 }; -function h5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o; - K(n, "topk"); - let i = t6.data.get(n.dataId).values, [p, u] = Cf(i, n.shape, n.dtype, s, a); - return [t6.makeTensorInfo(p.shape, p.dtype, p.values), t6.makeTensorInfo(u.shape, u.dtype, u.values)]; +var y$ = { kernelName: to, backendName: "cpu", kernelFunc: F5 }; +function O5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o; + j(n, "topk"); + let i = t10.data.get(n.dataId).values, [p, u] = Cf(i, n.shape, n.dtype, s, a); + return [t10.makeTensorInfo(p.shape, p.dtype, p.values), t10.makeTensorInfo(u.shape, u.dtype, u.values)]; } -var dE = { kernelName: Zn, backendName: "cpu", kernelFunc: h5 }; -function g5(r) { - let { inputs: e, attrs: t6, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t6, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], w = x[2], k = y.computeStrides(g), _ = k[0], $ = k[1], A = k[2], R = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); +var b$ = { kernelName: Jn, backendName: "cpu", kernelFunc: O5 }; +function P5(r) { + let { inputs: e, attrs: t10, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t10, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], w = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], A = k[2], R = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); R.fill(p); let D = o.data.get(n.dataId).values, P = o.data.get(s.dataId).values; for (let L = 0; L < c; ++L) { - let W = s.shape[0] === 1 ? P : P.subarray(L * 8, L * 8 + 8); - for (let V = 0; V < f; ++V) + let V = s.shape[0] === 1 ? P : P.subarray(L * 8, L * 8 + 8); + for (let z = 0; z < f; ++z) for (let U = 0; U < h; ++U) - for (let q = 0; q < d; ++q) { - let H, j = W[6] * U + W[7] * V + 1; - if (j === 0) + for (let K = 0; K < d; ++K) { + let H, q = V[6] * U + V[7] * z + 1; + if (q === 0) continue; - let X = (W[0] * U + W[1] * V + W[2]) / j, Z = (W[3] * U + W[4] * V + W[5]) / j, ee = fE(X, m, i), Y = fE(Z, l, i); + let Z = (V[0] * U + V[1] * z + V[2]) / q, ee = (V[3] * U + V[4] * z + V[5]) / q, oe = C$(Z, m, i), J = C$(ee, l, i); switch (a) { case "nearest": - H = S5(D, l, m, b, C, w, L, Y, ee, q, p); + H = z5(D, l, m, b, C, w, L, J, oe, K, p); break; case "bilinear": - H = w5(D, l, m, b, C, w, L, Y, ee, q, p); + H = W5(D, l, m, b, C, w, L, J, oe, K, p); break; default: throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${a}`); } - let J = L * _ + V * $ + U * A + q; - R[J] = H; + let te = L * _ + z * E + U * A + K; + R[te] = H; } return o.makeTensorInfo(g, n.dtype, R); } return { dataId: o.write(R, g, n.dtype), shape: n.shape, dtype: n.dtype }; } -var hE = { kernelName: Jn, backendName: "cpu", kernelFunc: g5 }; -function fE(r, e, t6) { - switch (t6) { +var S$ = { kernelName: es, backendName: "cpu", kernelFunc: P5 }; +function C$(r, e, t10) { + switch (t10) { case "reflect": - return x5(r, e); + return M5(r, e); case "wrap": - return y5(r, e); + return L5(r, e); case "nearest": - return C5(r, e); + return V5(r, e); case "constant": default: - return b5(r, e); + return B5(r, e); } } -function x5(r, e) { - let t6 = r; - if (t6 < 0) +function M5(r, e) { + let t10 = r; + if (t10 < 0) if (e <= 1) - t6 = 0; + t10 = 0; else { let o = 2 * e; - t6 < o && (t6 = o * Math.trunc(-t6 / o) + t6), t6 = t6 < -e ? t6 + o : -t6 - 1; + t10 < o && (t10 = o * Math.trunc(-t10 / o) + t10), t10 = t10 < -e ? t10 + o : -t10 - 1; } - else if (t6 > e - 1) + else if (t10 > e - 1) if (e <= 1) - t6 = 0; + t10 = 0; else { let o = 2 * e; - t6 -= o * Math.trunc(t6 / o), t6 >= e && (t6 = o - t6 - 1); + t10 -= o * Math.trunc(t10 / o), t10 >= e && (t10 = o - t10 - 1); } - return y.clamp(0, t6, e - 1); + return y.clamp(0, t10, e - 1); } -function y5(r, e) { - let t6 = r; - if (t6 < 0) +function L5(r, e) { + let t10 = r; + if (t10 < 0) if (e <= 1) - t6 = 0; + t10 = 0; else { let o = e - 1; - t6 += e * (Math.trunc(-t6 / o) + 1); + t10 += e * (Math.trunc(-t10 / o) + 1); } - else if (t6 > e - 1) + else if (t10 > e - 1) if (e <= 1) - t6 = 0; + t10 = 0; else { let o = e - 1; - t6 -= e * Math.trunc(t6 / o); + t10 -= e * Math.trunc(t10 / o); } - return y.clamp(0, t6, e - 1); + return y.clamp(0, t10, e - 1); } -function b5(r, e) { +function B5(r, e) { return r; } -function C5(r, e) { +function V5(r, e) { return y.clamp(0, r, e - 1); } -function bl(r, e, t6, o, n, s, a, i, p, u, c) { +function wl(r, e, t10, o, n, s, a, i, p, u, c) { let l = a * o + i * n + p * s + u; - return 0 <= i && i < e && 0 <= p && p < t6 ? r[l] : c; + return 0 <= i && i < e && 0 <= p && p < t10 ? r[l] : c; } -function S5(r, e, t6, o, n, s, a, i, p, u, c) { +function z5(r, e, t10, o, n, s, a, i, p, u, c) { let l = Math.round(i), m = Math.round(p); - return bl(r, e, t6, o, n, s, a, l, m, u, c); + return wl(r, e, t10, o, n, s, a, l, m, u, c); } -function w5(r, e, t6, o, n, s, a, i, p, u, c) { - let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * bl(r, e, t6, o, n, s, a, l, m, u, c) + (p - m) * bl(r, e, t6, o, n, s, a, l, f, u, c), g = (f - p) * bl(r, e, t6, o, n, s, a, d, m, u, c) + (p - m) * bl(r, e, t6, o, n, s, a, d, f, u, c); +function W5(r, e, t10, o, n, s, a, i, p, u, c) { + let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * wl(r, e, t10, o, n, s, a, l, m, u, c) + (p - m) * wl(r, e, t10, o, n, s, a, l, f, u, c), g = (f - p) * wl(r, e, t10, o, n, s, a, d, m, u, c) + (p - m) * wl(r, e, t10, o, n, s, a, d, f, u, c); return (d - i) * h + (i - l) * g; } -function I5(r) { - let { inputs: e, attrs: t6, backend: o } = r, { axis: n } = t6, { x: s } = e; - K(s, "unique"); +function U5(r) { + let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e; + j(s, "unique"); let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = Sf(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var gE = { kernelName: kp, backendName: "cpu", kernelFunc: I5 }; -function v5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { value: n } = e, { axis: s } = o; +var w$ = { kernelName: Np, backendName: "cpu", kernelFunc: U5 }; +function G5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape.length, i = n.shape[s], p = new Array(a - 1), u = 0; for (let d = 0; d < a; d++) @@ -14833,112 +14828,112 @@ function v5(r) { let m = new Array(i); for (let d = 0; d < m.length; d++) { c[s] = d; - let f = No({ inputs: { x: n }, backend: t6, attrs: { begin: c, size: l } }); - m[d] = Me({ inputs: { x: f }, backend: t6, attrs: { shape: p } }), t6.disposeIntermediateTensorInfo(f); + let f = To({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: l } }); + m[d] = Le({ inputs: { x: f }, backend: t10, attrs: { shape: p } }), t10.disposeIntermediateTensorInfo(f); } return m; } -var xE = { kernelName: Rs, backendName: "cpu", kernelFunc: v5 }; -function k5(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o; - K(n, "unsortedSegmentSum"); +var I$ = { kernelName: Fs, backendName: "cpu", kernelFunc: G5 }; +function H5(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o; + j(n, "unsortedSegmentSum"); let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s; for (let f = 0; f < l; ++f) { - let h = Jp({ inputs: { input: m }, backend: t6, attrs: { dim: f + 1 } }); + let h = ec({ inputs: { input: m }, backend: t10, attrs: { dim: f + 1 } }); m = h, c.push(h); } for (let f = 0; f < a; ++f) { - let h = y.createScalarValue(f, "int32"), g = t6.makeTensorInfo([], "int32", h), x = eS({ inputs: { a: g, b: m }, backend: t6 }), b = Io({ inputs: { x }, backend: t6, attrs: { dtype: "float32" } }), C = wu({ inputs: { a: b, b: n }, backend: t6 }), w = La({ inputs: { x: C }, backend: t6, attrs: { axis: 0, keepDims: false } }); + let h = y.createScalarValue(f, "int32"), g = t10.makeTensorInfo([], "int32", h), x = lS({ inputs: { a: g, b: m }, backend: t10 }), b = vo({ inputs: { x }, backend: t10, attrs: { dtype: "float32" } }), C = Tu({ inputs: { a: b, b: n }, backend: t10 }), w = za({ inputs: { x: C }, backend: t10, attrs: { axis: 0, keepDims: false } }); u.push(w), c.push(g), c.push(x), c.push(b), c.push(C), c.push(w); } - let d = AS({ inputs: u, backend: t6, attrs: { axis: 0 } }); - return c.forEach((f) => t6.disposeIntermediateTensorInfo(f)), d; + let d = WS({ inputs: u, backend: t10, attrs: { axis: 0 } }); + return c.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d; } -var yE = { kernelName: Np, backendName: "cpu", kernelFunc: k5 }; -var N5 = [GT, tT, HT, qT, aT, KT, jT, XT, YT, QT, ZT, JT, e2, t2, r2, n2, s2, a2, i2, UT, u2, p2, c2, l2, sT, iT, m2, rT, d2, h2, g2, x2, y2, b2, C2, S2, w2, I2, v2, k2, N2, T2, _2, E2, $2, A2, R2, F2, D2, O2, M2, MT, L2, uT, B2, pT, V2, cT, z2, W2, U2, lT, G2, H2, q2, K2, j2, mT, dT, oT, X2, f2, Y2, Q2, Z2, LT, fT, hT, J2, gT, e_, t_, r_, o_, n_, s_, a_, xT, i_, u_, p_, c_, m_, d_, f_, yT, h_, g_, b_, bT, CT, C_, S_, w_, ST, I_, N_, T_, kf, __, BT, IT, E_, $_, A_, R_, nT, gl, F_, VT, zT, WT, D_, O_, P_, M_, L_, B_, V_, _T, z_, U_, G_, H_, $T, q_, K_, j_, AT, x_, Y_, Q_, Z_, J_, eE, tE, rE, oE, FT, nE, DT, sE, aE, iE, uE, pE, OT, P2, cE, lE, mE, dE, hE, wT, gE, xE, yE, v_]; -for (let r of N5) - Ia(r); -var oc = {}; -Ue(oc, { assertNotComplex: () => is, bindCanvasToFramebuffer: () => O5, bindColorTextureToFramebuffer: () => Il, bindTextureToProgramUniformSampler: () => KS, bindTextureUnit: () => wE, bindVertexBufferToProgramAttribute: () => Af, callAndCheck: () => ce, canBeRepresented: () => OS, createFragmentShader: () => MS, createFramebuffer: () => GS, createProgram: () => LS, createStaticIndexBuffer: () => zS, createStaticVertexBuffer: () => VS, createTexture: () => WS, createVertexShader: () => PS, getBatchDim: () => Va, getExtensionOrThrow: () => ec, getFramebufferErrorMessage: () => IE, getMaxTexturesInShader: () => YS, getNumChannels: () => F5, getProgramUniformLocation: () => qS, getProgramUniformLocationOrThrow: () => HS, getRowsCols: () => za, getShapeAs3D: () => rc, getTextureShapeFromLogicalShape: () => jS, getWebGLDisjointQueryTimerVersion: () => QS, getWebGLErrorMessage: () => SE, getWebGLMaxTextureSize: () => XS, hasExtension: () => Ur, isCapableOfRenderingToFloatTexture: () => ZS, isDownloadFloatTextureEnabled: () => JS, isReshapeFree: () => Li, isWebGLFenceEnabled: () => ew, isWebGLVersionEnabled: () => Ff, linkProgram: () => BS, logShaderSourceAndInfoLog: () => $f, resetMaxTextureSize: () => P5, resetMaxTexturesInShader: () => M5, unbindColorTextureFromFramebuffer: () => Rf, unbindTextureUnit: () => D5, validateFramebuffer: () => tc, validateProgram: () => wl, validateTextureSize: () => US }); -var Eu = {}; +var v$ = { kernelName: Tp, backendName: "cpu", kernelFunc: H5 }; +var K5 = [YT, iT, QT, ZT, mT, JT, e2, t2, r2, o2, n2, s2, a2, i2, u2, c2, l2, m2, d2, XT, f2, h2, g2, x2, lT, dT, y2, uT, b2, S2, w2, I2, v2, k2, N2, T2, _2, $2, E2, A2, R2, D2, F2, O2, P2, M2, L2, B2, V2, z2, U2, UT, G2, fT, H2, hT, K2, gT, q2, j2, X2, xT, Y2, Q2, Z2, J2, e_, yT, bT, pT, t_, C2, r_, o_, n_, GT, CT, ST, s_, wT, a_, i_, u_, p_, c_, l_, m_, IT, d_, f_, h_, g_, y_, b_, C_, vT, S_, w_, k_, kT, NT, N_, T_, __, TT, $_, R_, D_, kf, F_, HT, $T, O_, P_, M_, L_, cT, bl, B_, KT, qT, jT, V_, z_, W_, U_, G_, H_, K_, FT, q_, X_, Y_, Q_, PT, Z_, J_, e$, MT, I_, r$, o$, n$, s$, a$, i$, u$, p$, BT, c$, VT, l$, m$, d$, f$, h$, zT, W2, g$, x$, y$, b$, S$, _T, w$, I$, v$, E_]; +for (let r of K5) + ka(r); +var nc = {}; +Ge(nc, { assertNotComplex: () => us, bindCanvasToFramebuffer: () => t8, bindColorTextureToFramebuffer: () => Nl, bindTextureToProgramUniformSampler: () => nw, bindTextureUnit: () => _$, bindVertexBufferToProgramAttribute: () => Af, callAndCheck: () => pe, canBeRepresented: () => KS, createFragmentShader: () => jS, createFramebuffer: () => tw, createProgram: () => XS, createStaticIndexBuffer: () => ZS, createStaticVertexBuffer: () => QS, createTexture: () => JS, createVertexShader: () => qS, getBatchDim: () => Ua, getExtensionOrThrow: () => tc, getFramebufferErrorMessage: () => $$, getMaxTexturesInShader: () => iw, getNumChannels: () => J5, getProgramUniformLocation: () => ow, getProgramUniformLocationOrThrow: () => rw, getRowsCols: () => Ga, getShapeAs3D: () => oc, getTextureShapeFromLogicalShape: () => sw, getWebGLDisjointQueryTimerVersion: () => uw, getWebGLErrorMessage: () => T$, getWebGLMaxTextureSize: () => aw, hasExtension: () => Wr, isCapableOfRenderingToFloatTexture: () => pw, isDownloadFloatTextureEnabled: () => cw, isReshapeFree: () => Bi, isWebGLFenceEnabled: () => lw, isWebGLVersionEnabled: () => Df, linkProgram: () => YS, logShaderSourceAndInfoLog: () => Ef, resetMaxTextureSize: () => r8, resetMaxTexturesInShader: () => o8, unbindColorTextureFromFramebuffer: () => Rf, unbindTextureUnit: () => e8, validateFramebuffer: () => rc, validateProgram: () => kl, validateTextureSize: () => ew }); +var Fu = {}; var Nf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true }; -function RS(r, e) { - Eu[r] = e; +function US(r, e) { + Fu[r] = e; } -function Wr(r, e) { - if (!(r in Eu) || e != null) { - let o = _5(r, e); +function zr(r, e) { + if (!(r in Fu) || e != null) { + let o = j5(r, e); if (o !== null) - Eu[r] = o; + Fu[r] = o; else return console.log("Could not get context for WebGL version", r), null; } - let t6 = Eu[r]; - return t6 == null || t6.isContextLost() ? (delete Eu[r], Wr(r)) : (t6.disable(t6.DEPTH_TEST), t6.disable(t6.STENCIL_TEST), t6.disable(t6.BLEND), t6.disable(t6.DITHER), t6.disable(t6.POLYGON_OFFSET_FILL), t6.disable(t6.SAMPLE_COVERAGE), t6.enable(t6.SCISSOR_TEST), t6.enable(t6.CULL_FACE), t6.cullFace(t6.BACK), Eu[r]); + let t10 = Fu[r]; + return t10 == null || t10.isContextLost() ? (delete Fu[r], zr(r)) : (t10.disable(t10.DEPTH_TEST), t10.disable(t10.STENCIL_TEST), t10.disable(t10.BLEND), t10.disable(t10.DITHER), t10.disable(t10.POLYGON_OFFSET_FILL), t10.disable(t10.SAMPLE_COVERAGE), t10.enable(t10.SCISSOR_TEST), t10.enable(t10.CULL_FACE), t10.cullFace(t10.BACK), Fu[r]); } -function T5(r) { +function q5(r) { if (typeof OffscreenCanvas != "undefined" && r === 2) return new OffscreenCanvas(300, 150); if (typeof document != "undefined") return document.createElement("canvas"); throw new Error("Cannot create a canvas in this context"); } -function _5(r, e) { +function j5(r, e) { if (r !== 1 && r !== 2) throw new Error("Cannot get WebGL rendering context, WebGL is disabled."); - let t6 = e == null ? T5(r) : e; - return t6.addEventListener("webglcontextlost", (o) => { - o.preventDefault(), delete Eu[r]; - }, false), O().getBool("SOFTWARE_WEBGL_ENABLED") && (Nf.failIfMajorPerformanceCaveat = false), r === 1 ? t6.getContext("webgl", Nf) || t6.getContext("experimental-webgl", Nf) : t6.getContext("webgl2", Nf); + let t10 = e == null ? q5(r) : e; + return t10.addEventListener("webglcontextlost", (o) => { + o.preventDefault(), delete Fu[r]; + }, false), O().getBool("SOFTWARE_WEBGL_ENABLED") && (Nf.failIfMajorPerformanceCaveat = false), r === 1 ? t10.getContext("webgl", Nf) || t10.getContext("experimental-webgl", Nf) : t10.getContext("webgl2", Nf); } -var Mi; +var Li; (function(r) { r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH"; -})(Mi || (Mi = {})); -var ir; +})(Li || (Li = {})); +var ur; (function(r) { r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD"; -})(ir || (ir = {})); +})(ur || (ur = {})); var Zt; (function(r) { r[r.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r[r.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r[r.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r[r.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r[r.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16"; })(Zt || (Zt = {})); -function $u(r, e) { +function Ou(r, e) { return [e, r]; } -function bE(r, e) { +function k$(r, e) { return r * e; } -function Cl(r) { - let e = y.sizeFromShape(r), t6 = Math.ceil(e / 4); - return y.sizeToSquarishShape(t6); +function Il(r) { + let e = y.sizeFromShape(r), t10 = Math.ceil(e / 4); + return y.sizeToSquarishShape(t10); } -function Ys(r, e) { +function Qs(r, e) { return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))]; } -function CE(r, e) { - let [t6, o] = Ys(r, e); - return t6 * o * 4; +function N$(r, e) { + let [t10, o] = Qs(r, e); + return t10 * o * 4; } -function Sl(r, e) { - let t6 = r, o, n, s, a, i, p, u, c, l, m; - return O().getNumber("WEBGL_VERSION") === 2 ? (o = t6.R32F, n = t6.R16F, s = t6.RGBA16F, a = t6.RGBA32F, i = t6.RED, u = 4, c = 1, l = t6.HALF_FLOAT, m = t6.FLOAT, p = t6.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t6.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; +function vl(r, e) { + let t10 = r, o, n, s, a, i, p, u, c, l, m; + return O().getNumber("WEBGL_VERSION") === 2 ? (o = t10.R32F, n = t10.R16F, s = t10.RGBA16F, a = t10.RGBA32F, i = t10.RED, u = 4, c = 1, l = t10.HALF_FLOAT, m = t10.FLOAT, p = t10.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t10.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; } -function ce(r, e) { - let t6 = e(); - return O().getBool("DEBUG") && E5(r), t6; +function pe(r, e) { + let t10 = e(); + return O().getBool("DEBUG") && X5(r), t10; } -function E5(r) { +function X5(r) { let e = r.getError(); if (e !== r.NO_ERROR) - throw new Error("WebGL Error: " + SE(r, e)); + throw new Error("WebGL Error: " + T$(r, e)); } -var $5 = 596e-10; -var A5 = 65504; -function OS(r) { - return !!(O().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || $5 < Math.abs(r) && Math.abs(r) < A5); +var Y5 = 596e-10; +var Q5 = 65504; +function KS(r) { + return !!(O().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || Y5 < Math.abs(r) && Math.abs(r) < Q5); } -function SE(r, e) { +function T$(r, e) { switch (e) { case r.NO_ERROR: return "NO_ERROR"; @@ -14958,31 +14953,31 @@ function SE(r, e) { return `Unknown error code ${e}`; } } -function ec(r, e) { - return Ba(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.'); +function tc(r, e) { + return Wa(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.'); } -function PS(r, e) { - let t6 = Ba(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader."); - if (ce(r, () => r.shaderSource(t6, e)), ce(r, () => r.compileShader(t6)), r.getShaderParameter(t6, r.COMPILE_STATUS) === false) - throw console.log(r.getShaderInfoLog(t6)), new Error("Failed to compile vertex shader."); - return t6; +function qS(r, e) { + let t10 = Wa(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader."); + if (pe(r, () => r.shaderSource(t10, e)), pe(r, () => r.compileShader(t10)), r.getShaderParameter(t10, r.COMPILE_STATUS) === false) + throw console.log(r.getShaderInfoLog(t10)), new Error("Failed to compile vertex shader."); + return t10; } -function MS(r, e) { - let t6 = Ba(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); - if (ce(r, () => r.shaderSource(t6, e)), ce(r, () => r.compileShader(t6)), O().get("ENGINE_COMPILE_ONLY")) - return t6; - if (r.getShaderParameter(t6, r.COMPILE_STATUS) === false) - throw $f(e, r.getShaderInfoLog(t6)), new Error("Failed to compile fragment shader."); - return t6; +function jS(r, e) { + let t10 = Wa(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); + if (pe(r, () => r.shaderSource(t10, e)), pe(r, () => r.compileShader(t10)), O().get("ENGINE_COMPILE_ONLY")) + return t10; + if (r.getShaderParameter(t10, r.COMPILE_STATUS) === false) + throw Ef(e, r.getShaderInfoLog(t10)), new Error("Failed to compile fragment shader."); + return t10; } -var R5 = /ERROR: [0-9]+:([0-9]+):/g; -function $f(r, e) { - let t6 = R5.exec(e); - if (t6 == null) { +var Z5 = /ERROR: [0-9]+:([0-9]+):/g; +function Ef(r, e) { + let t10 = Z5.exec(e); + if (t10 == null) { console.log(`Couldn't parse line number in error: ${e}`), console.log(r); return; } - let o = +t6[1], n = r.split(` + let o = +t10[1], n = r.split(` `), s = n.length.toString().length + 2, a = n.map((l, m) => y.rightPad((m + 1).toString(), s) + l), i = 0; for (let l = 0; l < a.length; l++) i = Math.max(a[l].length, i); @@ -14992,79 +14987,79 @@ function $f(r, e) { `)[0]), console.log(`%c ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(` `)); } -function LS(r) { - return Ba(r, () => r.createProgram(), "Unable to create WebGLProgram."); +function XS(r) { + return Wa(r, () => r.createProgram(), "Unable to create WebGLProgram."); } -function BS(r, e) { - if (ce(r, () => r.linkProgram(e)), !O().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false) +function YS(r, e) { + if (pe(r, () => r.linkProgram(e)), !O().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false) throw console.log(r.getProgramInfoLog(e)), new Error("Failed to link vertex and fragment shaders."); } -function wl(r, e) { - if (ce(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false) +function kl(r, e) { + if (pe(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false) throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed."); } -function VS(r, e) { - let t6 = Ba(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t6)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t6; +function QS(r, e) { + let t10 = Wa(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); + return pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), pe(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t10; } -function zS(r, e) { - let t6 = Ba(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t6)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t6; +function ZS(r, e) { + let t10 = Wa(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); + return pe(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t10)), pe(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t10; } -function F5() { +function J5() { return O().getNumber("WEBGL_VERSION") === 2 ? 1 : 4; } -function WS(r) { - return Ba(r, () => r.createTexture(), "Unable to create WebGLTexture."); +function JS(r) { + return Wa(r, () => r.createTexture(), "Unable to create WebGLTexture."); } -function US(r, e) { - let t6 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE"); +function ew(r, e) { + let t10 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE"); if (r <= 0 || e <= 0) { let o = `[${r}x${e}]`; throw new Error("Requested texture size " + o + " is invalid."); } - if (r > t6 || e > t6) { - let o = `[${r}x${e}]`, n = `[${t6}x${t6}]`; + if (r > t10 || e > t10) { + let o = `[${r}x${e}]`, n = `[${t10}x${t10}]`; throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + "."); } } -function GS(r) { - return Ba(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer."); +function tw(r) { + return Wa(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer."); } -function Af(r, e, t6, o, n, s, a) { - let i = r.getAttribLocation(e, t6); - return i === -1 ? false : (ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), ce(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), ce(r, () => r.enableVertexAttribArray(i)), true); +function Af(r, e, t10, o, n, s, a) { + let i = r.getAttribLocation(e, t10); + return i === -1 ? false : (pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), pe(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), pe(r, () => r.enableVertexAttribArray(i)), true); } -function wE(r, e, t6) { - vE(r, t6), ce(r, () => r.activeTexture(r.TEXTURE0 + t6)), ce(r, () => r.bindTexture(r.TEXTURE_2D, e)); +function _$(r, e, t10) { + E$(r, t10), pe(r, () => r.activeTexture(r.TEXTURE0 + t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, e)); } -function D5(r, e) { - vE(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function e8(r, e) { + E$(r, e), pe(r, () => r.activeTexture(r.TEXTURE0 + e)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function HS(r, e, t6) { - return Ba(r, () => r.getUniformLocation(e, t6), 'uniform "' + t6 + '" not present in program.'); +function rw(r, e, t10) { + return Wa(r, () => r.getUniformLocation(e, t10), 'uniform "' + t10 + '" not present in program.'); } -function qS(r, e, t6) { - return r.getUniformLocation(e, t6); +function ow(r, e, t10) { + return r.getUniformLocation(e, t10); } -function KS(r, e, t6, o) { - ce(r, () => wE(r, e, o)), ce(r, () => r.uniform1i(t6, o)); +function nw(r, e, t10, o) { + pe(r, () => _$(r, e, o)), pe(r, () => r.uniform1i(t10, o)); } -function O5(r) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), ce(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), ce(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height)); +function t8(r) { + pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), pe(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), pe(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height)); } -function Il(r, e, t6) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t6)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0)); +function Nl(r, e, t10) { + pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t10)), pe(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0)); } function Rf(r, e) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0)); + pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), pe(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0)); } -function tc(r) { +function rc(r) { let e = r.checkFramebufferStatus(r.FRAMEBUFFER); if (e !== r.FRAMEBUFFER_COMPLETE) - throw new Error("Error binding framebuffer: " + IE(r, e)); + throw new Error("Error binding framebuffer: " + $$(r, e)); } -function IE(r, e) { +function $$(r, e) { switch (e) { case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT"; @@ -15078,41 +15073,41 @@ function IE(r, e) { return `unknown error ${e}`; } } -function Ba(r, e, t6) { - let o = ce(r, () => e()); +function Wa(r, e, t10) { + let o = pe(r, () => e()); if (o == null) - throw new Error(t6); + throw new Error(t10); return o; } -function vE(r, e) { - let t6 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0; - if (o < r.TEXTURE0 || o > t6) { - let n = `[gl.TEXTURE0, gl.TEXTURE${t6}]`; +function E$(r, e) { + let t10 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0; + if (o < r.TEXTURE0 || o > t10) { + let n = `[gl.TEXTURE0, gl.TEXTURE${t10}]`; throw new Error(`textureUnit must be in ${n}.`); } } -function Va(r, e = 2) { +function Ua(r, e = 2) { return y.sizeFromShape(r.slice(0, r.length - e)); } -function za(r) { +function Ga(r) { if (r.length === 0) throw Error("Cannot get rows and columns of an empty shape array."); return [r.length > 1 ? r[r.length - 2] : 1, r[r.length - 1]]; } -function rc(r) { +function oc(r) { let e = [1, 1, 1]; - return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [Va(r), ...za(r)]), e; + return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [Ua(r), ...Ga(r)]), e; } -function jS(r, e = false) { - let t6 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = O().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); - o === 1 / 0 && O().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t6 / 2), e && (t6 = t6 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape); +function sw(r, e = false) { + let t10 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = O().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); + o === 1 / 0 && O().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t10 / 2), e && (t10 = t10 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape); let n = y.sizeFromShape(r), s = null; - r.length <= 1 && n <= t6 ? s = [1, n] : r.length === 2 && r[0] <= t6 && r[1] <= t6 ? s = r : r.length === 3 && r[0] * r[1] <= t6 && r[2] <= t6 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t6 && r[1] * r[2] <= t6 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t6 && r[3] <= t6 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t6 && r[1] * r[2] * r[3] <= t6 && (s = [r[0], r[1] * r[2] * r[3]]); + r.length <= 1 && n <= t10 ? s = [1, n] : r.length === 2 && r[0] <= t10 && r[1] <= t10 ? s = r : r.length === 3 && r[0] * r[1] <= t10 && r[2] <= t10 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t10 && r[1] * r[2] <= t10 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t10 && r[3] <= t10 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t10 && r[1] * r[2] * r[3] <= t10 && (s = [r[0], r[1] * r[2] * r[3]]); let a = s != null && Math.max(...s) > o && Math.min(...s) <= (e ? 2 : 1) && Math.min(...s) > 0; if (s == null || a) if (e) { - let i = Va(r), p = 2, u = 2; - r.length && ([p, u] = za(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); + let i = Ua(r), p = 2, u = 2; + r.length && ([p, u] = Ga(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); } else s = y.sizeToSquarishShape(n); return s; @@ -15120,165 +15115,165 @@ function jS(r, e = false) { function Tf(r) { return r % 2 === 0; } -function Li(r, e) { +function Bi(r, e) { if (r = r.slice(-2), e = e.slice(-2), y.arraysEqual(r, e) || !r.length || !e.length || r[0] === 0 || r[1] === 0 || e[0] === 0 || e[1] === 0) return true; if (r.length !== e.length) { - let t6 = r.slice(-1)[0], o = e.slice(-1)[0]; - if (t6 === o || Tf(t6) && Tf(o) && (r[0] === 1 || e[0] === 1)) + let t10 = r.slice(-1)[0], o = e.slice(-1)[0]; + if (t10 === o || Tf(t10) && Tf(o) && (r[0] === 1 || e[0] === 1)) return true; } return r[1] === e[1] && Tf(r[0]) && Tf(e[0]); } var _f; -var Ef; -function XS(r) { +var $f; +function aw(r) { if (_f == null) { - let e = Wr(r); + let e = zr(r); _f = e.getParameter(e.MAX_TEXTURE_SIZE); } return _f; } -function P5() { +function r8() { _f = null; } -function M5() { - Ef = null; +function o8() { + $f = null; } -function YS(r) { - if (Ef == null) { - let e = Wr(r); - Ef = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS); +function iw(r) { + if ($f == null) { + let e = zr(r); + $f = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS); } - return Math.min(16, Ef); + return Math.min(16, $f); } -function QS(r) { +function uw(r) { if (r === 0) return 0; - let e, t6 = Wr(r); - return Ur(t6, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Ur(t6, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e; + let e, t10 = zr(r); + return Wr(t10, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Wr(t10, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e; } -function Ur(r, e) { +function Wr(r, e) { return r.getExtension(e) != null; } -function Ff(r) { +function Df(r) { try { - if (Wr(r) != null) + if (zr(r) != null) return true; } catch (e) { return console.log("Error when getting WebGL context: ", e), false; } return false; } -function ZS(r) { +function pw(r) { if (r === 0) return false; - let e = Wr(r); + let e = zr(r); if (r === 1) { - if (!Ur(e, "OES_texture_float")) + if (!Wr(e, "OES_texture_float")) return false; - } else if (!Ur(e, "EXT_color_buffer_float")) + } else if (!Wr(e, "EXT_color_buffer_float")) return false; - return DS(e); + return HS(e); } -function JS(r) { +function cw(r) { if (r === 0) return false; - let e = Wr(r); + let e = zr(r); if (r === 1) { - if (!Ur(e, "OES_texture_float") || !Ur(e, "WEBGL_color_buffer_float")) + if (!Wr(e, "OES_texture_float") || !Wr(e, "WEBGL_color_buffer_float")) return false; } else { - if (Ur(e, "EXT_color_buffer_float")) - return DS(e); + if (Wr(e, "EXT_color_buffer_float")) + return HS(e); let o = "EXT_color_buffer_half_float"; - if (Ur(e, o)) { + if (Wr(e, o)) { let n = e.getExtension(o); - return L5(e, n); + return n8(e, n); } return false; } - return DS(e); + return HS(e); } -function DS(r) { - let e = Sl(r), t6 = r.createTexture(); - r.bindTexture(r.TEXTURE_2D, t6); +function HS(r) { + let e = vl(r), t10 = r.createTexture(); + r.bindTexture(r.TEXTURE_2D, t10); let o = 1, n = 1; r.texImage2D(r.TEXTURE_2D, 0, e.internalFormatFloat, o, n, 0, e.textureFormatFloat, e.textureTypeFloat, null); let s = r.createFramebuffer(); - r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t6, 0); + r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t10, 0); let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; - return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t6), r.deleteFramebuffer(s), a; + return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t10), r.deleteFramebuffer(s), a; } -function L5(r, e) { - let t6 = Sl(r, e), o = r.createTexture(); +function n8(r, e) { + let t10 = vl(r, e), o = r.createTexture(); r.bindTexture(r.TEXTURE_2D, o); let n = 1, s = 1; - r.texImage2D(r.TEXTURE_2D, 0, t6.internalFormatHalfFloat, n, s, 0, t6.textureFormatFloat, t6.textureTypeHalfFloat, null); + r.texImage2D(r.TEXTURE_2D, 0, t10.internalFormatHalfFloat, n, s, 0, t10.textureFormatFloat, t10.textureTypeHalfFloat, null); let a = r.createFramebuffer(); r.bindFramebuffer(r.FRAMEBUFFER, a), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0); let i = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(o), r.deleteFramebuffer(a), i; } -function ew(r) { - return r !== 2 ? false : Wr(r).fenceSync != null; +function lw(r) { + return r !== 2 ? false : zr(r).fenceSync != null; } -function is(r, e) { - Array.isArray(r) || (r = [r]), r.forEach((t6) => { - t6 != null && y.assert(t6.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`); +function us(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t10) => { + t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`); }); } -var Ce = O(); -Ce.registerFlag("HAS_WEBGL", () => Ce.getNumber("WEBGL_VERSION") > 0); -Ce.registerFlag("WEBGL_VERSION", () => Ff(2) ? 2 : Ff(1) ? 1 : 0); -Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); -Ce.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ce.get("WEBGL_VERSION") === 2); -Ce.registerFlag("WEBGL_CPU_FORWARD", () => true); -Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); -Ce.registerFlag("WEBGL_PACK", () => Ce.getBool("HAS_WEBGL")); -Ce.registerFlag("WEBGL_PACK_NORMALIZATION", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_CLIP", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_PACK_REDUCE", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_LAZILY_UNPACK", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_CONV_IM2COL", () => Ce.getBool("WEBGL_PACK")); -Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => XS(Ce.getNumber("WEBGL_VERSION"))); -Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => YS(Ce.getNumber("WEBGL_VERSION"))); -Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { - let r = Ce.getNumber("WEBGL_VERSION"); - return r === 0 ? 0 : QS(r); +var Se = O(); +Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0); +Se.registerFlag("WEBGL_VERSION", () => Df(2) ? 2 : Df(1) ? 1 : 0); +Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); +Se.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Se.get("WEBGL_VERSION") === 2); +Se.registerFlag("WEBGL_CPU_FORWARD", () => true); +Se.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); +Se.registerFlag("WEBGL_PACK", () => Se.getBool("HAS_WEBGL")); +Se.registerFlag("WEBGL_PACK_NORMALIZATION", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_CLIP", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_PACK_REDUCE", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_LAZILY_UNPACK", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_CONV_IM2COL", () => Se.getBool("WEBGL_PACK")); +Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => aw(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => iw(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { + let r = Se.getNumber("WEBGL_VERSION"); + return r === 0 ? 0 : uw(r); }); -Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !yi.isMobile()); -Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => ZS(Ce.getNumber("WEBGL_VERSION"))); -Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ce.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")); -Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => JS(Ce.getNumber("WEBGL_VERSION"))); -Ce.registerFlag("WEBGL_FENCE_API_ENABLED", () => ew(Ce.getNumber("WEBGL_VERSION"))); -Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0); -Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => { +Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ki.isMobile()); +Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => pw(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Se.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")); +Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => cw(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => lw(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0); +Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => { if (r < 0 && r !== -1) throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`); }); -Ce.registerFlag("WEBGL_FLUSH_THRESHOLD", () => yi.isMobile() ? 1 : -1, (r) => { +Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => ki.isMobile() ? 1 : -1, (r) => { if (r < 0 && r !== -1) throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`); }); -Ce.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); -Ce.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); -Ce.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); -Ce.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); -Ce.registerFlag("WEBGL_EXP_CONV", () => false); -Ce.registerFlag("SOFTWARE_WEBGL_ENABLED", () => Ce.getBool("IS_TEST")); -Ce.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0); -Ce.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); -Ce.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); -Ce.registerFlag("ENGINE_COMPILE_ONLY", () => false); -function St() { - let r, e, t6, o, n, s, a, i, p, u; - return O().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t6 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = O().getBool("WEBGL2_ISNAN_CUSTOM") ? ` +Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); +Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); +Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); +Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); +Se.registerFlag("WEBGL_EXP_CONV", () => false); +Se.registerFlag("SOFTWARE_WEBGL_ENABLED", () => Se.getBool("IS_TEST")); +Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0); +Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); +Se.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); +Se.registerFlag("ENGINE_COMPILE_ONLY", () => false); +function wt() { + let r, e, t10, o, n, s, a, i, p, u; + return O().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t10 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = O().getBool("WEBGL2_ISNAN_CUSTOM") ? ` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -15299,7 +15294,7 @@ function St() { ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `) : (r = "", e = "attribute", t6 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` + `) : (r = "", e = "attribute", t10 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -15324,52 +15319,52 @@ function St() { ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `), { version: r, attribute: e, varyingVs: t6, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; + `), { version: r, attribute: e, varyingVs: t10, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; } -function us(r, e, t6 = "index") { +function ps(r, e, t10 = "index") { let o = y.computeStrides(e); return o.map((n, s) => { - let a = `int ${r[s]} = ${t6} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t6} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`; + let a = `int ${r[s]} = ${t10} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`; return `${a}; ${i};`; }).join(""); } -function Au(r, e, t6 = "index") { +function Pu(r, e, t10 = "index") { let o = y.computeStrides(e); return o.map((n, s) => { - let a = `int ${r[s]} = ${t6} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t6} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`; + let a = `int ${r[s]} = ${t10} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`; return `${a}; ${i};`; }).join(""); } -function B5(r, e) { - let t6 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t6 - 1); - n[t6 - 2] = o[t6 - 1]; - for (let s = t6 - 3; s >= 0; --s) +function s8(r, e) { + let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1); + n[t10 - 2] = o[t10 - 1]; + for (let s = t10 - 3; s >= 0; --s) n[s] = `(${n[s + 1]} * ${o[s + 1]})`; return n; } -function kE(r, e, t6 = "index") { - let o = r.map((s, a) => a), n = B5(o, e); +function A$(r, e, t10 = "index") { + let o = r.map((s, a) => a), n = s8(o, e); return n.map((s, a) => { - let i = `int ${r[a]} = ${t6} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t6} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`; + let i = `int ${r[a]} = ${t10} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t10} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`; return `${i}; ${p};`; }).join(""); } -function nc(r) { - let e = y.computeStrides(r).map((t6) => t6.toString()); +function sc(r) { + let e = y.computeStrides(r).map((t10) => t10.toString()); return ` int getFlatIndex(ivec3 coords) { return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z; } `; } -function sc() { +function ac() { return ` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } `; } -var Df = ` +var Ff = ` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -15409,13 +15404,13 @@ var Df = ` return c / 255.0; } `; -var { getBroadcastDims: NE } = S; -function TE(r, e, t6) { +var { getBroadcastDims: R$ } = S; +function D$(r, e, t10) { let o = []; if (r.forEach((d) => { let f = y.sizeFromShape(d.shapeInfo.logicalShape); - if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t6.enableShapeUniforms) { - let { uniformShape: h } = Of(t6.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); + if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t10.enableShapeUniforms) { + let { uniformShape: h } = Of(t10.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); switch (h.length) { case 1: o.push(`uniform int ${d.name}Shape;`); @@ -15434,7 +15429,7 @@ function TE(r, e, t6) { } o.push(`uniform ivec2 ${d.name}TexShape;`); } - }), t6.enableShapeUniforms) { + }), t10.enableShapeUniforms) { switch (e.logicalShape.length) { case 1: o.push("uniform int outShape;"); @@ -15453,112 +15448,112 @@ function TE(r, e, t6) { } o.push("uniform ivec2 outTexShape;"); } - t6.customUniforms && t6.customUniforms.forEach((d) => { + t10.customUniforms && t10.customUniforms.forEach((d) => { o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`); }); let n = o.join(` -`), s = r.map((d) => V5(d, e, t6.packedInputs, t6.enableShapeUniforms)).join(` -`), a = e.texShape, i = St(), p = U5(i), u, c, l = q5(i); - return e.isPacked ? (u = z5(e.logicalShape, a, t6.enableShapeUniforms), c = H5(i)) : (u = W5(e.logicalShape, a, t6.enableShapeUniforms), c = G5(i)), t6.packedInputs && (l += Y5), [l, p, c, n, u, s, t6.userCode].join(` +`), s = r.map((d) => a8(d, e, t10.packedInputs, t10.enableShapeUniforms)).join(` +`), a = e.texShape, i = wt(), p = p8(i), u, c, l = m8(i); + return e.isPacked ? (u = i8(e.logicalShape, a, t10.enableShapeUniforms), c = l8(i)) : (u = u8(e.logicalShape, a, t10.enableShapeUniforms), c = c8(i)), t10.packedInputs && (l += g8), [l, p, c, n, u, s, t10.userCode].join(` `); } -function ic(r, e = false) { - let t6 = r.shapeInfo.logicalShape; - switch (t6.length) { +function uc(r, e = false) { + let t10 = r.shapeInfo.logicalShape; + switch (t10.length) { case 0: - return u8(r, e); + return _8(r, e); case 1: - return c8(r, e); + return E8(r, e); case 2: - return m8(r, e); + return R8(r, e); case 3: - return f8(r, e); + return F8(r, e); case 4: - return g8(r, e); + return P8(r, e); case 5: - return x8(r); + return M8(r); case 6: - return y8(r); + return L8(r); default: - throw new Error(`${t6.length}-D input sampling is not yet supported`); + throw new Error(`${t10.length}-D input sampling is not yet supported`); } } -function _E(r, e) { +function F$(r, e) { switch (r.shapeInfo.logicalShape.length) { case 0: - return i8(r); + return T8(r); case 1: - return p8(r, e); + return $8(r, e); case 2: - return l8(r, e); + return A8(r, e); case 3: - return d8(r, e); + return D8(r, e); default: - return h8(r, e); + return O8(r, e); } } -function V5(r, e, t6 = false, o) { +function a8(r, e, t10 = false, o) { let n = ""; - t6 ? n += _E(r, o) : n += ic(r, o); + t10 ? n += F$(r, o) : n += uc(r, o); let s = r.shapeInfo.logicalShape, a = e.logicalShape; - return s.length <= a.length && (t6 ? n += b8(r, e) : n += C8(r, e)), n; + return s.length <= a.length && (t10 ? n += B8(r, e) : n += V8(r, e)), n; } -function z5(r, e, t6) { +function i8(r, e, t10) { switch (r.length) { case 0: - return EE(); + return O$(); case 1: - return Q5(r, e, t6); + return x8(r, e, t10); case 2: - return s8(r, e, t6); + return k8(r, e, t10); case 3: - return J5(r, e, t6); + return b8(r, e, t10); default: - return t8(r, e, t6); + return S8(r, e, t10); } } -function W5(r, e, t6) { +function u8(r, e, t10) { switch (r.length) { case 0: - return EE(); + return O$(); case 1: - return Z5(r, e, t6); + return y8(r, e, t10); case 2: - return a8(r, e, t6); + return N8(r, e, t10); case 3: - return e8(r, e, t6); + return C8(r, e, t10); case 4: - return r8(r, e, t6); + return w8(r, e, t10); case 5: - return o8(r, e); + return I8(r, e); case 6: - return n8(r, e); + return v8(r, e); default: throw new Error(`${r.length}-D output sampling is not yet supported`); } } -function U5(r) { +function p8(r) { return ` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } `; } -function G5(r) { +function c8(r) { return ` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } `; } -function H5(r) { +function l8(r) { return ` void setOutput(vec4 val) { ${r.output} = val; } `; } -function q5(r) { +function m8(r) { return `${r.version} precision highp float; precision highp int; @@ -15614,12 +15609,12 @@ function q5(r) { return fract((p3.x + p3.y) * p3.z); } - ${K5} - ${j5} - ${X5} + ${d8} + ${f8} + ${h8} `; } -var K5 = ` +var d8 = ` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -15632,7 +15627,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var j5 = ` +var f8 = ` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -15641,7 +15636,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var X5 = ` +var h8 = ` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -15651,7 +15646,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `; -var Y5 = ` +var g8 = ` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -15663,16 +15658,16 @@ var Y5 = ` return modCoord == 0. ? frag.r : frag.g; } `; -function EE() { +function O$() { return ` int getOutputCoords() { return 0; } `; } -function Q5(r, e, t6) { +function x8(r, e, t10) { let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)]; - return o[0] === 1 ? t6 ? ` + return o[0] === 1 ? t10 ? ` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -15680,7 +15675,7 @@ function Q5(r, e, t6) { int getOutputCoords() { return 2 * int(resultUV.x * ${o[1]}.0); } - ` : o[1] === 1 ? t6 ? ` + ` : o[1] === 1 ? t10 ? ` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } @@ -15688,7 +15683,7 @@ function Q5(r, e, t6) { int getOutputCoords() { return 2 * int(resultUV.y * ${o[0]}.0); } - ` : t6 ? ` + ` : t10 ? ` int getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -15703,8 +15698,8 @@ function Q5(r, e, t6) { } `; } -function Z5(r, e, t6) { - return e[0] === 1 ? t6 ? ` +function y8(r, e, t10) { + return e[0] === 1 ? t10 ? ` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -15712,7 +15707,7 @@ function Z5(r, e, t6) { int getOutputCoords() { return int(resultUV.x * ${e[1]}.0); } - ` : e[1] === 1 ? t6 ? ` + ` : e[1] === 1 ? t10 ? ` int getOutputCoords() { return int(resultUV.y * float(outTexShape[0])); } @@ -15720,7 +15715,7 @@ function Z5(r, e, t6) { int getOutputCoords() { return int(resultUV.y * ${e[0]}.0); } - ` : t6 ? ` + ` : t10 ? ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -15734,8 +15729,8 @@ function Z5(r, e, t6) { } `; } -function J5(r, e, t6) { - if (t6) +function b8(r, e, t10) { + if (t10) return ` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -15771,18 +15766,18 @@ function J5(r, e, t6) { } `; } -function e8(r, e, t6) { - if (t6) +function C8(r, e, t10) { + if (t10) return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Au(["r", "c", "d"], r)} + ${Pu(["r", "c", "d"], r)} return ivec3(r, c, d); } `; - let o = us(["r", "c", "d"], r); + let o = ps(["r", "c", "d"], r); return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -15793,8 +15788,8 @@ function e8(r, e, t6) { } `; } -function t8(r, e, t6) { - if (t6) +function S8(r, e, t10) { + if (t10) return ` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -15842,18 +15837,18 @@ function t8(r, e, t6) { } `; } -function r8(r, e, t6) { - if (t6) +function w8(r, e, t10) { + if (t10) return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Au(["r", "c", "d", "d2"], r)} + ${Pu(["r", "c", "d", "d2"], r)} return ivec4(r, c, d, d2); } `; - let o = us(["r", "c", "d", "d2"], r); + let o = ps(["r", "c", "d", "d2"], r); return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -15864,8 +15859,8 @@ function r8(r, e, t6) { } `; } -function o8(r, e) { - let t6 = us(["r", "c", "d", "d2", "d3"], r); +function I8(r, e) { + let t10 = ps(["r", "c", "d", "d2", "d3"], r); return ` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, @@ -15873,32 +15868,32 @@ function o8(r, e) { int index = resTexRC.x * ${e[1]} + resTexRC.y; - ${t6} + ${t10} ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } `; } -function n8(r, e) { - let t6 = us(["r", "c", "d", "d2", "d3", "d4"], r); +function v8(r, e) { + let t10 = ps(["r", "c", "d", "d2", "d3", "d4"], r); return ` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); int index = resTexRC.x * ${e[1]} + resTexRC.y; - ${t6} + ${t10} ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } `; } -function s8(r, e, t6) { +function k8(r, e, t10) { let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)]; if (y.arraysEqual(r, e)) - return t6 ? ` + return t10 ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -15909,7 +15904,7 @@ function s8(r, e, t6) { } `; let n = Math.ceil(r[1] / 2); - return t6 ? ` + return t10 ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0)); @@ -15935,8 +15930,8 @@ function s8(r, e, t6) { } `; } -function a8(r, e, t6) { - return y.arraysEqual(r, e) ? t6 ? ` +function N8(r, e, t10) { + return y.arraysEqual(r, e) ? t10 ? ` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -15944,7 +15939,7 @@ function a8(r, e, t6) { ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); } - ` : r[1] === 1 ? t6 ? ` + ` : r[1] === 1 ? t10 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -15958,7 +15953,7 @@ function a8(r, e, t6) { int index = resTexRC.x * ${e[1]} + resTexRC.y; return ivec2(index, 0); } - ` : r[0] === 1 ? t6 ? ` + ` : r[0] === 1 ? t10 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -15972,7 +15967,7 @@ function a8(r, e, t6) { int index = resTexRC.x * ${e[1]} + resTexRC.y; return ivec2(0, index); } - ` : t6 ? ` + ` : t10 ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -15992,53 +15987,53 @@ function a8(r, e, t6) { } `; } -function Ru(r) { +function Mu(r) { return `offset${r}`; } -function i8(r) { - let e = r.name, t6 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = St(); +function T8(r) { + let e = r.name, t10 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = wt(); return ` - vec4 ${t6}() { + vec4 ${t10}() { return ${o.texture2D}(${e}, halfCR); } `; } -function u8(r, e) { - let t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1); +function _8(r, e) { + let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1); if (r.shapeInfo.isUniform) - return `float ${o}() {return ${t6};}`; + return `float ${o}() {return ${t10};}`; let [n, s] = r.shapeInfo.texShape; if (n === 1 && s === 1) return ` float ${o}() { - return sampleTexture(${t6}, halfCR); + return sampleTexture(${t10}, halfCR); } `; - let a = Ru(t6); + let a = Mu(t10); if (e) return ` float ${o}() { - vec2 uv = uvFromFlat(${t6}TexShape[0], ${t6}TexShape[1], ${a}); - return sampleTexture(${t6}, uv); + vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], ${a}); + return sampleTexture(${t10}, uv); } `; let [i, p] = r.shapeInfo.texShape; return ` float ${o}() { vec2 uv = uvFromFlat(${i}, ${p}, ${a}); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; } -function p8(r, e) { - let t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1), n = r.shapeInfo.texShape, s = St(); +function $8(r, e) { + let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = r.shapeInfo.texShape, s = wt(); if (e) return ` vec4 ${o}(int index) { - ivec2 packedTexShape = ivec2(ceil(float(${t6}TexShape[0]) / 2.0), ceil(float(${t6}TexShape[1]) / 2.0)); + ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( packedTexShape[0], packedTexShape[1], index); - return ${s.texture2D}(${t6}, uv); + return ${s.texture2D}(${t10}, uv); } `; let a = [Math.ceil(n[0] / 2), Math.ceil(n[1] / 2)]; @@ -16046,61 +16041,61 @@ function p8(r, e) { vec4 ${o}(int index) { vec2 uv = packedUVfrom1D( ${a[0]}, ${a[1]}, index); - return ${s.texture2D}(${t6}, uv); + return ${s.texture2D}(${t10}, uv); } `; } -function c8(r, e) { - let t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1); +function E8(r, e) { + let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1); if (r.shapeInfo.isUniform) return ` float ${o}(int index) { - ${uc(r)} + ${pc(r)} } `; let n = r.shapeInfo.texShape, s = n[0], a = n[1]; if (a === 1 && s === 1) return ` float ${o}(int index) { - return sampleTexture(${t6}, halfCR); + return sampleTexture(${t10}, halfCR); } `; - let i = Ru(t6); + let i = Mu(t10); return a === 1 ? e ? ` float ${o}(int index) { - vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t6}TexShape[0])); - return sampleTexture(${t6}, uv); + vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t10}TexShape[0])); + return sampleTexture(${t10}, uv); } ` : ` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } ` : s === 1 ? e ? ` float ${o}(int index) { - vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t6}TexShape[1]), 0.5); - return sampleTexture(${t6}, uv); + vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t10}TexShape[1]), 0.5); + return sampleTexture(${t10}, uv); } ` : ` float ${o}(int index) { vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } ` : e ? ` float ${o}(int index) { - vec2 uv = uvFromFlat(${t6}TexShape[0], ${t6}TexShape[1], index + ${i}); - return sampleTexture(${t6}, uv); + vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], index + ${i}); + return sampleTexture(${t10}, uv); } ` : ` float ${o}(int index) { vec2 uv = uvFromFlat(${s}, ${a}, index + ${i}); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; } -function l8(r, e) { - let t6 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = St(); - if (s != null && y.arraysEqual(t6, s)) +function A8(r, e) { + let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = wt(); + if (s != null && y.arraysEqual(t10, s)) return e ? ` vec4 ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); @@ -16123,7 +16118,7 @@ function l8(r, e) { return ${p.texture2D}(${o}, uv); } `; - let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t6[1] / 2); + let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t10[1] / 2); return ` vec4 ${n}(int row, int col) { vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col); @@ -16131,9 +16126,9 @@ function l8(r, e) { } `; } -function m8(r, e) { - let t6 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape; - if (s != null && y.arraysEqual(t6, s)) { +function R8(r, e) { + let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape; + if (s != null && y.arraysEqual(t10, s)) { if (e) return ` float ${n}(int row, int col) { @@ -16149,24 +16144,24 @@ function m8(r, e) { } `; } - let { newShape: a, keptDims: i } = y.squeezeShape(t6), p = a; - if (p.length < t6.length) { - let m = pc(r, p), d = ["row", "col"]; + let { newShape: a, keptDims: i } = y.squeezeShape(t10), p = a; + if (p.length < t10.length) { + let m = cc(r, p), d = ["row", "col"]; return ` - ${ic(m, e)} + ${uc(m, e)} float ${n}(int row, int col) { - return ${n}(${cc(d, i)}); + return ${n}(${lc(d, i)}); } `; } if (r.shapeInfo.isUniform) return ` float ${n}(int row, int col) { - int index = round(dot(vec2(row, col), vec2(${t6[1]}, 1))); - ${uc(r)} + int index = round(dot(vec2(row, col), vec2(${t10[1]}, 1))); + ${pc(r)} } `; - let u = s[0], c = s[1], l = Ru(o); + let u = s[0], c = s[1], l = Mu(o); return c === 1 ? e ? ` float ${n}(int row, int col) { float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1)); @@ -16175,7 +16170,7 @@ function m8(r, e) { } ` : ` float ${n}(int row, int col) { - float index = dot(vec3(row, col, ${l}), vec3(${t6[1]}, 1, 1)); + float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0); return sampleTexture(${o}, uv); } @@ -16187,7 +16182,7 @@ function m8(r, e) { } ` : ` float ${n}(int row, int col) { - float index = dot(vec3(row, col, ${l}), vec3(${t6[1]}, 1, 1)); + float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1)); vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5); return sampleTexture(${o}, uv); } @@ -16201,24 +16196,24 @@ function m8(r, e) { ` : ` float ${n}(int row, int col) { // Explicitly use integer operations as dot() only works on floats. - int index = row * ${t6[1]} + col + ${l}; + int index = row * ${t10[1]} + col + ${l}; vec2 uv = uvFromFlat(${u}, ${c}, index); return sampleTexture(${o}, uv); } `; } -function d8(r, e) { - let t6 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; - if (t6[0] === 1) { - let m = t6.slice(1), d = [1, 2], f = pc(r, m), h = ["b", "row", "col"]; +function D8(r, e) { + let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; + if (t10[0] === 1) { + let m = t10.slice(1), d = [1, 2], f = cc(r, m), h = ["b", "row", "col"]; return ` - ${_E(f, e)} + ${F$(f, e)} vec4 ${n}(int b, int row, int col) { - return ${n}(${cc(h, d)}); + return ${n}(${lc(h, d)}); } `; } - let i = St(); + let i = wt(); if (e) return ` vec4 ${n}(int b, int row, int col) { @@ -16230,7 +16225,7 @@ function d8(r, e) { return ${i.texture2D}(${o}, uv); } `; - let p = a[0], u = a[1], c = Math.ceil(t6[2] / 2), l = c * Math.ceil(t6[1] / 2); + let p = a[0], u = a[1], c = Math.ceil(t10[2] / 2), l = c * Math.ceil(t10[1] / 2); return ` vec4 ${n}(int b, int row, int col) { vec2 uv = packedUVfrom3D( @@ -16239,14 +16234,14 @@ function d8(r, e) { } `; } -function f8(r, e) { - let t6 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t6[1] * t6[2], a = t6[2], { newShape: i, keptDims: p } = y.squeezeShape(t6), u = i; - if (u.length < t6.length) { - let h = pc(r, u), g = ["row", "col", "depth"]; +function F8(r, e) { + let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[1] * t10[2], a = t10[2], { newShape: i, keptDims: p } = y.squeezeShape(t10), u = i; + if (u.length < t10.length) { + let h = cc(r, u), g = ["row", "col", "depth"]; return ` - ${ic(h, e)} + ${uc(h, e)} float ${n}(int row, int col, int depth) { - return ${n}(${cc(g, p)}); + return ${n}(${lc(g, p)}); } `; } @@ -16255,7 +16250,7 @@ function f8(r, e) { float ${n}(int row, int col, int depth) { int index = round(dot(vec3(row, col, depth), vec3(${s}, ${a}, 1))); - ${uc(r)} + ${pc(r)} } `; let c = r.shapeInfo.texShape, l = c[0], m = c[1], d = r.shapeInfo.flatOffset; @@ -16288,13 +16283,13 @@ function f8(r, e) { } ` : ` float ${n}(int row, int col, int depth) { - float texR = dot(vec2(row, col), vec2(${t6[1]}, 1)); + float texR = dot(vec2(row, col), vec2(${t10[1]}, 1)); float texC = float(depth); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0); return sampleTexture(${o}, uv); } `; - let f = Ru(o); + let f = Mu(o); return e ? ` float ${n}(int row, int col, int depth) { // Explicitly use integer operations as dot() only works on floats. @@ -16313,20 +16308,20 @@ function f8(r, e) { } `; } -function h8(r, e) { - let t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1), n = St(); +function O8(r, e) { + let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = wt(); if (e) return ` vec4 ${o}(int b2, int b, int row, int col) { - int valuesPerRow = int(ceil(float(${t6}Shape[3]) / 2.0)); - int texelsInBatch = valuesPerRow * int(ceil(float(${t6}Shape[2]) / 2.0)); + int valuesPerRow = int(ceil(float(${t10}Shape[3]) / 2.0)); + int texelsInBatch = valuesPerRow * int(ceil(float(${t10}Shape[2]) / 2.0)); int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2); - texelsInBatch *= ${t6}Shape[1]; + texelsInBatch *= ${t10}Shape[1]; index = b2 * texelsInBatch + index; - ivec2 packedTexShape = ivec2(ceil(float(${t6}TexShape[0]) / 2.0), ceil(float(${t6}TexShape[1]) / 2.0)); + ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0)); int texR = index / packedTexShape[1]; int texC = index - texR * packedTexShape[1]; - vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${t6}, uv); + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${t10}, uv); } `; let s = r.shapeInfo.logicalShape, a = s.length, i = r.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`; @@ -16338,18 +16333,18 @@ function h8(r, e) { int texR = index / ${c}; int texC = index - texR * ${c}; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u}); - return ${n.texture2D}(${t6}, uv); + return ${n.texture2D}(${t10}, uv); } `; } -function g8(r, e) { - let t6 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t6[3], a = t6[2] * s, i = t6[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t6); - if (p.length < t6.length) { - let b = pc(r, p), C = ["row", "col", "depth", "depth2"]; +function P8(r, e) { + let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[3], a = t10[2] * s, i = t10[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t10); + if (p.length < t10.length) { + let b = cc(r, p), C = ["row", "col", "depth", "depth2"]; return ` - ${ic(b, e)} + ${uc(b, e)} float ${n}(int row, int col, int depth, int depth2) { - return ${n}(${cc(C, u)}); + return ${n}(${lc(C, u)}); } `; } @@ -16358,7 +16353,7 @@ function g8(r, e) { float ${n}(int row, int col, int depth, int depth2) { int index = round(dot(vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, 1))); - ${uc(r)} + ${pc(r)} } `; let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`; @@ -16399,14 +16394,14 @@ function g8(r, e) { ` : ` float ${n}(int row, int col, int depth, int depth2) { float texR = dot(vec3(row, col, depth), - vec3(${t6[1] * t6[2]}, ${t6[2]}, 1)); + vec3(${t10[1] * t10[2]}, ${t10[2]}, 1)); float texC = float(depth2); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); return sampleTexture(${o}, uv); } `; - let x = Ru(o); + let x = Mu(o); return e ? ` float ${n}(int row, int col, int depth, int depth2) { // Explicitly use integer operations as dot() only works on floats. @@ -16428,14 +16423,14 @@ function g8(r, e) { } `; } -function x8(r) { - let e = r.shapeInfo.logicalShape, t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); +function M8(r) { + let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); if (p.length < e.length) { - let h = pc(r, p), g = ["row", "col", "depth", "depth2", "depth3"]; + let h = cc(r, p), g = ["row", "col", "depth", "depth2", "depth3"]; return ` - ${ic(h)} + ${uc(h)} float ${o}(int row, int col, int depth, int depth2, int depth3) { - return ${o}(${cc(g, u)}); + return ${o}(${lc(g, u)}); } `; } @@ -16446,7 +16441,7 @@ function x8(r) { vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, ${n})) + depth3; - ${uc(r)} + ${pc(r)} } `; let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1]; @@ -16458,7 +16453,7 @@ function x8(r) { vec4(${a}, ${s}, ${n}, 1)); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; if (d === n && c == null) @@ -16471,29 +16466,29 @@ function x8(r) { int texC = depth3; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${m}.0); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; - let f = Ru(t6); + let f = Mu(t10); return ` float ${o}(int row, int col, int depth, int depth2, int depth3) { // Explicitly use integer operations as dot() only works on floats. int index = row * ${i} + col * ${a} + depth * ${s} + depth2 * ${n} + depth3 + ${f}; vec2 uv = uvFromFlat(${m}, ${d}, index); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; } -function y8(r) { - let e = r.shapeInfo.logicalShape, t6 = r.name, o = "get" + t6.charAt(0).toUpperCase() + t6.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e); +function L8(r) { + let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e); if (n.length < e.length) { - let g = pc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; + let g = cc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; return ` - ${ic(g)} + ${uc(g)} float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { - return ${o}(${cc(x, s)}); + return ${o}(${lc(x, s)}); } `; } @@ -16508,7 +16503,7 @@ function y8(r) { dot( vec2(depth3, depth4), vec2(${a}, 1))); - ${uc(r)} + ${pc(r)} } `; let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, d = m[0], f = m[1]; @@ -16522,7 +16517,7 @@ function y8(r) { float(depth4); vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${f}.0, ${d}.0); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; if (f === a && l == null) @@ -16537,10 +16532,10 @@ function y8(r) { int texC = depth4; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${f}.0, ${d}.0); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; - let h = Ru(t6); + let h = Mu(t10); return ` float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { @@ -16548,22 +16543,22 @@ function y8(r) { int index = row * ${c} + col * ${u} + depth * ${p} + depth2 * ${i} + depth3 * ${a} + depth4 + ${h}; vec2 uv = uvFromFlat(${d}, ${f}, index); - return sampleTexture(${t6}, uv); + return sampleTexture(${t10}, uv); } `; } -function uc(r) { - let e = r.name, t6 = y.sizeFromShape(r.shapeInfo.logicalShape); - return t6 < 2 ? `return ${e};` : ` - for (int i = 0; i < ${t6}; i++) { +function pc(r) { + let e = r.name, t10 = y.sizeFromShape(r.shapeInfo.logicalShape); + return t10 < 2 ? `return ${e};` : ` + for (int i = 0; i < ${t10}; i++) { if (i == index) { return ${e}[i]; } } `; } -function b8(r, e) { - let t6 = r.name, o = t6.charAt(0).toUpperCase() + t6.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = NE(r.shapeInfo.logicalShape, e.logicalShape), p = _e(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; +function B8(r, e) { + let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = R$(r.shapeInfo.logicalShape, e.logicalShape), p = $e(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(` `); let m = ""; @@ -16592,15 +16587,15 @@ function b8(r, e) { } `; } -function C8(r, e) { - let t6 = r.name, o = t6.charAt(0).toUpperCase() + t6.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length; +function V8(r, e) { + let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length; if (!r.shapeInfo.isUniform && i === p && r.shapeInfo.flatOffset == null && y.arraysEqual(a, s)) return ` float ${n}() { - return sampleTexture(${t6}, resultUV); + return sampleTexture(${t10}, resultUV); } `; - let u = _e(p), c = NE(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; + let u = $e(p), c = R$(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${d[h + l]} = 0;`).join(` `); let f = ""; @@ -16612,7 +16607,7 @@ function C8(r, e) { } `; } -function _e(r) { +function $e(r) { if (r <= 1) return "int"; if (r === 2) @@ -16627,54 +16622,54 @@ function _e(r) { return "ivec6"; throw Error(`GPU for rank ${r} is not yet supported`); } -function Of(r, e, t6) { - let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t6) && o.length < s || a; +function Of(r, e, t10) { + let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t10) && o.length < s || a; return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n }; } -function pc(r, e) { - let t6 = JSON.parse(JSON.stringify(r)); - return t6.shapeInfo.logicalShape = e, t6; -} function cc(r, e) { - return e.map((t6) => r[t6]).join(", "); + let t10 = JSON.parse(JSON.stringify(r)); + return t10.shapeInfo.logicalShape = e, t10; } -function AE(r, e, t6, o) { - let n = t6.map((c, l) => { +function lc(r, e) { + return e.map((t10) => r[t10]).join(", "); +} +function M$(r, e, t10, o) { + let n = t10.map((c, l) => { let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null }; return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: e.variableNames[l], shapeInfo: m }; - }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = TE(n, a, e), p = MS(r.gl, i), u = r.createProgram(p); - return O().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, tw(r, e, u)); + }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = D$(n, a, e), p = jS(r.gl, i), u = r.createProgram(p); + return O().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, mw(r, e, u)); } -function tw(r, e, t6) { +function mw(r, e, t10) { let o = {}, n = {}, s = {}, a = [], i, p, u, c = null, l = null; - l = r.getUniformLocation(t6, "NAN", false), O().getNumber("WEBGL_VERSION") === 1 && (c = r.getUniformLocation(t6, "INFINITY", false)); + l = r.getUniformLocation(t10, "NAN", false), O().getNumber("WEBGL_VERSION") === 1 && (c = r.getUniformLocation(t10, "INFINITY", false)); let m = false; for (let d = 0; d < e.variableNames.length; d++) { let f = e.variableNames[d]; - o[f] = r.getUniformLocation(t6, f, m), o[`offset${f}`] = r.getUniformLocation(t6, `offset${f}`, m), e.enableShapeUniforms && (n[`${f}Shape`] = r.getUniformLocation(t6, `${f}Shape`, m), s[`${f}TexShape`] = r.getUniformLocation(t6, `${f}TexShape`, m)); + o[f] = r.getUniformLocation(t10, f, m), o[`offset${f}`] = r.getUniformLocation(t10, `offset${f}`, m), e.enableShapeUniforms && (n[`${f}Shape`] = r.getUniformLocation(t10, `${f}Shape`, m), s[`${f}TexShape`] = r.getUniformLocation(t10, `${f}TexShape`, m)); } - return e.enableShapeUniforms && (i = r.getUniformLocation(t6, "outShape", m), u = r.getUniformLocation(t6, "outShapeStrides", m), p = r.getUniformLocation(t6, "outTexShape", m)), e.customUniforms && e.customUniforms.forEach((d, f) => { - a[f] = r.getUniformLocation(t6, d.name, m); + return e.enableShapeUniforms && (i = r.getUniformLocation(t10, "outShape", m), u = r.getUniformLocation(t10, "outShapeStrides", m), p = r.getUniformLocation(t10, "outTexShape", m)), e.customUniforms && e.customUniforms.forEach((d, f) => { + a[f] = r.getUniformLocation(t10, d.name, m); }), { uniformLocations: o, customUniformLocations: a, infLoc: c, nanLoc: l, inShapesLocations: n, inTexShapesLocations: s, outShapeLocation: i, outShapeStridesLocation: u, outTexShapeLocation: p }; } -function $E(r, e) { +function P$(r, e) { if (r.length !== e.length) throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`); - r.forEach((t6, o) => { - let n = t6.logicalShape, s = e[o], a = s.shape; + r.forEach((t10, o) => { + let n = t10.logicalShape, s = e[o], a = s.shape; if (!y.arraysEqual(n, a)) throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`); - if (t6.isUniform && s.isUniform) + if (t10.isUniform && s.isUniform) return; - let i = t6.texShape, p = s.isUniform ? null : s.texData.texShape; + let i = t10.texShape, p = s.isUniform ? null : s.texData.texShape; if (!y.arraysEqual(i, p)) throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`); }); } -function RE(r, e, t6, o, n) { - e.program.enableShapeUniforms || ($E(e.inShapeInfos, t6), $E([e.outShapeInfo], [o])); +function L$(r, e, t10, o, n) { + e.program.enableShapeUniforms || (P$(e.inShapeInfos, t10), P$([e.outShapeInfo], [o])); let s = o.texData.texture, a = o.texData.texShape; - o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), O().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN), t6.forEach((p, u) => { + o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), O().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN), t10.forEach((p, u) => { let c = e.program.variableNames[u], l = e.uniformLocations[c], m = e.uniformLocations[`offset${c}`], d = e.inShapesLocations[`${c}Shape`], f = e.inTexShapesLocations[`${c}TexShape`]; if (d) { let { uniformShape: h } = Of(e.program.packedInputs, p.shape, p.texData.texShape); @@ -16764,9 +16759,9 @@ function RE(r, e, t6, o, n) { throw Error(`uniform type ${p.type} is not supported yet.`); }), r.executeProgram(); } -function FE(r, e, t6) { +function B$(r, e, t10) { let o = ""; - e.concat(t6).forEach((a) => { + e.concat(t10).forEach((a) => { let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0; if (r.enableShapeUniforms && !a.isUniform) { let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Of(r.packedInputs, a.shape, p), m = "", d = "", f = ""; @@ -16779,7 +16774,7 @@ function FE(r, e, t6) { let k = y.computeStrides(c); f = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`; } - let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = S.getBroadcastDims(a.shape, t6.shape), C = !r.packedInputs && h === t6.shape.length && y.arraysEqual(p, t6.texData.texShape), w = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; + let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = S.getBroadcastDims(a.shape, t10.shape), C = !r.packedInputs && h === t10.shape.length && y.arraysEqual(p, t10.texData.texShape), w = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; o += `${h}_${C}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`; } else { let p = a.isUniform ? "uniform" : a.texData.texShape; @@ -16789,16 +16784,16 @@ function FE(r, e, t6) { let n = r.userCode, s = r.constructor.name; return s += "_" + o + "_" + n + `${O().getNumber("WEBGL_VERSION")}`, s; } -function ct(r) { +function lt(r) { return O().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4; } var Pf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = Mi.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let t6 = St(); - this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = Li.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let t10 = wt(); + this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? Au(["r", "c", "d"], e) : us(["r", "c", "d"], e)} + ${this.enableShapeUniforms ? Pu(["r", "c", "d"], e) : ps(["r", "c", "d"], e)} return ivec3(r, c, d); } @@ -16814,18 +16809,18 @@ var Pf = class { result[i] = getA(rc.x, rc.y, rc.z); } - ${t6.output} = result; + ${t10.output} = result; } `; } }; var Mf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = Mi.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let t6 = St(); - this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = Li.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let t10 = wt(); + this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? Au(["r", "c", "d"], e) : us(["r", "c", "d"], e)} + ${this.enableShapeUniforms ? Pu(["r", "c", "d"], e) : ps(["r", "c", "d"], e)} return ivec3(r, c, d); } @@ -16841,58 +16836,58 @@ var Mf = class { result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); } - ${t6.output} = result; + ${t10.output} = result; } `; } }; var Lf = class { constructor(e) { - this.variableNames = ["A"], this.outTexUsage = ir.DOWNLOAD; - let t6 = St(); + this.variableNames = ["A"], this.outTexUsage = ur.DOWNLOAD; + let t10 = wt(); this.outputShape = e, this.userCode = ` - ${Df} + ${Ff} void main() { float x = getAAtOutCoords(); - ${t6.output} = encode_float(x); + ${t10.output} = encode_float(x); } `; } }; var Bf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = ir.DOWNLOAD; - let t6 = St(); + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = ur.DOWNLOAD; + let t10 = wt(); this.outputShape = e, this.userCode = ` - ${Df} + ${Ff} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); - ${t6.output} = encode_float(x); + ${t10.output} = encode_float(x); } `; } }; -var I8 = { R: 0, G: 1, B: 2, A: 3 }; -var vl = class { - constructor(e, t6 = false, o = "RGBA") { +var U8 = { R: 0, G: 1, B: 2, A: 3 }; +var Tl = class { + constructor(e, t10 = false, o = "RGBA") { this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let n = St(); - this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length); + let n = wt(); + this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length); let s = "result"; - t6 && (s = "floor(result * 255. + 0.5)"); + t10 && (s = "floor(result * 255. + 0.5)"); let a = ""; for (let i = 0; i < o.length; i++) { let p = o[i]; a += ` if(offset == ${i}) { - result = values[${I8[p]}]; + result = values[${U8[p]}]; }`; } this.userCode = ` - ${this.enableShapeUniforms ? sc() : nc(e)} + ${this.enableShapeUniforms ? ac() : sc(e)} void main() { ivec3 coords = getOutputCoords(); @@ -16915,12 +16910,12 @@ var vl = class { } }; var Vf = class { - constructor(e, t6 = false) { + constructor(e, t10 = false) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let o = St(); - this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length); + let o = wt(); + this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length); let n = "", s = "result"; - t6 && (s = "floor(result * 255. + 0.5)"); + t10 && (s = "floor(result * 255. + 0.5)"); for (let a = 0; a <= 1; a++) for (let i = 0; i <= 1; i++) { let p = a * 2 + i; @@ -16955,7 +16950,7 @@ var Vf = class { `; } this.userCode = ` - ${this.enableShapeUniforms ? sc() : nc(e)} + ${this.enableShapeUniforms ? ac() : sc(e)} void main() { ivec3 coords = getOutputCoords(); @@ -16973,10 +16968,10 @@ var Vf = class { `; } }; -var yw = {}; -Ue(yw, { bindVertexProgramAttributeStreams: () => cw, createBufferFromOutputTexture: () => dw, createFloat16MatrixTexture: () => aw, createFloat16PackedMatrixTexture: () => pw, createFloat32MatrixTexture: () => sw, createIndexBuffer: () => nw, createPackedMatrixTexture: () => uw, createUnsignedBytesMatrixTexture: () => iw, createVertexBuffer: () => ow, createVertexShader: () => rw, downloadByteEncodedFloatMatrixFromOutputTexture: () => hw, downloadFloat32MatrixFromBuffer: () => fw, downloadMatrixFromPackedOutputTexture: () => xw, downloadPackedMatrixFromBuffer: () => gw, getInternalFormatForFloat16MatrixTexture: () => Wf, getInternalFormatForFloat16PackedMatrixTexture: () => Hf, getInternalFormatForFloat32MatrixTexture: () => zf, getInternalFormatForPackedMatrixTexture: () => Gf, getInternalFormatForUnsignedBytesMatrixTexture: () => Uf, uploadDenseMatrixToTexture: () => lw, uploadPixelDataToTexture: () => mw }); -function rw(r) { - let e = St(), t6 = `${e.version} +var $w = {}; +Ge($w, { bindVertexProgramAttributeStreams: () => Sw, createBufferFromOutputTexture: () => vw, createFloat16MatrixTexture: () => xw, createFloat16PackedMatrixTexture: () => Cw, createFloat32MatrixTexture: () => gw, createIndexBuffer: () => hw, createPackedMatrixTexture: () => bw, createUnsignedBytesMatrixTexture: () => yw, createVertexBuffer: () => fw, createVertexShader: () => dw, downloadByteEncodedFloatMatrixFromOutputTexture: () => Nw, downloadFloat32MatrixFromBuffer: () => kw, downloadMatrixFromPackedOutputTexture: () => _w, downloadPackedMatrixFromBuffer: () => Tw, getInternalFormatForFloat16MatrixTexture: () => Wf, getInternalFormatForFloat16PackedMatrixTexture: () => Hf, getInternalFormatForFloat32MatrixTexture: () => zf, getInternalFormatForPackedMatrixTexture: () => Gf, getInternalFormatForUnsignedBytesMatrixTexture: () => Uf, uploadDenseMatrixToTexture: () => ww, uploadPixelDataToTexture: () => Iw }); +function dw(r) { + let e = wt(), t10 = `${e.version} precision highp float; ${e.attribute} vec3 clipSpacePos; ${e.attribute} vec2 uv; @@ -16986,120 +16981,120 @@ function rw(r) { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`; - return PS(r, t6); + return qS(r, t10); } -function ow(r) { +function fw(r) { let e = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]); - return VS(r, e); + return QS(r, e); } -function nw(r) { +function hw(r) { let e = new Uint16Array([0, 1, 2, 2, 1, 3]); - return zS(r, e); + return ZS(r, e); } -function kl(r, e, t6, o, n, s) { - US(e, t6); - let a = WS(r), i = r.TEXTURE_2D; - return ce(r, () => r.bindTexture(i, a)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), ce(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), O().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, e, t6, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, e, t6)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t6, e] }; +function _l(r, e, t10, o, n, s) { + ew(e, t10); + let a = JS(r), i = r.TEXTURE_2D; + return pe(r, () => r.bindTexture(i, a)), pe(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), pe(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), pe(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), pe(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), O().getNumber("WEBGL_VERSION") === 1 ? pe(r, () => r.texImage2D(i, 0, o, e, t10, 0, n, s, null)) : pe(r, () => r.texStorage2D(i, 1, o, e, t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t10, e] }; } function zf(r) { return r.internalFormatFloat; } -function sw(r, e, t6, o) { - let [n, s] = $u(e, t6); - return kl(r, n, s, zf(o), o.textureFormatFloat, r.FLOAT); +function gw(r, e, t10, o) { + let [n, s] = Ou(e, t10); + return _l(r, n, s, zf(o), o.textureFormatFloat, r.FLOAT); } function Wf(r) { return r.internalFormatHalfFloat; } -function aw(r, e, t6, o) { - let [n, s] = $u(e, t6); - return kl(r, n, s, Wf(o), o.textureFormatFloat, o.textureTypeHalfFloat); +function xw(r, e, t10, o) { + let [n, s] = Ou(e, t10); + return _l(r, n, s, Wf(o), o.textureFormatFloat, o.textureTypeHalfFloat); } function Uf(r) { return r.downloadTextureFormat; } -function iw(r, e, t6, o) { - let [n, s] = $u(e, t6); - return kl(r, n, s, Uf(o), r.RGBA, r.UNSIGNED_BYTE); +function yw(r, e, t10, o) { + let [n, s] = Ou(e, t10); + return _l(r, n, s, Uf(o), r.RGBA, r.UNSIGNED_BYTE); } function Gf(r) { return r.internalFormatPackedFloat; } -function uw(r, e, t6, o) { - let [n, s] = Ys(e, t6); - return kl(r, n, s, Gf(o), r.RGBA, r.FLOAT); +function bw(r, e, t10, o) { + let [n, s] = Qs(e, t10); + return _l(r, n, s, Gf(o), r.RGBA, r.FLOAT); } function Hf(r) { return r.internalFormatPackedHalfFloat; } -function pw(r, e, t6, o) { - let [n, s] = Ys(e, t6); - return kl(r, n, s, Hf(o), r.RGBA, o.textureTypeHalfFloat); +function Cw(r, e, t10, o) { + let [n, s] = Qs(e, t10); + return _l(r, n, s, Hf(o), r.RGBA, o.textureTypeHalfFloat); } -function cw(r, e, t6) { - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t6)), Af(r, e, "clipSpacePos", t6, 3, 20, 0) && Af(r, e, "uv", t6, 2, 20, 12); +function Sw(r, e, t10) { + return pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), Af(r, e, "clipSpacePos", t10, 3, 20, 0) && Af(r, e, "uv", t10, 2, 20, 12); } -function lw(r, e, t6, o, n, s) { - ce(r, () => r.bindTexture(r.TEXTURE_2D, e)); +function ww(r, e, t10, o, n, s) { + pe(r, () => r.bindTexture(r.TEXTURE_2D, e)); let a, i, p; - n instanceof Uint8Array ? (a = new Uint8Array(t6 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t6 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), O().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t6, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t6, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); + n instanceof Uint8Array ? (a = new Uint8Array(t10 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t10 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10, o, r.RGBA, i, a)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t10, o, 0, r.RGBA, i, a)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function mw(r, e, t6) { - ce(r, () => r.bindTexture(r.TEXTURE_2D, e)), t6.data instanceof Uint8Array ? O().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t6.width, t6.height, r.RGBA, r.UNSIGNED_BYTE, t6.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t6.width, t6.height, 0, r.RGBA, r.UNSIGNED_BYTE, t6.data)) : O().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t6)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t6)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function Iw(r, e, t10) { + pe(r, () => r.bindTexture(r.TEXTURE_2D, e)), t10.data instanceof Uint8Array ? O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10.width, t10.height, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t10.width, t10.height, 0, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t10)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null)); } -function dw(r, e, t6, o) { +function vw(r, e, t10, o) { let n = r.createBuffer(); - ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n)); - let i = 4 * 4 * e * t6; - return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, t6, e, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n; + pe(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n)); + let i = 4 * 4 * e * t10; + return pe(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), pe(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, 0)), pe(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n; } -function fw(r, e, t6) { - let o = r, n = new Float32Array(t6); +function kw(r, e, t10) { + let o = r, n = new Float32Array(t10); return o.bindBuffer(o.PIXEL_PACK_BUFFER, e), o.getBufferSubData(o.PIXEL_PACK_BUFFER, 0, n), o.bindBuffer(o.PIXEL_PACK_BUFFER, null), n; } -function hw(r, e, t6, o) { - let [n, s] = $u(e, t6), a = 4, i = new Uint8Array(bE(e * t6, a)); - return ce(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer); +function Nw(r, e, t10, o) { + let [n, s] = Ou(e, t10), a = 4, i = new Uint8Array(k$(e * t10, a)); + return pe(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer); } -function gw(r, e, t6, o, n, s, a, i) { - let p = r, u = new Float32Array(CE(s, a)); +function Tw(r, e, t10, o, n, s, a, i) { + let p = r, u = new Float32Array(N$(s, a)); return p.bindBuffer(p.PIXEL_PACK_BUFFER, e), p.getBufferSubData(p.PIXEL_PACK_BUFFER, 0, u), p.bindBuffer(p.PIXEL_PACK_BUFFER, null), u; } -function xw(r, e, t6) { - let o = new Float32Array(e * t6 * 4); - return ce(r, () => r.readPixels(0, 0, t6, e, r.RGBA, r.FLOAT, o)), o; +function _w(r, e, t10) { + let o = new Float32Array(e * t10 * 4); + return pe(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, o)), o; } -var Fu = class { +var Lu = class { constructor(e) { this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = []; - let t6 = O().getNumber("WEBGL_VERSION"); - if (e != null ? (this.gl = e, RS(t6, e)) : this.gl = Wr(t6), e = this.gl, O().getNumber("WEBGL_VERSION") === 2) { + let t10 = O().getNumber("WEBGL_VERSION"); + if (e != null ? (this.gl = e, US(t10, e)) : this.gl = zr(t10), e = this.gl, O().getNumber("WEBGL_VERSION") === 2) { let s = e; - this.createVertexArray = () => ce(s, () => s.createVertexArray()), this.bindVertexArray = (a) => ce(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => ce(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => ce(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING)); + this.createVertexArray = () => pe(s, () => s.createVertexArray()), this.bindVertexArray = (a) => pe(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => pe(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => pe(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING)); } else if (e != null) { let s = e.getExtension("OES_vertex_array_object"); if (s == null) throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object."); - this.createVertexArray = () => ce(e, () => s.createVertexArrayOES()), this.bindVertexArray = (a) => ce(e, () => s.bindVertexArrayOES(a)), this.deleteVertexArray = (a) => ce(e, () => s.deleteVertexArrayOES(a)), this.getVertexArray = () => ce(e, () => e.getParameter(s.VERTEX_ARRAY_BINDING_OES)); + this.createVertexArray = () => pe(e, () => s.createVertexArrayOES()), this.bindVertexArray = (a) => pe(e, () => s.bindVertexArrayOES(a)), this.deleteVertexArray = (a) => pe(e, () => s.deleteVertexArrayOES(a)), this.getVertexArray = () => pe(e, () => e.getParameter(s.VERTEX_ARRAY_BINDING_OES)); } let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float"; if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), O().getNumber("WEBGL_VERSION") === 1) { let s = "OES_texture_float", a = "OES_texture_half_float"; - if (this.textureFloatExtension = ec(this.gl, s), Ur(this.gl, a)) - this.textureHalfFloatExtension = ec(this.gl, a); + if (this.textureFloatExtension = tc(this.gl, s), Wr(this.gl, a)) + this.textureHalfFloatExtension = tc(this.gl, a); else if (O().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - if (this.colorBufferFloatExtension = this.gl.getExtension(o), Ur(this.gl, n)) - this.colorBufferHalfFloatExtension = ec(this.gl, n); + if (this.colorBufferFloatExtension = this.gl.getExtension(o), Wr(this.gl, n)) + this.colorBufferHalfFloatExtension = tc(this.gl, n); else if (O().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - } else if (o = "EXT_color_buffer_float", Ur(this.gl, o)) + } else if (o = "EXT_color_buffer_float", Wr(this.gl, o)) this.colorBufferFloatExtension = this.gl.getExtension(o); - else if (Ur(this.gl, n)) + else if (Wr(this.gl, n)) this.colorBufferHalfFloatExtension = this.gl.getExtension(n); else throw new Error("GL context does not support color renderable floats"); - this.vertexBuffer = ow(this.gl), this.indexBuffer = nw(this.gl), this.framebuffer = GS(this.gl), this.textureConfig = Sl(this.gl, this.textureHalfFloatExtension); + this.vertexBuffer = fw(this.gl), this.indexBuffer = hw(this.gl), this.framebuffer = tw(this.gl), this.textureConfig = vl(this.gl, this.textureHalfFloatExtension); } get debug() { return O().getBool("DEBUG"); @@ -17109,44 +17104,44 @@ var Fu = class { return; this.program != null && console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."), this.outputTexture != null && console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing."); let e = this.gl; - ce(e, () => e.finish()), ce(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), ce(e, () => e.deleteFramebuffer(this.framebuffer)), ce(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), ce(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), ce(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true; + pe(e, () => e.finish()), pe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), pe(e, () => e.deleteFramebuffer(this.framebuffer)), pe(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), pe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), pe(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true; } - createFloat32MatrixTexture(e, t6) { - return this.throwIfDisposed(), sw(this.gl, e, t6, this.textureConfig); + createFloat32MatrixTexture(e, t10) { + return this.throwIfDisposed(), gw(this.gl, e, t10, this.textureConfig); } - createFloat16MatrixTexture(e, t6) { - return this.throwIfDisposed(), aw(this.gl, e, t6, this.textureConfig); + createFloat16MatrixTexture(e, t10) { + return this.throwIfDisposed(), xw(this.gl, e, t10, this.textureConfig); } - createUnsignedBytesMatrixTexture(e, t6) { - return this.throwIfDisposed(), iw(this.gl, e, t6, this.textureConfig); + createUnsignedBytesMatrixTexture(e, t10) { + return this.throwIfDisposed(), yw(this.gl, e, t10, this.textureConfig); } - uploadPixelDataToTexture(e, t6) { - this.throwIfDisposed(), mw(this.gl, e, t6); + uploadPixelDataToTexture(e, t10) { + this.throwIfDisposed(), Iw(this.gl, e, t10); } - uploadDenseMatrixToTexture(e, t6, o, n) { - this.throwIfDisposed(), lw(this.gl, e, t6, o, n, this.textureConfig); + uploadDenseMatrixToTexture(e, t10, o, n) { + this.throwIfDisposed(), ww(this.gl, e, t10, o, n, this.textureConfig); } - createFloat16PackedMatrixTexture(e, t6) { - return this.throwIfDisposed(), pw(this.gl, e, t6, this.textureConfig); + createFloat16PackedMatrixTexture(e, t10) { + return this.throwIfDisposed(), Cw(this.gl, e, t10, this.textureConfig); } - createPackedMatrixTexture(e, t6) { - return this.throwIfDisposed(), uw(this.gl, e, t6, this.textureConfig); + createPackedMatrixTexture(e, t10) { + return this.throwIfDisposed(), bw(this.gl, e, t10, this.textureConfig); } deleteMatrixTexture(e) { - this.throwIfDisposed(), this.outputTexture === e && (Rf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(e)); + this.throwIfDisposed(), this.outputTexture === e && (Rf(this.gl, this.framebuffer), this.outputTexture = null), pe(this.gl, () => this.gl.deleteTexture(e)); } - downloadByteEncodedFloatMatrixFromOutputTexture(e, t6, o) { - return this.downloadMatrixDriver(e, () => hw(this.gl, t6, o, this.textureConfig)); + downloadByteEncodedFloatMatrixFromOutputTexture(e, t10, o) { + return this.downloadMatrixDriver(e, () => Nw(this.gl, t10, o, this.textureConfig)); } - downloadPackedMatrixFromBuffer(e, t6, o, n, s, a) { - return gw(this.gl, e, t6, o, n, s, a, this.textureConfig); + downloadPackedMatrixFromBuffer(e, t10, o, n, s, a) { + return Tw(this.gl, e, t10, o, n, s, a, this.textureConfig); } - downloadFloat32MatrixFromBuffer(e, t6) { - return fw(this.gl, e, t6); + downloadFloat32MatrixFromBuffer(e, t10) { + return kw(this.gl, e, t10); } - createBufferFromTexture(e, t6, o) { + createBufferFromTexture(e, t10, o) { this.bindTextureToFrameBuffer(e); - let n = dw(this.gl, t6, o, this.textureConfig); + let n = vw(this.gl, t10, o, this.textureConfig); return this.unbindTextureToFrameBuffer(), n; } createAndWaitForFence() { @@ -17154,78 +17149,78 @@ var Fu = class { return this.pollFence(e); } createFence(e) { - let t6, o; + let t10, o; if (O().getBool("WEBGL_FENCE_API_ENABLED")) { let n = e, s = n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE, 0); e.flush(), o = () => { let a = n.clientWaitSync(s, 0, 0); return a === n.ALREADY_SIGNALED || a === n.CONDITION_SATISFIED; - }, t6 = s; + }, t10 = s; } else - O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t6 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t6, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true; - return { query: t6, isFencePassed: o }; + O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t10 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t10, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true; + return { query: t10, isFencePassed: o }; } - downloadMatrixFromPackedTexture(e, t6, o) { - return this.downloadMatrixDriver(e, () => xw(this.gl, t6, o)); + downloadMatrixFromPackedTexture(e, t10, o) { + return this.downloadMatrixDriver(e, () => _w(this.gl, t10, o)); } createProgram(e) { this.throwIfDisposed(); - let t6 = this.gl; - this.vertexShader == null && (this.vertexShader = rw(t6)); - let o = LS(t6); - ce(t6, () => t6.attachShader(o, this.vertexShader)), ce(t6, () => t6.attachShader(o, e)), BS(t6, o); + let t10 = this.gl; + this.vertexShader == null && (this.vertexShader = dw(t10)); + let o = XS(t10); + pe(t10, () => t10.attachShader(o, this.vertexShader)), pe(t10, () => t10.attachShader(o, e)), YS(t10, o); let n; - return n = Object.assign(o, { vao: this.createVertexArray() }), this.bindVertexArray(n.vao), ce(t6, () => t6.bindBuffer(t6.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), console.assert(cw(t6, n, this.vertexBuffer), "gpgpu_util.bindVertexProgramAttributeStreams not fully successful."), this.debug && wl(t6, n), this.setProgram(n), n; + return n = Object.assign(o, { vao: this.createVertexArray() }), this.bindVertexArray(n.vao), pe(t10, () => t10.bindBuffer(t10.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), console.assert(Sw(t10, n, this.vertexBuffer), "gpgpu_util.bindVertexProgramAttributeStreams not fully successful."), this.debug && kl(t10, n), this.setProgram(n), n; } deleteProgram(e) { - this.throwIfDisposed(), e === this.program && (this.program = null), e != null && (ce(this.gl, () => this.gl.deleteProgram(e)), this.deleteVertexArray(e.vao)); + this.throwIfDisposed(), e === this.program && (this.program = null), e != null && (pe(this.gl, () => this.gl.deleteProgram(e)), this.deleteVertexArray(e.vao)); } setProgram(e) { - this.throwIfDisposed(), this.program = e, this.program != null && (this.bindVertexArray(this.program.vao), this.debug && wl(this.gl, this.program)), ce(this.gl, () => this.gl.useProgram(e)); + this.throwIfDisposed(), this.program = e, this.program != null && (this.bindVertexArray(this.program.vao), this.debug && kl(this.gl, this.program)), pe(this.gl, () => this.gl.useProgram(e)); } - getUniformLocation(e, t6, o = true) { - return this.throwIfDisposed(), o ? HS(this.gl, e, t6) : qS(this.gl, e, t6); + getUniformLocation(e, t10, o = true) { + return this.throwIfDisposed(), o ? rw(this.gl, e, t10) : ow(this.gl, e, t10); } - getAttributeLocation(e, t6) { - return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(e, t6)); + getAttributeLocation(e, t10) { + return this.throwIfDisposed(), pe(this.gl, () => this.gl.getAttribLocation(e, t10)); } - getUniformLocationNoThrow(e, t6) { - return this.throwIfDisposed(), this.gl.getUniformLocation(e, t6); + getUniformLocationNoThrow(e, t10) { + return this.throwIfDisposed(), this.gl.getUniformLocation(e, t10); } - setInputMatrixTexture(e, t6, o) { - this.throwIfDisposed(), this.throwIfNoProgram(), KS(this.gl, e, t6, o); + setInputMatrixTexture(e, t10, o) { + this.throwIfDisposed(), this.throwIfNoProgram(), nw(this.gl, e, t10, o); } - setOutputMatrixTexture(e, t6, o) { - this.setOutputMatrixTextureDriver(e, o, t6); + setOutputMatrixTexture(e, t10, o) { + this.setOutputMatrixTextureDriver(e, o, t10); } - setOutputPackedMatrixTexture(e, t6, o) { + setOutputPackedMatrixTexture(e, t10, o) { this.throwIfDisposed(); - let [n, s] = Ys(t6, o); + let [n, s] = Qs(t10, o); this.setOutputMatrixTextureDriver(e, n, s); } - setOutputMatrixWriteRegion(e, t6, o, n) { - this.setOutputMatrixWriteRegionDriver(o, e, n, t6); + setOutputMatrixWriteRegion(e, t10, o, n) { + this.setOutputMatrixWriteRegionDriver(o, e, n, t10); } - setOutputPackedMatrixWriteRegion(e, t6, o, n) { + setOutputPackedMatrixWriteRegion(e, t10, o, n) { throw new Error("setOutputPackedMatrixWriteRegion not implemented."); } debugValidate() { - this.program != null && wl(this.gl, this.program), tc(this.gl); + this.program != null && kl(this.gl, this.program), rc(this.gl); } executeProgram() { this.throwIfDisposed(), this.throwIfNoProgram(); let e = this.gl; if (this.debug) { - let t6 = this.getVertexArray(); - console.assert(t6 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate(); + let t10 = this.getVertexArray(); + console.assert(t10 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate(); } - ce(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0)); + pe(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0)); } blockUntilAllProgramsCompleted() { - this.throwIfDisposed(), ce(this.gl, () => this.gl.finish()); + this.throwIfDisposed(), pe(this.gl, () => this.gl.finish()); } getQueryTimerExtension() { - return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = ec(this.gl, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; + return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = tc(this.gl, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; } getQueryTimerExtensionWebGL2() { return this.getQueryTimerExtension(); @@ -17238,13 +17233,13 @@ var Fu = class { let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.createQuery(); return o.beginQuery(n.TIME_ELAPSED_EXT, s), s; } - let e = this.getQueryTimerExtensionWebGL1(), t6 = e.createQueryEXT(); - return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t6), t6; + let e = this.getQueryTimerExtensionWebGL1(), t10 = e.createQueryEXT(); + return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t10), t10; } endQuery() { if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { - let t6 = this.gl, o = this.getQueryTimerExtensionWebGL2(); - t6.endQuery(o.TIME_ELAPSED_EXT); + let t10 = this.gl, o = this.getQueryTimerExtensionWebGL2(); + t10.endQuery(o.TIME_ELAPSED_EXT); return; } let e = this.getQueryTimerExtensionWebGL1(); @@ -17253,10 +17248,10 @@ var Fu = class { async waitForQueryAndGetTime(e) { return await y.repeatedTry(() => this.disposed || this.isQueryAvailable(e, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); } - getQueryTime(e, t6) { - if (t6 === 0) + getQueryTime(e, t10) { + if (t10 === 0) return null; - if (t6 === 2) { + if (t10 === 2) { let o = this.gl; return o.getQueryParameter(e, o.QUERY_RESULT) / 1e6; } else { @@ -17264,10 +17259,10 @@ var Fu = class { return o.getQueryObjectEXT(e, o.QUERY_RESULT_EXT) / 1e6; } } - isQueryAvailable(e, t6) { - if (t6 === 0) + isQueryAvailable(e, t10) { + if (t10 === 0) return true; - if (t6 === 2) { + if (t10 === 2) { let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.getQueryParameter(e, o.QUERY_RESULT_AVAILABLE); return this.disjoint == null && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), s && !this.disjoint; } else { @@ -17276,42 +17271,42 @@ var Fu = class { } } pollFence(e) { - return new Promise((t6) => { - this.addItemToPoll(() => e.isFencePassed(), () => t6()); + return new Promise((t10) => { + this.addItemToPoll(() => e.isFencePassed(), () => t10()); }); } pollItems() { - let e = v8(this.itemsToPoll.map((t6) => t6.isDoneFn)); - for (let t6 = 0; t6 <= e; ++t6) { - let { resolveFn: o } = this.itemsToPoll[t6]; + let e = G8(this.itemsToPoll.map((t10) => t10.isDoneFn)); + for (let t10 = 0; t10 <= e; ++t10) { + let { resolveFn: o } = this.itemsToPoll[t10]; o(); } this.itemsToPoll = this.itemsToPoll.slice(e + 1); } - addItemToPoll(e, t6) { - if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t6 }), this.itemsToPoll.length > 1) + addItemToPoll(e, t10) { + if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t10 }), this.itemsToPoll.length > 1) return; let o; "setTimeoutCustom" in O().platform && (o = O().platform.setTimeoutCustom.bind(O().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o); } bindTextureToFrameBuffer(e) { - this.throwIfDisposed(), Il(this.gl, e, this.framebuffer), this.debug && tc(this.gl); + this.throwIfDisposed(), Nl(this.gl, e, this.framebuffer), this.debug && rc(this.gl); } unbindTextureToFrameBuffer() { - this.outputTexture != null ? (Il(this.gl, this.outputTexture, this.framebuffer), this.debug && tc(this.gl)) : Rf(this.gl, this.framebuffer); + this.outputTexture != null ? (Nl(this.gl, this.outputTexture, this.framebuffer), this.debug && rc(this.gl)) : Rf(this.gl, this.framebuffer); } - downloadMatrixDriver(e, t6) { + downloadMatrixDriver(e, t10) { this.bindTextureToFrameBuffer(e); - let o = t6(); + let o = t10(); return this.unbindTextureToFrameBuffer(), o; } - setOutputMatrixTextureDriver(e, t6, o) { + setOutputMatrixTextureDriver(e, t10, o) { this.throwIfDisposed(); let n = this.gl; - Il(n, e, this.framebuffer), this.debug && tc(n), this.outputTexture = e, ce(n, () => n.viewport(0, 0, t6, o)), ce(n, () => n.scissor(0, 0, t6, o)); + Nl(n, e, this.framebuffer), this.debug && rc(n), this.outputTexture = e, pe(n, () => n.viewport(0, 0, t10, o)), pe(n, () => n.scissor(0, 0, t10, o)); } - setOutputMatrixWriteRegionDriver(e, t6, o, n) { - this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(e, t6, o, n)); + setOutputMatrixWriteRegionDriver(e, t10, o, n) { + this.throwIfDisposed(), pe(this.gl, () => this.gl.scissor(e, t10, o, n)); } throwIfDisposed() { if (this.disposed) @@ -17322,37 +17317,37 @@ var Fu = class { throw new Error("No GPU program is currently set."); } }; -function v8(r) { +function G8(r) { let e = 0; for (; e < r.length && r[e](); ++e) ; return e - 1; } -var { addImpl: DE, bincountImpl: qf, bincountReduceImpl: OE, castImpl: PE, ceilImpl: ME, concatImpl: LE, equalImpl: BE, expImpl: VE, expm1Impl: zE, floorImpl: WE, gatherNdImpl: UE, gatherV2Impl: GE, greaterImpl: HE, greaterEqualImpl: qE, lessImpl: KE, lessEqualImpl: jE, linSpaceImpl: XE, logImpl: YE, maxImpl: QE, maximumImpl: ZE, minimumImpl: JE, multiplyImpl: e$, negImpl: t$, notEqualImpl: r$, prodImpl: o$, raggedGatherImpl: n$, raggedRangeImpl: s$, raggedTensorToTensorImpl: a$, rangeImpl: i$, rsqrtImpl: u$, scatterImpl: p$, sigmoidImpl: c$, simpleAbsImpl: Kf, sliceImpl: l$, sparseFillEmptyRowsImpl: m$, sparseReshapeImpl: d$, sparseSegmentReductionImpl: jf, sqrtImpl: f$, stridedSliceImpl: h$, stringNGramsImpl: g$, stringSplitImpl: x$, stringToHashBucketFastImpl: y$, subImpl: b$, tileImpl: C$, topKImpl: S$, transposeImpl: Du, uniqueImpl: w$ } = Qp; -function bw(r, e) { - return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t6) => `${r}.${t6}`); +var { addImpl: V$, bincountImpl: Kf, bincountReduceImpl: z$, castImpl: W$, ceilImpl: U$, concatImpl: G$, equalImpl: H$, expImpl: K$, expm1Impl: q$, floorImpl: j$, gatherNdImpl: X$, gatherV2Impl: Y$, greaterImpl: Q$, greaterEqualImpl: Z$, lessImpl: J$, lessEqualImpl: eE, linSpaceImpl: tE, logImpl: rE, maxImpl: oE, maximumImpl: nE, minimumImpl: sE, multiplyImpl: aE, negImpl: iE, notEqualImpl: uE, prodImpl: pE, raggedGatherImpl: cE, raggedRangeImpl: lE, raggedTensorToTensorImpl: mE, rangeImpl: dE, rsqrtImpl: fE, scatterImpl: hE, sigmoidImpl: gE, simpleAbsImpl: qf, sliceImpl: xE, sparseFillEmptyRowsImpl: yE, sparseReshapeImpl: bE, sparseSegmentReductionImpl: jf, sqrtImpl: CE, stridedSliceImpl: SE, stringNGramsImpl: wE, stringSplitImpl: IE, stringToHashBucketFastImpl: vE, subImpl: kE, tileImpl: NE, topKImpl: TE, transposeImpl: Bu, uniqueImpl: _E } = Zp; +function Ew(r, e) { + return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t10) => `${r}.${t10}`); } -function $t(r, e) { - return e === 1 ? [r] : bw(r, e); +function Et(r, e) { + return e === 1 ? [r] : Ew(r, e); } -function I$(r, e) { +function $E(r, e) { if (r === 1) return "rc"; - let t6 = ""; + let t10 = ""; for (let o = 0; o < r; o++) - t6 += e[o], o < r - 1 && (t6 += ","); - return t6; + t10 += e[o], o < r - 1 && (t10 += ","); + return t10; } var Xf = class { constructor(e) { - if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = ct(this.outputShape.length), this.rank === 0) + if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = lt(this.outputShape.length), this.rank === 0) this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } `; else { - let t6 = $t("rc", this.rank), o = _e(this.rank), n = this.getOutOfBoundsCondition(t6), s = this.getSetup(t6), a = this.getOutput(t6); + let t10 = Et("rc", this.rank), o = $e(this.rank), n = this.getOutOfBoundsCondition(t10), s = this.getSetup(t10), a = this.getOutput(t10); this.userCode = ` void main() { ${o} rc = getOutputCoords(); @@ -17369,31 +17364,31 @@ var Xf = class { } } getSourceCoordsArr(e) { - let t6 = []; + let t10 = []; for (let o = 0; o <= 1; o++) for (let n = 0; n <= 1; n++) { let s = `${o === 0 ? "r" : "rp1"}, ${n === 0 ? "c" : "cp1"}`; for (let a = 2; a < this.rank; a++) s = `${e[e.length - 1 - a]},` + s; - t6.push(s); + t10.push(s); } - return t6; + return t10; } getOutOfBoundsCondition(e) { if (this.rank === 1) return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`; - let t6 = ""; + let t10 = ""; for (let o = this.rank - 2; o < this.rank; o++) - t6 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t6 += "||"); - return t6; + t10 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t10 += "||"); + return t10; } getSetup(e) { if (this.rank === 1) return ""; - let t6 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; + let t10 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; return ` - int r = ${t6[0]}; - int c = ${t6[1]}; + int r = ${t10[0]}; + int c = ${t10[1]}; int rp1 = r + 1; int cp1 = c + 1; @@ -17402,16 +17397,16 @@ var Xf = class { `; } getOutput(e) { - let t6 = this.getSourceCoordsArr(e); - return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t6[0]}), - cEdge ? 0. : getA(${t6[1]}), - rEdge ? 0. : getA(${t6[2]}), - rEdge || cEdge ? 0. : getA(${t6[3]})`; + let t10 = this.getSourceCoordsArr(e); + return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t10[0]}), + cEdge ? 0. : getA(${t10[1]}), + rEdge ? 0. : getA(${t10[2]}), + rEdge || cEdge ? 0. : getA(${t10[3]})`; } }; -var lc = class { - constructor(e, t6) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length); +var mc = class { + constructor(e, t10) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length); let o = ""; for (let n = 0; n < 4; n++) { let s = "thisRC = rc;"; @@ -17429,8 +17424,8 @@ var lc = class { `; } this.userCode = ` - ${k8(t6, this.enableShapeUniforms)} - ${this.enableShapeUniforms ? sc() : nc(e)} + ${H8(t10, this.enableShapeUniforms)} + ${this.enableShapeUniforms ? ac() : sc(e)} void main() { ivec3 rc = getOutputCoords(); @@ -17448,10 +17443,10 @@ var lc = class { `; } }; -function k8(r, e) { +function H8(r, e) { return ` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${e ? kE(["r", "c", "d"], "inputShape") : us(["r", "c", "d"], r)} + ${e ? A$(["r", "c", "d"], "inputShape") : ps(["r", "c", "d"], r)} return ivec3(r, c, d); } `; @@ -17460,10 +17455,10 @@ var Yf = class { constructor(e) { this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {}; } - acquireTexture(e, t6, o) { - let n = k$(t6, o), s = N$(e, n, o); + acquireTexture(e, t10, o) { + let n = AE(t10, o), s = RE(e, n, o); s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []); - let a = v$(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); + let a = EE(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); if (this.freeTextures[s].length > 0) { this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log(); let p = this.freeTextures[s].shift(); @@ -17472,12 +17467,12 @@ var Yf = class { let i; return n === Zt.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === Zt.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === Zt.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === Zt.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === Zt.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i; } - releaseTexture(e, t6, o, n) { + releaseTexture(e, t10, o, n) { if (this.freeTextures == null) return; - let s = k$(o, n), a = N$(t6, s, n); + let s = AE(o, n), a = RE(t10, s, n); a in this.freeTextures || (this.freeTextures[a] = []); - let i = v$(t6, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = O().get("WEBGL_DELETE_TEXTURE_THRESHOLD"); + let i = EE(t10, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = O().get("WEBGL_DELETE_TEXTURE_THRESHOLD"); p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--; let u = this.usedTextures[a], c = u.indexOf(e); if (c < 0) @@ -17489,8 +17484,8 @@ var Yf = class { return; let e = this.numFreeTextures + this.numUsedTextures; console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${e})`); - let t6 = this._numBytesFree / this._numBytesAllocated; - console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t6)}%)`); + let t10 = this._numBytesFree / this._numBytesAllocated; + console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t10)}%)`); } get numBytesAllocated() { return this._numBytesAllocated; @@ -17507,46 +17502,46 @@ var Yf = class { dispose() { if (this.freeTextures != null) { for (let e in this.freeTextures) - this.freeTextures[e].forEach((t6) => { - this.gpgpu.deleteMatrixTexture(t6.texture); + this.freeTextures[e].forEach((t10) => { + this.gpgpu.deleteMatrixTexture(t10.texture); }); for (let e in this.usedTextures) - this.usedTextures[e].forEach((t6) => { - this.gpgpu.deleteMatrixTexture(t6.texture); + this.usedTextures[e].forEach((t10) => { + this.gpgpu.deleteMatrixTexture(t10.texture); }); this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0; } } }; -function N8(r, e) { - let t6 = r; - if (e === t6.R32F) +function K8(r, e) { + let t10 = r; + if (e === t10.R32F) return 4; - if (e === t6.R16F) + if (e === t10.R16F) return 2; - if (e === t6.RGBA32F) + if (e === t10.RGBA32F) return 16; if (e === r.RGBA) return 16; - if (e === t6.RGBA16F) + if (e === t10.RGBA16F) return 8; - if (e === t6.RGBA8) + if (e === t10.RGBA8) return 4; throw new Error(`Unknown internal format ${e}`); } -function v$(r, e, t6, o, n) { - let s = T8(e, o), a; +function EE(r, e, t10, o, n) { + let s = q8(e, o), a; if (n) { - let [p, u] = Ys(r[0], r[1]); + let [p, u] = Qs(r[0], r[1]); a = p * u; } else { - let [p, u] = $u(r[0], r[1]); + let [p, u] = Ou(r[0], r[1]); a = p * u; } - let i = N8(t6, s); + let i = K8(t10, s); return a * i; } -function T8(r, e) { +function q8(r, e) { switch (r) { case Zt.PACKED_2X2_FLOAT32: return Gf(e); @@ -17562,26 +17557,26 @@ function T8(r, e) { throw new Error(`Unknown physical texture type ${r}`); } } -function _8(r) { +function j8(r) { return O().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? Zt.PACKED_2X2_FLOAT32 : Zt.UNPACKED_FLOAT32 : r ? Zt.PACKED_2X2_FLOAT16 : Zt.UNPACKED_FLOAT16; } -function k$(r, e) { - if (r === ir.UPLOAD) +function AE(r, e) { + if (r === ur.UPLOAD) return Zt.PACKED_2X2_FLOAT32; - if (r === ir.RENDER || r == null) - return _8(e); - if (r === ir.DOWNLOAD || r === ir.PIXELS) + if (r === ur.RENDER || r == null) + return j8(e); + if (r === ur.DOWNLOAD || r === ur.PIXELS) return Zt.PACKED_4X1_UNSIGNED_BYTE; throw new Error(`Unknown logical texture type ${r}`); } -function N$(r, e, t6) { - return `${r[0]}_${r[1]}_${e}_${t6}`; +function RE(r, e, t10) { + return `${r[0]}_${r[1]}_${e}_${t10}`; } var Jt = class { - constructor(e, t6) { - this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length), this.userCode = ` + constructor(e, t10) { + this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = ` float unaryOperation(float x) { - ${t6} + ${t10} } void main() { @@ -17593,20 +17588,20 @@ var Jt = class { `; } }; -var Bt = "if (isnan(x)) return x;"; -var T$ = "return x;"; -var Cw = "return abs(x);"; -var _$ = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; -var E$ = Bt + ` +var zt = "if (isnan(x)) return x;"; +var DE = "return x;"; +var Aw = "return abs(x);"; +var FE = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; +var OE = zt + ` return (x < 0.0) ? 0.0 : x; `; -var $$ = Bt + ` +var PE = zt + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; -var Qs = "return x;"; -var A$ = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var F$ = "return x;"; -var D$ = ` +var Zs = "return x;"; +var ME = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var BE = "return x;"; +var VE = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -17616,7 +17611,7 @@ var D$ = ` return result; `; -var O$ = ` +var zE = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -17627,7 +17622,7 @@ var O$ = ` return result; `; -var P$ = ` +var WE = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -17638,12 +17633,12 @@ var P$ = ` return result; `; -var M$ = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var Ar = class { - constructor(e, t6) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length), this.userCode = ` +var UE = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var Er = class { + constructor(e, t10) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = ` vec4 unaryOperation(vec4 x) { - ${t6} + ${t10} } void main() { @@ -17657,8 +17652,8 @@ var Ar = class { }; var Qf = class { constructor(e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length); - let t6 = e.length, o = $t("rc", t6), n = _e(t6), s = I$(t6, o), a = o.slice(-2), i = t6 <= 1 ? "rc" : `vec2(${a.join(",")})`; + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length); + let t10 = e.length, o = Et("rc", t10), n = $e(t10), s = $E(t10, o), a = o.slice(-2), i = t10 <= 1 ? "rc" : `vec2(${a.join(",")})`; this.userCode = ` void main() { ${n} rc = getOutputCoords(); @@ -17669,81 +17664,81 @@ var Qf = class { `; } }; -var $8 = Lt.whereImpl; -var A8 = 1e-7; -var R8 = 1e-4; +var Y8 = Vt.whereImpl; +var Q8 = 1e-7; +var Z8 = 1e-4; var Zf = {}; -function F8(r) { +function J8(r) { return r in Zf || (Zf[r] = {}), Zf[r]; } -var D8 = O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); -var O8 = 600; -function P8() { - return O().global.screen == null ? 1024 : O().global.screen.height * O().global.screen.width * window.devicePixelRatio * O8 / 1024 / 1024; +var eY = O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); +var tY = 600; +function rY() { + return O().global.screen == null ? 1024 : O().global.screen.height * O().global.screen.width * window.devicePixelRatio * tY / 1024 / 1024; } -var Bi = class extends Zr { +var Vi = class extends Zr { constructor(e) { if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !O().getBool("HAS_WEBGL")) throw new Error("WebGL is not supported on this device"); - let t6; + let t10; if (e != null) { - if (e instanceof Fu) - t6 = e; + if (e instanceof Lu) + t10 = e; else { - let o = Wr(O().getNumber("WEBGL_VERSION"), e); - t6 = new Fu(o); + let o = zr(O().getNumber("WEBGL_VERSION"), e); + t10 = new Lu(o); } this.binaryCache = {}, this.gpgpuCreatedLocally = false; } else { - let o = Wr(O().getNumber("WEBGL_VERSION")); - t6 = new Fu(o), this.binaryCache = F8(O().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; + let o = zr(O().getNumber("WEBGL_VERSION")); + t10 = new Lu(o), this.binaryCache = J8(O().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; } - this.gpgpu = t6, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new Yf(this.gpgpu), this.numMBBeforeWarning = P8(), this.texData = new Do(this, cr()); + this.gpgpu = t10, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new Yf(this.gpgpu), this.numMBBeforeWarning = rY(), this.texData = new Fo(this, sr()); } nextDataId() { - return Bi.nextDataId++; + return Vi.nextDataId++; } numDataIds() { return this.texData.numDataIds() - this.pendingDeletes; } - writeTexture(e, t6, o, n, s, a) { - let i = this.makeTensorInfo(t6, o), p = this.texData.get(i.dataId); + writeTexture(e, t10, o, n, s, a) { + let i = this.makeTensorInfo(t10, o), p = this.texData.get(i.dataId); p.isPacked = false, p.texture = { texture: e, texShape: [n, s] }, p.texShape = [n, s]; - let u = rc(t6), c = new vl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); - return l.shape = t6, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId; + let u = oc(t10), c = new Tl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); + return l.shape = t10, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId; } - write(e, t6, o) { + write(e, t10, o) { if ((O().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || O().getBool("DEBUG")) && this.checkNumericalProblems(e), o === "complex64" && e != null) throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); let n = { id: this.nextDataId() }; - return this.texData.set(n, { shape: t6, dtype: o, values: e, usage: ir.UPLOAD, refCount: 1 }), n; + return this.texData.set(n, { shape: t10, dtype: o, values: e, usage: ur.UPLOAD, refCount: 1 }), n; } refCount(e) { return this.texData.has(e) ? this.texData.get(e).refCount : 0; } incRef(e) { - let t6 = this.texData.get(e); - t6.refCount++; + let t10 = this.texData.get(e); + t10.refCount++; } decRef(e) { if (this.texData.has(e)) { - let t6 = this.texData.get(e); - t6.refCount--; + let t10 = this.texData.get(e); + t10.refCount--; } } - move(e, t6, o, n, s) { - if (O().getBool("DEBUG") && this.checkNumericalProblems(t6), n === "complex64") + move(e, t10, o, n, s) { + if (O().getBool("DEBUG") && this.checkNumericalProblems(t10), n === "complex64") throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); - this.texData.set(e, { shape: o, dtype: n, values: t6, usage: ir.UPLOAD, refCount: s }); + this.texData.set(e, { shape: o, dtype: n, values: t10, usage: ur.UPLOAD, refCount: s }); } disposeIntermediateTensorInfo(e) { this.disposeData(e.dataId); } readSync(e) { - let t6 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t6; + let t10 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t10; if (a != null) { let m; - p ? m = new Ar(i, Qs) : m = new Jt(i, Qs); + p ? m = new Er(i, Zs) : m = new Jt(i, Zs); let d = this.runWebGLProgram(m, [{ dataId: e, shape: i, dtype: n }], n), f = this.readSync(d.dataId); return this.disposeIntermediateTensorInfo(d), f; } @@ -17766,10 +17761,10 @@ var Bi = class extends Zr { let f = this.pendingRead.get(e); return new Promise((h) => f.push(h)); } - let t6 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t6; + let t10 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t10; if (s != null) { let f; - p ? f = new Ar(n, Qs) : f = new Jt(n, Qs); + p ? f = new Er(n, Zs) : f = new Jt(n, Zs); let h = this.runWebGLProgram(f, [{ dataId: e, shape: n, dtype: a }], a), g = this.read(h.dataId); return this.disposeIntermediateTensorInfo(h), g; } @@ -17781,7 +17776,7 @@ var Bi = class extends Zr { if (a !== "complex64" && O().get("WEBGL_BUFFER_SUPPORTED")) { c = this.decode(e); let f = this.texData.get(c.dataId); - u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Cl(n)); + u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Il(n)); } this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence(); let l; @@ -17796,62 +17791,62 @@ var Bi = class extends Zr { } if (c != null && this.disposeIntermediateTensorInfo(c), u != null) { let f = this.gpgpu.gl; - ce(f, () => f.deleteBuffer(u)); + pe(f, () => f.deleteBuffer(u)); } let m = this.convertAndCacheOnCPU(e, l), d = this.pendingRead.get(e); - return this.pendingRead.delete(e), d.forEach((f) => f(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && cr().removeDataId(e, this), this.pendingDeletes--), m; + return this.pendingRead.delete(e), d.forEach((f) => f(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && sr().removeDataId(e, this), this.pendingDeletes--), m; } - readToGPU(e, t6 = {}) { + readToGPU(e, t10 = {}) { let o = this.texData.get(e), { values: n, shape: s, slice: a, dtype: i, isPacked: p, texture: u } = o; if (i === "complex64") throw new Error("Does not support reading texture for complex64 dtype."); if (a != null) { let d; - p ? d = new Ar(s, Qs) : d = new Jt(s, Qs); - let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t6); + p ? d = new Er(s, Zs) : d = new Jt(s, Zs); + let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t10); return this.disposeIntermediateTensorInfo(f), h; } if (u == null) throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); - let c = this.decode(e, t6.customTexShape), l = cr().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); + let c = this.decode(e, t10.customTexShape), l = sr().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); return Object.assign({ tensorRef: l }, m.texture); } bufferSync(e) { - let t6 = this.readSync(e.dataId); + let t10 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t6.map((n) => y.decodeString(n)); + let o = t10.map((n) => y.decodeString(n)); return le(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return le(e.shape, e.dtype, t6); + return le(e.shape, e.dtype, t10); } checkNumericalProblems(e) { if (e != null) - for (let t6 = 0; t6 < e.length; t6++) { - let o = e[t6]; - if (!OS(o)) + for (let t10 = 0; t10 < e.length; t10++) { + let o = e[t10]; + if (!KS(o)) throw O().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`); } } getValuesFromTexture(e) { - let { shape: t6, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t6); + let { shape: t10, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t10); if (O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { - let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Cl(t6)).subarray(0, s); + let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Il(t10)).subarray(0, s); return this.disposeIntermediateTensorInfo(m), f; } - let a = O().getBool("WEBGL_PACK") && n === true, i = a ? rc(t6) : t6, p = a ? new Bf(i) : new Lf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); + let a = O().getBool("WEBGL_PACK") && n === true, i = a ? oc(t10) : t10, p = a ? new Bf(i) : new Lf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); return this.disposeIntermediateTensorInfo(u), l; } timerAvailable() { return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0; } time(e) { - let t6 = this.activeTimers, o = [], n = false; + let t10 = this.activeTimers, o = [], n = false; this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e(); let s = y.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = y.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null); - this.activeTimers = t6, n && (this.programTimersStack = null); + this.activeTimers = t10, n && (this.programTimersStack = null); let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }; return (async () => { if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { @@ -17874,25 +17869,25 @@ var Bi = class extends Zr { async getQueryTime(e) { if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) return this.gpgpu.waitForQueryAndGetTime(e); - let t6 = e; - return t6.endMs - t6.startMs; + let t10 = e; + return t10.endMs - t10.startMs; } - disposeData(e, t6 = false) { + disposeData(e, t10 = false) { if (this.pendingDisposal.has(e)) return false; if (!this.texData.has(e)) return true; - if (t6 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t6 && this.texData.get(e).refCount > 0) + if (t10 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t10 && this.texData.get(e).refCount > 0) return false; if (this.pendingRead.has(e)) return this.pendingDisposal.add(e), this.pendingDeletes++, false; this.releaseGPUData(e); let { complexTensorInfos: o } = this.texData.get(e); - return o != null && (this.disposeData(o.real.dataId, t6), this.disposeData(o.imag.dataId, t6)), this.texData.delete(e), true; + return o != null && (this.disposeData(o.real.dataId, t10), this.disposeData(o.imag.dataId, t10)), this.texData.delete(e), true; } releaseGPUData(e) { - let { texture: t6, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p); - u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t6 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t6, n, s, a))); + let { texture: t10, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p); + u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t10 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t10, n, s, a))); let c = this.texData.get(e); c.texture = null, c.texShape = null, c.isPacked = false, c.slice = null; } @@ -17902,75 +17897,75 @@ var Bi = class extends Zr { getDataInfo(e) { return this.texData.get(e); } - shouldExecuteOnCPU(e, t6 = D8) { - return O().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t6); + shouldExecuteOnCPU(e, t10 = eY) { + return O().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t10); } getGPGPUContext() { return this.gpgpu; } where(e) { S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead"); - let t6 = e.dataSync(); - return $8(e.shape, t6); + let t10 = e.dataSync(); + return Y8(e.shape, t10); } - packedUnaryOp(e, t6, o) { - let n = new Ar(e.shape, t6), s = this.compileAndRun(n, [e], o); - return cr().makeTensorFromTensorInfo(s); + packedUnaryOp(e, t10, o) { + let n = new Er(e.shape, t10), s = this.compileAndRun(n, [e], o); + return sr().makeTensorFromTensorInfo(s); } abs(e) { if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") { - let n = Kf(this.texData.get(e.dataId).values); + let n = qf(this.texData.get(e.dataId).values); return this.makeOutput(e.shape, e.dtype, n); } if (O().getBool("WEBGL_PACK_UNARY_OPERATIONS")) - return this.packedUnaryOp(e, Cw, e.dtype); - let t6 = new Jt(e.shape, Cw), o = this.compileAndRun(t6, [e]); - return cr().makeTensorFromTensorInfo(o); + return this.packedUnaryOp(e, Aw, e.dtype); + let t10 = new Jt(e.shape, Aw), o = this.compileAndRun(t10, [e]); + return sr().makeTensorFromTensorInfo(o); } - makeTensorInfo(e, t6, o) { + makeTensorInfo(e, t10, o) { let n; - if (t6 === "string" && o != null && o.length > 0 && y.isString(o[0])) { + if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) { let s = o.map((a) => y.encodeString(a)); - n = this.write(s, e, t6); + n = this.write(s, e, t10); } else - n = this.write(o, e, t6); - return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t6 }; + n = this.write(o, e, t10); + return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t10 }; } - makeOutput(e, t6, o) { - return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e, t6, o), this); + makeOutput(e, t10, o) { + return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e, t10, o), this); } unpackTensor(e) { - let t6 = new Qf(e.shape); - return this.runWebGLProgram(t6, [e], e.dtype); + let t10 = new Qf(e.shape); + return this.runWebGLProgram(t10, [e], e.dtype); } packTensor(e) { - let t6 = new Xf(e.shape), o = true; - return this.runWebGLProgram(t6, [e], e.dtype, null, o); + let t10 = new Xf(e.shape), o = true; + return this.runWebGLProgram(t10, [e], e.dtype, null, o); } - packedReshape(e, t6) { - let o = [Va(e.shape), ...za(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [Va(t6), ...za(t6)], a = new lc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i); - return { dataId: u.dataId, shape: t6, dtype: u.dtype }; + packedReshape(e, t10) { + let o = [Ua(e.shape), ...Ga(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [Ua(t10), ...Ga(t10)], a = new mc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i); + return { dataId: u.dataId, shape: t10, dtype: u.dtype }; } - decode(e, t6) { + decode(e, t10) { let o = this.texData.get(e), { isPacked: n, shape: s, dtype: a } = o; - if (t6 != null) { - let m = y.sizeFromShape(s), d = t6[0] * t6[1] * 4; + if (t10 != null) { + let m = y.sizeFromShape(s), d = t10[0] * t10[1] * 4; y.assert(m <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."); } - let i = rc(s), p; + let i = oc(s), p; n ? p = new Mf(i) : p = new Pf(i); - let u = true, c = [t6 != null ? t6 : Cl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t6); + let u = true, c = [t10 != null ? t10 : Il(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t10); return { dtype: a, shape: s, dataId: l.dataId }; } - runWebGLProgram(e, t6, o, n, s = false, a) { + runWebGLProgram(e, t10, o, n, s = false, a) { let i = this.makeTensorInfo(e.outputShape, o), p = this.texData.get(i.dataId); - if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === Mi.DENSE) { - let x = a != null ? a : Cl(e.outputShape); + if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === Li.DENSE) { + let x = a != null ? a : Il(e.outputShape); p.texShape = x.map((b) => b * 2); } if (e.outTexUsage != null && (p.usage = e.outTexUsage), y.sizeFromShape(i.shape) === 0) return p.values = y.getTypedArrayFromDType(i.dtype, 0), i; - let u = [], c = t6.map((x) => { + let u = [], c = t10.map((x) => { if (x.dtype === "complex64") throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); let b = this.texData.get(x.dataId); @@ -17981,15 +17976,15 @@ var Bi = class extends Zr { } if (this.uploadToGPU(x.dataId), !!b.isPacked != !!e.packedInputs) x = b.isPacked ? this.unpackTensor(x) : this.packTensor(x), u.push(x), b = this.texData.get(x.dataId); - else if (b.isPacked && !Li(b.shape, x.shape)) { + else if (b.isPacked && !Bi(b.shape, x.shape)) { let C = x, w = x.shape; x.shape = b.shape, x = this.packedReshape(x, w), u.push(x), b = this.texData.get(x.dataId), C.shape = w; } return { shape: x.shape, texData: b, isUniform: false }; }); this.uploadToGPU(i.dataId); - let l = { shape: i.shape, texData: p, isUniform: false }, m = FE(e, c, l), d = this.getAndSaveBinary(m, () => AE(this.gpgpu, e, c, l)), f = this.activeTimers != null, h; - f && (h = this.startTimer()), O().get("ENGINE_COMPILE_ONLY") || RE(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) })); + let l = { shape: i.shape, texData: p, isUniform: false }, m = B$(e, c, l), d = this.getAndSaveBinary(m, () => M$(this.gpgpu, e, c, l)), f = this.activeTimers != null, h; + f && (h = this.startTimer()), O().get("ENGINE_COMPILE_ONLY") || L$(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) })); let g = O().get("WEBGL_FLUSH_THRESHOLD"); if (g > 0) { let x = y.now(); @@ -18001,18 +17996,18 @@ var Bi = class extends Zr { } return i; } - compileAndRun(e, t6, o, n, s = false) { - return o = o || t6[0].dtype, this.runWebGLProgram(e, t6, o, n, s); + compileAndRun(e, t10, o, n, s = false) { + return o = o || t10[0].dtype, this.runWebGLProgram(e, t10, o, n, s); } - getAndSaveBinary(e, t6) { - return e in this.binaryCache || (this.binaryCache[e] = t6()), this.binaryCache[e]; + getAndSaveBinary(e, t10) { + return e in this.binaryCache || (this.binaryCache[e] = t10()), this.binaryCache[e]; } getTextureManager() { return this.textureManager; } dispose() { - this.disposed || (O().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t6) => { - this.gpgpu.deleteProgram(this.binaryCache[t6].webGLProgram), delete this.binaryCache[t6]; + this.disposed || (O().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t10) => { + this.gpgpu.deleteProgram(this.binaryCache[t10].webGLProgram), delete this.binaryCache[t10]; }), this.textureManager.dispose(), this.canvas != null && typeof HTMLCanvasElement != "undefined" && this.canvas instanceof HTMLCanvasElement ? this.canvas.remove() : this.canvas = null, this.gpgpuCreatedLocally && (this.gpgpu.program = null, this.gpgpu.dispose()), this.disposed = true); } floatPrecision() { @@ -18020,48 +18015,48 @@ var Bi = class extends Zr { if (!O().get("WEBGL_RENDER_FLOAT32_ENABLED")) { let e = O().getBool("DEBUG"); O().set("DEBUG", false); - let t6 = this.abs(be(1e-8)).dataSync()[0]; - if (O().set("DEBUG", e), t6 > 0) + let t10 = this.abs(Ce(1e-8)).dataSync()[0]; + if (O().set("DEBUG", e), t10 > 0) return 32; } return 16; })), this.floatPrecisionValue; } epsilon() { - return this.floatPrecision() === 32 ? A8 : R8; + return this.floatPrecision() === 32 ? Q8 : Z8; } uploadToGPU(e) { - let t6 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t6; + let t10 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t10; if (a != null) return; let u = this.activeTimers != null, c; u && (c = y.now()); - let l = t6.texShape; - if (l == null && (l = jS(o, p), t6.texShape = l), s != null) { - let m = rc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; - (p || !g) && ([f, h] = Ys(l[0], l[1])), p ? d = new Vf(m, g) : d = new vl(m, g); + let l = t10.texShape; + if (l == null && (l = sw(o, p), t10.texShape = l), s != null) { + let m = oc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; + (p || !g) && ([f, h] = Qs(l[0], l[1])), p ? d = new Vf(m, g) : d = new Tl(m, g); let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), C = this.texData.get(b.dataId); - g ? C.usage = ir.PIXELS : C.usage = ir.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); - let w = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, w, k), $ = this.texData.get(_.dataId); - t6.texShape = $.texShape, t6.isPacked = $.isPacked, t6.usage = $.usage, O().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t6.texture = $.texture, t6.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c); + g ? C.usage = ur.PIXELS : C.usage = ur.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); + let w = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, w, k), E = this.texData.get(_.dataId); + t10.texShape = E.texShape, t10.isPacked = E.isPacked, t10.usage = E.usage, O().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t10.texture = E.texture, t10.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c); } else { let m = this.acquireTexture(l, i, n, p); - t6.texture = m; + t10.texture = m; } } - convertAndCacheOnCPU(e, t6) { + convertAndCacheOnCPU(e, t10) { let o = this.texData.get(e), { dtype: n } = o; - return t6 != null && (o.values = M8(t6, n)), o.values; + return t10 != null && (o.values = oY(t10, n)), o.values; } - acquireTexture(e, t6, o, n) { + acquireTexture(e, t10, o, n) { if (this.numBytesInGPU += this.computeBytes(e, o), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) { let s = (this.numBytesInGPU / 1024 / 1024).toFixed(2); this.warnedAboutMemory = true, console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`); } - return this.textureManager.acquireTexture(e, t6, n); + return this.textureManager.acquireTexture(e, t10, n); } - computeBytes(e, t6) { - return e[0] * e[1] * y.bytesPerElement(t6); + computeBytes(e, t10) { + return e[0] * e[1] * y.bytesPerElement(t10); } checkCompileCompletion() { for (let [, e] of Object.entries(this.binaryCache)) @@ -18070,14 +18065,14 @@ var Bi = class extends Zr { async checkCompileCompletionAsync() { let e = []; if (this.gpgpu.parallelCompilationExtension) { - for (let [, t6] of Object.entries(this.binaryCache)) - e.push(this.checkCompletionAsync_(t6)); + for (let [, t10] of Object.entries(this.binaryCache)) + e.push(this.checkCompletionAsync_(t10)); return Promise.all(e); } else { - for (let [, t6] of Object.entries(this.binaryCache)) { + for (let [, t10] of Object.entries(this.binaryCache)) { let o = new Promise((n) => { try { - this.checkCompletion_(t6), n(true); + this.checkCompletion_(t10), n(true); } catch (s) { throw s; } @@ -18088,52 +18083,53 @@ var Bi = class extends Zr { } } async checkCompletionAsync_(e) { - return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await CC(), this.checkCompletionAsync_(e)); + return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await AC(), this.checkCompletionAsync_(e)); } checkCompletion_(e) { if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) - throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? ($f(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); + throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Ef(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); return true; } getUniformLocations() { for (let [, e] of Object.entries(this.binaryCache)) { - let { uniformLocations: t6, customUniformLocations: o, infLoc: n, nanLoc: s, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: p, outShapeStridesLocation: u, outTexShapeLocation: c } = tw(this.gpgpu, e.program, e.webGLProgram); - e.uniformLocations = t6, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = p, e.outShapeStridesLocation = u, e.outTexShapeLocation = c; + let { uniformLocations: t10, customUniformLocations: o, infLoc: n, nanLoc: s, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: p, outShapeStridesLocation: u, outTexShapeLocation: c } = mw(this.gpgpu, e.program, e.webGLProgram); + e.uniformLocations = t10, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = p, e.outShapeStridesLocation = u, e.outTexShapeLocation = c; } } - createTensorFromTexture(e, t6, o) { - let { texture: n, height: s, width: a, channels: i } = e, p = cr().backend; + createTensorFromGPUData(e, t10, o) { + e.channels = e.channels || "RGBA"; + let { texture: n, height: s, width: a, channels: i } = e, p = sr().backend; if (!p.gpgpu.gl.isTexture(n)) throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'."); - let u = p.writeTexture(n, t6, o, s, a, i); - return cr().makeTensorFromDataId(u, t6, o, p); + let u = p.writeTexture(n, t10, o, s, a, i); + return sr().makeTensorFromDataId(u, t10, o, p); } }; -Bi.nextDataId = 0; -function M8(r, e) { +Vi.nextDataId = 0; +function oY(r, e) { if (e === "float32" || e === "complex64") return r; if (e === "int32" || e === "bool") { - let t6 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length); - for (let o = 0; o < t6.length; ++o) - t6[o] = Math.round(r[o]); - return t6; + let t10 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length); + for (let o = 0; o < t10.length; ++o) + t10[o] = Math.round(r[o]); + return t10; } else throw new Error(`Unknown dtype ${e}`); } -var L8 = "4.1.0"; -function L$() { +var nY = "4.2.0"; +function GE() { O().set("WEBGL_FORCE_F16_TEXTURES", true); } -yi.isBrowser() && Ci("webgl", () => new Bi(), 2); -var L9e = { forceHalfFloat: L$ }; -var mc = ` +ki.isBrowser() && Ti("webgl", () => new Vi(), 2); +var CJe = { forceHalfFloat: GE }; +var dc = ` if (isnan(a)) return a; if (isnan(b)) return b; `; var io = class { - constructor(e, t6, o) { - this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t6, o), this.enableShapeUniforms = ct(this.outputShape.length), this.userCode = ` + constructor(e, t10, o) { + this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = ` float binaryOperation(float a, float b) { ${e} } @@ -18146,17 +18142,17 @@ var io = class { `; } }; -var Zs = ` +var Js = ` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `; -var To = class { - constructor(e, t6, o, n = false) { - this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = S.assertAndGetBroadcastShape(t6, o); +var _o = class { + constructor(e, t10, o, n = false) { + this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = S.assertAndGetBroadcastShape(t10, o); let s = this.outputShape.length; - this.enableShapeUniforms = ct(s); + this.enableShapeUniforms = lt(s); let a = ""; if (n) if (s === 0 || y.sizeFromShape(this.outputShape) === 1) @@ -18166,7 +18162,7 @@ var To = class { result.w = 0.; `; else if (a = ` - ${_e(s)} coords = getOutputCoords(); + ${$e(s)} coords = getOutputCoords(); `, s === 1) this.enableShapeUniforms ? a += ` result.y = (coords + 1) >= outShape ? 0. : result.y; @@ -18178,7 +18174,7 @@ var To = class { result.w = 0.; `; else { - let p = $t("coords", s); + let p = Et("coords", s); this.enableShapeUniforms ? a += ` bool nextRowOutOfBounds = (${p[s - 2]} + 1) >= outShape[${s} - 2]; @@ -18215,55 +18211,55 @@ var To = class { } }; function At(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - return t6.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; + let { inputs: e, backend: t10 } = r, { x: o } = e; + return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var B$ = { kernelName: mo, backendName: "webgl", kernelFunc: At }; -function Rr(r) { - let { inputs: e, backend: t6 } = r, { real: o, imag: n } = e, s = t6.makeTensorInfo(o.shape, "complex64"), a = t6.texData.get(s.dataId), i = At({ inputs: { x: o }, backend: t6 }), p = At({ inputs: { x: n }, backend: t6 }); +var HE = { kernelName: mo, backendName: "webgl", kernelFunc: At }; +function Ar(r) { + let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.texData.get(s.dataId), i = At({ inputs: { x: o }, backend: t10 }), p = At({ inputs: { x: n }, backend: t10 }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var V$ = { kernelName: ei, backendName: "webgl", kernelFunc: Rr }; -var Sw = "return (a < 0.) ? b * a : a;"; -var ww = ` +var KE = { kernelName: ri, backendName: "webgl", kernelFunc: Ar }; +var Rw = "return (a < 0.) ? b * a : a;"; +var Dw = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function B8(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t6.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new To(ww, n.shape, a.shape) : new io(Sw, n.shape, a.shape), p = t6.runWebGLProgram(i, [n, a], "float32"); - return t6.disposeIntermediateTensorInfo(a), p; +function sY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t10.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(Dw, n.shape, a.shape) : new io(Rw, n.shape, a.shape), p = t10.runWebGLProgram(i, [n, a], "float32"); + return t10.disposeIntermediateTensorInfo(a), p; } -var z$ = { kernelName: mn, backendName: "webgl", kernelFunc: B8 }; -var Iw = "return (a < 0.) ? b * a : a;"; -var vw = ` +var qE = { kernelName: ln, backendName: "webgl", kernelFunc: sY }; +var Fw = "return (a < 0.) ? b * a : a;"; +var Ow = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function V8(r) { - let { inputs: e, backend: t6 } = r, { x: o, alpha: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new To(vw, o.shape, n.shape) : new io(Iw, o.shape, n.shape); - return t6.runWebGLProgram(s, [o, n], "float32"); +function aY(r) { + let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(Ow, o.shape, n.shape) : new io(Fw, o.shape, n.shape); + return t10.runWebGLProgram(s, [o, n], "float32"); } -var W$ = { kernelName: Rn, backendName: "webgl", kernelFunc: V8 }; -var _o = "if (isnan(x)) return x;"; -function ge({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t6, dtype: o }) { +var jE = { kernelName: Rn, backendName: "webgl", kernelFunc: aY }; +var $o = "if (isnan(x)) return x;"; +function he({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t10, dtype: o }) { return ({ inputs: n, backend: s }) => { let { x: a } = n, i = s, p = o || a.dtype; - if (i.shouldExecuteOnCPU([a]) && t6 != null) { - let l = i.texData.get(a.dataId), m = t6(l.values, p); + if (i.shouldExecuteOnCPU([a]) && t10 != null) { + let l = i.texData.get(a.dataId), m = t10(l.values, p); return i.makeTensorInfo(a.shape, p, m); } let u = O().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c; - return u ? c = new Ar(a.shape, e) : c = new Jt(a.shape, r), i.runWebGLProgram(c, [a], p); + return u ? c = new Er(a.shape, e) : c = new Jt(a.shape, r), i.runWebGLProgram(c, [a], p); }; } -function tt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t6 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { +function tt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t10 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { return ({ inputs: a, backend: i }) => { let { a: p, b: u } = a, c = i; if (o && p.dtype === "complex64") { let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((C) => { - let [w, k] = C, _ = { dataId: w.dataId, dtype: w.dtype, shape: p.shape }, $ = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, A = new io(r, p.shape, u.shape); - return c.runWebGLProgram(A, [_, $], dt(w.dtype, k.dtype)); - }), b = Rr({ inputs: { real: g, imag: x }, backend: c }); + let [w, k] = C, _ = { dataId: w.dataId, dtype: w.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, A = new io(r, p.shape, u.shape); + return c.runWebGLProgram(A, [_, E], dt(w.dtype, k.dtype)); + }), b = Ar({ inputs: { real: g, imag: x }, backend: c }); return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(x), b; } let l = s || dt(p.dtype, u.dtype); @@ -18272,29 +18268,29 @@ function tt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t6 = false, su return k.values = b, w; } let m = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d; - return m ? d = new To(e, p.shape, u.shape, t6) : d = new io(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); + return m ? d = new _o(e, p.shape, u.shape, t10) : d = new io(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); }; } -function Wa(r, e = false) { +function Ha(r, e = false) { if (r === "linear") - return e ? F$ : T$; + return e ? BE : DE; if (r === "relu") - return e ? O$ : E$; + return e ? zE : OE; if (r === "elu") - return e ? D$ : _$; + return e ? VE : FE; if (r === "relu6") - return e ? P$ : $$; + return e ? WE : PE; if (r === "prelu") - return e ? vw : Iw; + return e ? Ow : Fw; if (r === "leakyrelu") - return e ? ww : Sw; + return e ? Dw : Rw; if (r === "sigmoid") - return e ? M$ : A$; + return e ? UE : ME; throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`); } -var dc = class { - constructor(e, t6, o, n = false, s = false, a = false, i = null, p = false, u = false) { - this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = ct(this.outputShape.length); +var fc = class { + constructor(e, t10, o, n = false, s = false, a = false, i = null, p = false, u = false) { + this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = lt(this.outputShape.length); let c = n ? e[1] : e[2], l = Math.ceil(c / 2), m = n ? "i * 2, rc.y" : "rc.y, i * 2", d = s ? "rc.z, i * 2" : "i * 2, rc.z", f = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", x = ""; i && (p ? g = `vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); @@ -18308,16 +18304,16 @@ var dc = class { let b = a ? "result += getBiasAtOutCoords();" : ""; a && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha"); let C = "rc.x", w = "rc.x"; - e[0] < t6[0] ? C = `int(min(float(rc.x), ${e[0] - 1}.))` : t6[0] < e[0] && (w = `int(min(float(rc.x), ${t6[0] - 1}.))`), this.userCode = ` + e[0] < t10[0] ? C = `imod(rc.x, ${e[0]})` : t10[0] < e[0] && (w = `imod(rc.x, ${t10[0]})`), this.userCode = ` ${g} // Don't use uniform for sharedDimensionPacked for performance. const float sharedDimension = ${l}.0; vec4 dot2x2ARowBCol(ivec3 rc) { vec4 result = vec4(0); + int batchA = ${C}; + int batchB = ${w}; for (int i = 0; i < ${l}; i++) { - int batchA = ${C}; - int batchB = ${w}; vec4 a = getMatrixA(batchA, ${m}); vec4 b = getMatrixB(batchB, ${d}); @@ -18342,10 +18338,10 @@ var dc = class { `; } }; -var kw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; -var Nl = class { - constructor(e, t6, o) { - this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = S.assertAndGetBroadcastShape(t6, o), this.userCode = ` +var Pw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; +var $l = class { + constructor(e, t10, o) { + this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.userCode = ` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -18361,40 +18357,40 @@ var Nl = class { `; } }; -var U$ = "return a * b;"; -function Tl(r) { - let { inputs: e, backend: t6 } = r, { a: o, b: n } = e, s = S.upcastType(o.dtype, n.dtype); +var XE = "return a * b;"; +function El(r) { + let { inputs: e, backend: t10 } = r, { a: o, b: n } = e, s = S.upcastType(o.dtype, n.dtype); if (o.dtype === "complex64") { - let i = t6.texData.get(o.dataId), p = t6.texData.get(n.dataId), u = new Nl(kw.REAL, o.shape, n.shape), c = new Nl(kw.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t6.runWebGLProgram(u, l, "float32"), d = t6.runWebGLProgram(c, l, "float32"), f = Rr({ inputs: { real: m, imag: d }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d), f; + let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), u = new $l(Pw.REAL, o.shape, n.shape), c = new $l(Pw.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Ar({ inputs: { real: m, imag: d }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f; } - if (t6.shouldExecuteOnCPU([o, n])) { - let i = t6.texData.get(o.dataId), p = t6.texData.get(n.dataId), [u, c] = e$(o.shape, n.shape, i.values, p.values, s), l = t6.makeTensorInfo(c, s), m = t6.texData.get(l.dataId); + if (t10.shouldExecuteOnCPU([o, n])) { + let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), [u, c] = aE(o.shape, n.shape, i.values, p.values, s), l = t10.makeTensorInfo(c, s), m = t10.texData.get(l.dataId); return m.values = u, l; } let a; - return O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new To(U$, o.shape, n.shape) : a = new io(U$, o.shape, n.shape), t6.runWebGLProgram(a, [o, n], s); + return O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new _o(XE, o.shape, n.shape) : a = new io(XE, o.shape, n.shape), t10.runWebGLProgram(a, [o, n], s); } -var G$ = { kernelName: kn, backendName: "webgl", kernelFunc: Tl }; -function H$(r, e, t6) { - let o = [Va(r.shape), ...za(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [Va(e), ...za(e)], a = new lc(s, o), i = true, p = [o], u = t6.runWebGLProgram(a, [n], r.dtype, p, i); +var YE = { kernelName: kn, backendName: "webgl", kernelFunc: El }; +function QE(r, e, t10) { + let o = [Ua(r.shape), ...Ga(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [Ua(e), ...Ga(e)], a = new mc(s, o), i = true, p = [o], u = t10.runWebGLProgram(a, [n], r.dtype, p, i); return { dataId: u.dataId, shape: e, dtype: u.dtype }; } -function te(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t6, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); +function re(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t10, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); y.assert(i === u, () => `The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`); let c = a.texData.get(n.dataId); - return c.isPacked && !Li(n.shape, p) && !(c.texture !== null && Li(c.shape, p)) ? H$(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); + return c.isPacked && !Bi(n.shape, p) && !(c.texture !== null && Bi(c.shape, p)) ? QE(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); } -var q$ = { kernelName: Ns, backendName: "webgl", kernelFunc: te }; -var _l = class { - constructor(e, t6) { +var ZE = { kernelName: _s, backendName: "webgl", kernelFunc: re }; +var Al = class { + constructor(e, t10) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e; this.outputShape = [n, a]; let i = Math.floor(o / 4) * 4, p = o % 4, u = "sumValue += dot(values, ones);"; - if (t6 != null) { - let l = 1 / t6; + if (t10 != null) { + let l = 1 / t10; u = `sumValue += dot(values * ${y.isInt(l) ? l.toPrecision(2) : l}, ones);`; } let c = ""; @@ -18455,23 +18451,23 @@ var _l = class { } }; var Jf = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e; this.outputShape = [n, a]; let i = "0.0", p = ""; - t6 === "prod" ? i = "1.0" : t6 === "min" ? (i = "1.0 / 1e-20", p = "min") : t6 === "max" && (i = "-1.0 / 1e-20", p = "max"); - let u = `${t6}(${t6}(${t6}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t6 === "sum" ? u = "sumValue" : t6 === "prod" ? u = "prodValue" : t6 === "all" ? u = "allValue" : t6 === "any" && (u = "anyValue"); + t10 === "prod" ? i = "1.0" : t10 === "min" ? (i = "1.0 / 1e-20", p = "min") : t10 === "max" && (i = "-1.0 / 1e-20", p = "max"); + let u = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t10 === "sum" ? u = "sumValue" : t10 === "prod" ? u = "prodValue" : t10 === "all" ? u = "allValue" : t10 === "any" && (u = "anyValue"); let c = Math.floor(o / 4) * 4, l = o % 4, m = ` - if (${t6 === "sum"}) { + if (${t10 === "sum"}) { sumValue += dot(values, ones); - } else if (${t6 === "prod"}) { + } else if (${t10 === "prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { minMaxValue = ${p}(values, minMaxValue); - if (${t6 === "min"} || ${t6 === "max"}) { + if (${t10 === "min"} || ${t10 === "max"}) { minMaxValue = ${p}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { @@ -18480,11 +18476,11 @@ var Jf = class { } } `, d = "vec4"; - t6 === "all" ? (i = "1.0", m = ` + t10 === "all" ? (i = "1.0", m = ` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); - `, d = "bvec4") : t6 === "any" && (i = "0.0", m = ` + `, d = "bvec4") : t10 === "any" && (i = "0.0", m = ` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); @@ -18561,30 +18557,30 @@ var Jf = class { `; } }; -function W8(r) { +function uY(r) { let e = []; for (; e.length === 0 || e[e.length - 1].outSize !== 1; ) { - let t6 = e.length ? e[e.length - 1].outSize : r[1], o = S.computeOptimalWindowSize(t6); - e.push({ inSize: t6, windowSize: o, outSize: Math.ceil(t6 / o) }); + let t10 = e.length ? e[e.length - 1].outSize : r[1], o = S.computeOptimalWindowSize(t10); + e.push({ inSize: t10, windowSize: o, outSize: Math.ceil(t10 / o) }); } return e; } -function Gr(r, e, t6, o) { - let n = W8(r.shape), s = r; +function Ur(r, e, t10, o) { + let n = uY(r.shape), s = r; for (let a = 0; a < n.length; a++) { let { inSize: i, windowSize: p, outSize: u } = n[a], c, l; - t6 === "mean" ? c = a === 0 ? new _l({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new _l({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new Jf({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t6), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l); + t10 === "mean" ? c = a === 0 ? new Al({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Al({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new Jf({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t10), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l); } return s; } var eh = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["A"]; let o = new Array(e.length); for (let a = 0; a < o.length; a++) - o[a] = e[t6[a]]; + o[a] = e[t10[a]]; this.outputShape = o, this.rank = o.length; - let n = _e(this.rank), s = U8(t6); + let n = $e(this.rank), s = pY(t10); this.userCode = ` void main() { ${n} resRC = getOutputCoords(); @@ -18593,26 +18589,26 @@ var eh = class { `; } }; -function U8(r) { +function pY(r) { let e = r.length; if (e > 6) throw Error(`Transpose for rank ${e} is not yet supported`); - let t6 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e); + let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e); for (let n = 0; n < r.length; n++) - o[r[n]] = t6[n]; + o[r[n]] = t10[n]; return o.join(); } var th = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true; let o = new Array(e.length); for (let c = 0; c < o.length; c++) - o[c] = e[t6[c]]; + o[c] = e[t10[c]]; if (this.outputShape = o, this.rank = o.length, this.rank > 6) throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`); - let n = _e(this.rank), s = bw("rc", this.rank), a = new Array(this.rank); - for (let c = 0; c < t6.length; c++) - a[t6[c]] = s[c]; + let n = $e(this.rank), s = Ew("rc", this.rank), a = new Array(this.rank); + for (let c = 0; c < t10.length; c++) + a[t10[c]] = s[c]; let i = `vec2(${a.slice(-2).join()})`, p = `++${s[this.rank - 1]} < ${o[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`; this.userCode = ` void main() { @@ -18634,101 +18630,101 @@ var th = class { `; } }; -function Vi(r, e, t6) { +function zi(r, e, t10) { let o = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new th(r.shape, e) : new eh(r.shape, e); - return t6.runWebGLProgram(o, [r], r.dtype); + return t10.runWebGLProgram(o, [r], r.dtype); } -function K$(r, e, t6, o) { +function JE(r, e, t10, o) { let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = S.getAxesPermutation(i, s), u = p != null, c = r; - u && (c = Vi(r, p, o), i = S.getInnerMostAxes(i.length, s)), S.assertAxesAreInnerMostDims("sum", i, s); + u && (c = zi(r, p, o), i = S.getInnerMostAxes(i.length, s)), S.assertAxesAreInnerMostDims("sum", i, s); let [l, m] = S.computeOutAndReduceShapes(c.shape, i), d = l; - t6 && (d = S.expandShapeToKeepDim(l, a)); - let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = ka(r.dtype), C = Gr(x, b, "sum", o), w = te({ inputs: { x: C }, attrs: { shape: d }, backend: o }); + t10 && (d = S.expandShapeToKeepDim(l, a)); + let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = re({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = Ta(r.dtype), C = Ur(x, b, "sum", o), w = re({ inputs: { x: C }, attrs: { shape: d }, backend: o }); return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), w; } -function Ou(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return K$(n, s, a, t6); +function Vu(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return JE(n, s, a, t10); } -var j$ = { kernelName: Hn, backendName: "webgl", kernelFunc: Ou }; -function xt(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t6, i = n.shape.length, p = new Array(i); +var eA = { kernelName: Kn, backendName: "webgl", kernelFunc: Vu }; +function yt(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; let u; if (a.shouldExecuteOnCPU([n])) { - let l = a.texData.get(n.dataId).values, m = Du(l, n.shape, n.dtype, s, p); + let l = a.texData.get(n.dataId).values, m = Bu(l, n.shape, n.dtype, s, p); u = a.makeTensorInfo(p, n.dtype); let d = a.texData.get(u.dataId); d.values = m; } else - u = Vi(n, s, a); + u = zi(n, s, a); return u; } -var X$ = { kernelName: ro, backendName: "webgl", kernelFunc: xt }; -var Nw = 1e3; -function Pu({ a: r, b: e, transposeA: t6, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = e.shape.length, l = t6 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t6 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = br.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t6} and transposeB=${o} must match.`); - let k = t6 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], $ = te({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = te({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [$, A], D = Math.max(x, b), P = t6 ? $.shape[1] : $.shape[2], M = s != null, L = a != null, W = p === "leakyrelu", V = p != null ? Wa(p, true) : null, U = M || L || W || V != null, q; - if ((d === 1 || f === 1) && P > Nw && U === false) { - let j = $, X = A; - t6 && (j = xt({ inputs: { x: $ }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(j)), o && (X = xt({ inputs: { x: A }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(X)); - let Z = f !== 1, ee = f === 1, Y = j; - Z && (Y = te({ inputs: { x: j }, backend: n, attrs: { shape: [D, P, 1] } }), R.push(Y)); - let J = f === 1 ? 2 : 1, ie = X; - ee && (ie = te({ inputs: { x: X }, backend: n, attrs: { shape: [D, 1, P] } }), R.push(ie)); - let pe = Tl({ inputs: { a: Y, b: ie }, backend: n }); - q = Ou({ inputs: { x: pe }, backend: n, attrs: { axis: J, keepDims: true } }), R.push(pe); +var tA = { kernelName: ro, backendName: "webgl", kernelFunc: yt }; +var Mw = 1e3; +function zu({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = yr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`); + let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = re({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = re({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [E, A], D = Math.max(x, b), P = t10 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, V = p === "leakyrelu", z = p != null ? Ha(p, true) : null, U = M || L || V || z != null, K; + if ((d === 1 || f === 1) && P > Mw && U === false) { + let q = E, Z = A; + t10 && (q = yt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(q)), o && (Z = yt({ inputs: { x: A }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(Z)); + let ee = f !== 1, oe = f === 1, J = q; + ee && (J = re({ inputs: { x: q }, backend: n, attrs: { shape: [D, P, 1] } }), R.push(J)); + let te = f === 1 ? 2 : 1, ie = Z; + oe && (ie = re({ inputs: { x: Z }, backend: n, attrs: { shape: [D, 1, P] } }), R.push(ie)); + let ce = El({ inputs: { a: J, b: ie }, backend: n }); + K = Vu({ inputs: { x: ce }, backend: n, attrs: { axis: te, keepDims: true } }), R.push(ce); } else { - let j = dt(r.dtype, e.dtype), X = new dc(k, _, [D, d, f], t6, o, M, V, L, W), Z = [$, A]; - if (s != null && Z.push(s), L && Z.push(a), W) { - let ee = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32")); - Z.push(ee), R.push(ee); + let q = dt(r.dtype, e.dtype), Z = new fc(k, _, [D, d, f], t10, o, M, z, L, V), ee = [E, A]; + if (s != null && ee.push(s), L && ee.push(a), V) { + let oe = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32")); + ee.push(oe), R.push(oe); } - q = n.runWebGLProgram(X, Z, j); + K = n.runWebGLProgram(Z, ee, q); } - let H = te({ inputs: { x: q }, backend: n, attrs: { shape: w } }); - R.push(q); - for (let j of R) - n.disposeIntermediateTensorInfo(j); + let H = re({ inputs: { x: K }, backend: n, attrs: { shape: w } }); + R.push(K); + for (let q of R) + n.disposeIntermediateTensorInfo(q); return H; } -function G8(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return Pu({ a: n, b: s, transposeA: p, transposeB: u, backend: t6, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function cY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return zu({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var Y$ = { kernelName: fo, backendName: "webgl", kernelFunc: G8 }; -var Q$ = "return abs(x);"; -function H8(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - if (t6.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") { - let s = t6.texData.get(o.dataId), a = Kf(s.values); - return t6.makeTensorInfo(o.shape, o.dtype, a); +var rA = { kernelName: ho, backendName: "webgl", kernelFunc: cY }; +var oA = "return abs(x);"; +function lY(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; + if (t10.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") { + let s = t10.texData.get(o.dataId), a = qf(s.values); + return t10.makeTensorInfo(o.shape, o.dtype, a); } let n; - return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, Q$) : n = new Jt(o.shape, Q$), t6.runWebGLProgram(n, [o], o.dtype); + return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Er(o.shape, oA) : n = new Jt(o.shape, oA), t10.runWebGLProgram(n, [o], o.dtype); } -var Z$ = { kernelName: gs, backendName: "webgl", kernelFunc: H8 }; -var q8 = Bt + ` +var nA = { kernelName: ys, backendName: "webgl", kernelFunc: lY }; +var mY = zt + ` if (abs(x) > 1.) { return NAN; } return acos(x); `; -var K8 = ge({ opSnippet: q8 }); -var J$ = { kernelName: sa, backendName: "webgl", kernelFunc: K8 }; -var j8 = Bt + ` +var dY = he({ opSnippet: mY }); +var sA = { kernelName: aa, backendName: "webgl", kernelFunc: dY }; +var fY = zt + ` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`; -var X8 = ge({ opSnippet: j8 }); -var eA = { kernelName: aa, backendName: "webgl", kernelFunc: X8 }; -var tA = "return a + b;"; -var Y8 = tt({ opSnippet: tA, packedOpSnippet: tA, supportsComplex: true, cpuKernelImpl: DE }); -var rA = { kernelName: eo, backendName: "webgl", kernelFunc: Y8 }; +var hY = he({ opSnippet: fY }); +var aA = { kernelName: ia, backendName: "webgl", kernelFunc: hY }; +var iA = "return a + b;"; +var gY = tt({ opSnippet: iA, packedOpSnippet: iA, supportsComplex: true, cpuKernelImpl: V$ }); +var uA = { kernelName: eo, backendName: "webgl", kernelFunc: gY }; var rh = class { - constructor(e, t6) { - this.outputShape = [], this.outputShape = e, this.variableNames = t6.map((s, a) => `T${a}`); + constructor(e, t10) { + this.outputShape = [], this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`); let o = []; this.variableNames.forEach((s) => { o.push(`float v${s} = get${s}AtOutCoords();`); @@ -18746,8 +18742,8 @@ var rh = class { } }; var oh = class { - constructor(e, t6) { - this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t6.map((s, a) => `T${a}`); + constructor(e, t10) { + this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`); let o = []; this.variableNames.forEach((s) => { o.push(`vec4 v${s} = get${s}AtOutCoords();`); @@ -18765,47 +18761,47 @@ var oh = class { } }; function nh(r) { - let { inputs: e, backend: t6 } = r, o = e; + let { inputs: e, backend: t10 } = r, o = e; if (o.length === 1) - return At({ inputs: { x: o[0] }, backend: t6 }); + return At({ inputs: { x: o[0] }, backend: t10 }); if (o.length > O().get("WEBGL_MAX_TEXTURES_IN_SHADER")) { - let p = Math.floor(o.length / 2), u = nh({ inputs: o.slice(0, p), backend: t6 }), c = nh({ inputs: o.slice(p), backend: t6 }); - return nh({ inputs: [u, c], backend: t6 }); + let p = Math.floor(o.length / 2), u = nh({ inputs: o.slice(0, p), backend: t10 }), c = nh({ inputs: o.slice(p), backend: t10 }); + return nh({ inputs: [u, c], backend: t10 }); } let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = O().getBool("WEBGL_PACK") ? new oh(o[0].shape, s) : new rh(o[0].shape, s); - return t6.runWebGLProgram(i, o, n); + return t10.runWebGLProgram(i, o, n); } -var oA = { kernelName: Mo, backendName: "webgl", kernelFunc: nh }; -function Q8(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; - c != null && (l = xt({ inputs: { x: n }, backend: t6, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("all", u, i); - let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t6, attrs: { shape: [-1, f] } }), g = Gr(h, h.dtype, "all", t6), x; +var pA = { kernelName: Po, backendName: "webgl", kernelFunc: nh }; +function xY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; + c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("all", u, i); + let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "all", t10), x; if (a) { let b = S.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: b } }); + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: m } }); - return t6.disposeIntermediateTensorInfo(h), t6.disposeIntermediateTensorInfo(g), c != null && t6.disposeIntermediateTensorInfo(l), x; + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); + return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var nA = { kernelName: Lo, backendName: "webgl", kernelFunc: Q8 }; -function Z8(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; - c != null && (l = xt({ inputs: { x: n }, backend: t6, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("any", u, i); - let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t6, attrs: { shape: [-1, f] } }), g = Gr(h, h.dtype, "any", t6), x; +var cA = { kernelName: Mo, backendName: "webgl", kernelFunc: xY }; +function yY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; + c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("any", u, i); + let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "any", t10), x; if (a) { let b = S.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: b } }); + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: m } }); - return t6.disposeIntermediateTensorInfo(h), t6.disposeIntermediateTensorInfo(g), c != null && t6.disposeIntermediateTensorInfo(l), x; + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); + return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var sA = { kernelName: Bo, backendName: "webgl", kernelFunc: Z8 }; +var lA = { kernelName: Lo, backendName: "webgl", kernelFunc: yY }; var sh = class { - constructor(e, t6, o) { + constructor(e, t10, o) { this.variableNames = ["A"]; let { windowSize: n, batchSize: s, outSize: a } = e; o || this.variableNames.push("bestIndicesA"), this.outputShape = [s, a]; - let i = t6 === "max" ? ">" : "<", p = o ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; + let i = t10 === "max" ? ">" : "<", p = o ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -18830,14 +18826,14 @@ var sh = class { } }; var ah = class { - constructor(e, t6, o, n) { + constructor(e, t10, o, n) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(e.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`); - let s = e[e.length - 1], a = Math.ceil(s / t6); + let s = e[e.length - 1], a = Math.ceil(s / t10); this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), n || this.variableNames.push("bestIndicesA"); - let i = this.outputShape, p = i.length, u = _e(p), c = $t("coords", p), l, m; + let i = this.outputShape, p = i.length, u = $e(p), c = Et("coords", p), l, m; if (a === 1) { m = p + 1; - let A = _e(m); + let A = $e(m); l = ` ${A} sourceLocR = ${A}(${c.join()}, 0); ++${c[p - 1]}; @@ -18857,7 +18853,7 @@ var ah = class { --${c[p - 1]}; ${u} sourceLocB = coords; --${c[p - 2]};`; - let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((A) => "int " + A), g = $t("sourceLocR", m - 1).concat("inIdx.r"), x = $t("sourceLocG", m - 1).concat("inIdx.g"), b = $t("sourceLocB", m - 1).concat("inIdx.b"), C = $t("sourceLocA", m - 1).concat("inIdx.a"), w = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` + let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((A) => "int " + A), g = Et("sourceLocR", m - 1).concat("inIdx.r"), x = Et("sourceLocG", m - 1).concat("inIdx.g"), b = Et("sourceLocB", m - 1).concat("inIdx.b"), C = Et("sourceLocA", m - 1).concat("inIdx.a"), w = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), @@ -18865,7 +18861,7 @@ var ah = class { getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`, $ = n ? "" : ` + hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`, E = n ? "" : ` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); @@ -18875,19 +18871,19 @@ var ah = class { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } - ${$} + ${E} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[p - 1]} < ${i[p - 1] - 1}; bool hasNextRow = ${c[p - 2]} < ${i[p - 2] - 1}; ${l} ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f}, - sourceLocB${f}, sourceLocA${f}) * ${t6}; + sourceLocB${f}, sourceLocA${f}) * ${t10}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${_}; - for (int i = 0; i < ${t6}; i++) { + for (int i = 0; i < ${t10}; i++) { inIdx = srcIdx; ${k} vec4 candidate = ${_}; @@ -18907,94 +18903,94 @@ var ah = class { `; } }; -function aA(r, e, t6, o = null) { +function mA(r, e, t10, o = null) { let n = e.shape[0], s = e.shape[1]; o != null && (n = o.shape[0], s = o.shape[1]); - let a = S.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new sh(i, t6, o == null), u = [e]; + let a = S.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new sh(i, t10, o == null), u = [e]; o != null && u.push(o); let c = r.runWebGLProgram(p, u, "int32"); if (c.shape[1] === 1) return c; - let l = aA(r, e, t6, c); + let l = mA(r, e, t10, c); return r.disposeIntermediateTensorInfo(c), l; } -function iA(r, e, t6, o = null) { - let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = S.computeOptimalWindowSize(s), i = new ah(n, a, t6, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32"); +function dA(r, e, t10, o = null) { + let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = S.computeOptimalWindowSize(s), i = new ah(n, a, t10, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32"); if (u.shape.length === e.shape.length) { - let c = iA(r, e, t6, u); + let c = dA(r, e, t10, u); return r.disposeIntermediateTensorInfo(u), c; } return u; } -function ih(r, e, t6, o) { - let n = [t6]; +function ih(r, e, t10, o) { + let n = [t10]; if (S.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !O().getBool("WEBGL_PACK_REDUCE") || e.shape.length <= 2) { let s = [], a = r.texData.get(e.dataId), i = a !== null && a.isPacked, p = e; i && (p = r.unpackTensor(e), s.push(p)); - let [u, c] = S.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } }); + let [u, c] = S.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = re({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } }); s.push(m); - let d = aA(r, m, o); + let d = mA(r, m, o); s.push(d); - let f = te({ inputs: { x: d }, backend: r, attrs: { shape: u } }); + let f = re({ inputs: { x: d }, backend: r, attrs: { shape: u } }); return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), f; } - return iA(r, e, o); + return dA(r, e, o); } -function J8(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = xt({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = ih(t6, p, a[0], "max"); - return u.forEach((l) => t6.disposeIntermediateTensorInfo(l)), c; +function bY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = yt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = ih(t10, p, a[0], "max"); + return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c; } -var uA = { kernelName: Vo, backendName: "webgl", kernelFunc: J8 }; -function eY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = xt({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = ih(t6, p, a[0], "min"); - return u.forEach((l) => t6.disposeIntermediateTensorInfo(l)), c; +var fA = { kernelName: Bo, backendName: "webgl", kernelFunc: bY }; +function CY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = yt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = ih(t10, p, a[0], "min"); + return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c; } -var pA = { kernelName: Za, backendName: "webgl", kernelFunc: eY }; -var tY = Bt + ` +var hA = { kernelName: ei, backendName: "webgl", kernelFunc: CY }; +var SY = zt + ` if (abs(x) > 1.) { return NAN; } return asin(x); `; -var rY = ge({ opSnippet: tY }); -var cA = { kernelName: ia, backendName: "webgl", kernelFunc: rY }; -var oY = Bt + "return log(x + sqrt(x * x + 1.0));"; -var nY = ge({ opSnippet: oY }); -var lA = { kernelName: ua, backendName: "webgl", kernelFunc: nY }; -var sY = Bt + ` +var wY = he({ opSnippet: SY }); +var gA = { kernelName: ua, backendName: "webgl", kernelFunc: wY }; +var IY = zt + "return log(x + sqrt(x * x + 1.0));"; +var vY = he({ opSnippet: IY }); +var xA = { kernelName: pa, backendName: "webgl", kernelFunc: vY }; +var kY = zt + ` return atan(x); `; -var aY = ge({ opSnippet: sY }); -var mA = { kernelName: pa, backendName: "webgl", kernelFunc: aY }; -var iY = mc + ` +var NY = he({ opSnippet: kY }); +var yA = { kernelName: ca, backendName: "webgl", kernelFunc: NY }; +var TY = dc + ` return atan(a, b); `; -var uY = ` +var _Y = ` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Zs + ` + ` + Js + ` return result; `; -var pY = tt({ opSnippet: iY, packedOpSnippet: uY }); -var dA = { kernelName: la, backendName: "webgl", kernelFunc: pY }; -var cY = Bt + ` +var $Y = tt({ opSnippet: TY, packedOpSnippet: _Y }); +var bA = { kernelName: ma, backendName: "webgl", kernelFunc: $Y }; +var EY = zt + ` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`; -var lY = ge({ opSnippet: cY }); -var fA = { kernelName: ca, backendName: "webgl", kernelFunc: lY }; -var ps = class { - constructor(e, t6, o, n = false, s = false) { - if (this.variableNames = ["x"], t6 === "avg" && o) +var AY = he({ opSnippet: EY }); +var CA = { kernelName: la, backendName: "webgl", kernelFunc: AY }; +var cs = class { + constructor(e, t10, o, n = false, s = false) { + if (this.variableNames = ["x"], t10 === "avg" && o) throw new Error("Cannot compute positions for average pool."); let a = e.filterWidth, i = e.strideHeight, p = e.strideWidth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterHeight, m = e.effectiveFilterWidth, d = e.padInfo.top, f = e.padInfo.left; this.outputShape = e.outShape; - let h = t6 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0"; + let h = t10 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0"; if (h || (b = "-1.0 / 1e-20"), o) { let A = ">="; this.userCode = ` @@ -19051,9 +19047,9 @@ var ps = class { `; return; } - let C = "max", w = `${t6}(${t6}(${t6}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t6 === "avg" && (w = "avgValue / count"); - let k = Math.floor(a / 4) * 4, _ = a % 4, $ = ` + let C = "max", w = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t10 === "avg" && (w = "avgValue / max(count, 1.0)"); + let k = Math.floor(a / 4) * 4, _ = a % 4, E = ` if (${h}) { avgValue += dot(values, ones); } else { @@ -19109,7 +19105,7 @@ var ps = class { getValue(batch, xR, xC + 3 * ${c}, d) ); - ${$} + ${E} } int xC = xCCorner + ${k}; @@ -19121,7 +19117,7 @@ var ps = class { initializationValue ); - ${$} + ${E} } else if (${_ === 2}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -19130,7 +19126,7 @@ var ps = class { initializationValue ); - ${$} + ${E} } else if (${_ === 3}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -19139,7 +19135,7 @@ var ps = class { initializationValue ); - ${$} + ${E} } } setOutput(${w}); @@ -19147,13 +19143,13 @@ var ps = class { `; } }; -var zi = class { - constructor(e, t6, o, n = false, s = false) { - if (this.variableNames = ["x"], t6 === "avg" && o) +var Wi = class { + constructor(e, t10, o, n = false, s = false) { + if (this.variableNames = ["x"], t10 === "avg" && o) throw new Error("Cannot compute positions for average pool."); let a = e.filterWidth, i = e.strideDepth, p = e.strideHeight, u = e.strideWidth, c = e.dilationDepth, l = e.dilationHeight, m = e.dilationWidth, d = e.effectiveFilterDepth, f = e.effectiveFilterHeight, h = e.effectiveFilterWidth, g = e.padInfo.front, x = e.padInfo.top, b = e.padInfo.left; this.outputShape = e.outShape; - let C = t6 === "avg", w = "0.0"; + let C = t10 === "avg", w = "0.0"; if (C || (w = "-1.0 / 1e-20"), o) { let D = ">="; this.userCode = ` @@ -19221,9 +19217,9 @@ var zi = class { `; return; } - let k = "max", _ = `${t6}(${t6}(${t6}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; - t6 === "avg" && (_ = "avgValue / count"); - let $ = Math.floor(a / 4) * 4, A = a % 4, R = ` + let k = "max", _ = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + t10 === "avg" && (_ = "avgValue / max(count, 1.0)"); + let E = Math.floor(a / 4) * 4, A = a % 4, R = ` if (${C}) { avgValue += dot(values, ones); } else { @@ -19279,7 +19275,7 @@ var zi = class { continue; } - for (int wC = 0; wC < ${$}; wC += 4) { + for (int wC = 0; wC < ${E}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( @@ -19292,7 +19288,7 @@ var zi = class { ${R} } - int xC = xCCorner + ${$}; + int xC = xCCorner + ${E}; if (${A === 1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -19322,33 +19318,33 @@ var zi = class { ${R} } } - setOutput(${_}); } + setOutput(${_}); } `; } }; -function mY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e; - is(n, "avgPool"); +function RY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; + us(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = S.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return At({ inputs: { x: n }, backend: t6 }); - let l = new ps(c, "avg", false); - return t6.runWebGLProgram(l, [n], "float32"); + return At({ inputs: { x: n }, backend: t10 }); + let l = new cs(c, "avg", false); + return t10.runWebGLProgram(l, [n], "float32"); } -var hA = { kernelName: zo, backendName: "webgl", kernelFunc: mY }; -function dY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new zi(l, "avg", false); - return t6.runWebGLProgram(m, [n], "float32"); +var SA = { kernelName: Vo, backendName: "webgl", kernelFunc: RY }; +function DY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new Wi(l, "avg", false); + return t10.runWebGLProgram(m, [n], "float32"); } -var gA = { kernelName: ip, backendName: "webgl", kernelFunc: dY }; +var wA = { kernelName: dp, backendName: "webgl", kernelFunc: DY }; var uh = class { constructor(e) { this.variableNames = ["dy"], this.outputShape = e.inShape; - let t6 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t6 * o); + let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t10 * o); this.userCode = ` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); @@ -19397,7 +19393,7 @@ var uh = class { var ph = class { constructor(e) { this.variableNames = ["dy"], this.outputShape = e.inShape; - let t6 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t6 * o * n); + let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t10 * o * n); this.userCode = ` const ivec3 pads = ivec3(${f}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -19457,26 +19453,26 @@ var ph = class { `; } }; -function fY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new ph(m); - return t6.runWebGLProgram(d, [n], a.dtype); +function FY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new ph(m); + return t10.runWebGLProgram(d, [n], a.dtype); } -var xA = { kernelName: Im, backendName: "webgl", kernelFunc: fY }; -function hY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, a = s; - is([n, s], "avgPoolGrad"); +var IA = { kernelName: vm, backendName: "webgl", kernelFunc: FY }; +function OY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; + us([n, s], "avgPoolGrad"); let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = new uh(c); - return t6.runWebGLProgram(l, [n], a.dtype); + return t10.runWebGLProgram(l, [n], a.dtype); } -var yA = { kernelName: wm, backendName: "webgl", kernelFunc: hY }; -function gY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - return Pu({ a: n, b: s, transposeA: a, transposeB: i, backend: t6 }); +var vA = { kernelName: mp, backendName: "webgl", kernelFunc: OY }; +function PY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + return zu({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 }); } -var bA = { kernelName: Wo, backendName: "webgl", kernelFunc: gY }; +var kA = { kernelName: zo, backendName: "webgl", kernelFunc: PY }; var ch = class { - constructor(e, t6, o, n, s, a) { - this.outputShape = [], this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t6), S.assertAndGetBroadcastShape(e, o); + constructor(e, t10, o, n, s, a) { + this.outputShape = [], this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o); let i = "0.0"; n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); let p = "1.0"; @@ -19494,8 +19490,8 @@ var ch = class { } }; var lh = class { - constructor(e, t6, o, n, s, a) { - this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t6), S.assertAndGetBroadcastShape(e, o); + constructor(e, t10, o, n, s, a) { + this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o); let i = "vec4(0.0)"; n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()"); let p = "vec4(1.0)"; @@ -19515,10 +19511,10 @@ var lh = class { `; } }; -var xY = ({ inputs: r, backend: e, attrs: t6 }) => { +var MY = ({ inputs: r, backend: e, attrs: t10 }) => { let { x: o, mean: n, variance: s, offset: a, scale: i } = r; y.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(i == null || n.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let { varianceEpsilon: p } = t6; + let { varianceEpsilon: p } = t10; p == null && (p = 1e-3); let u = [o, n, s], c = null; a != null && (c = a.shape, u.push(a)); @@ -19527,16 +19523,16 @@ var xY = ({ inputs: r, backend: e, attrs: t6 }) => { let m = O().getBool("WEBGL_PACK_NORMALIZATION") ? new lh(o.shape, n.shape, s.shape, c, l, p) : new ch(o.shape, n.shape, s.shape, c, l, p); return e.runWebGLProgram(m, u, u[0].dtype); }; -var CA = { kernelName: an, backendName: "webgl", kernelFunc: xY }; +var NA = { kernelName: sn, backendName: "webgl", kernelFunc: MY }; var mh = class { constructor(e) { this.variableNames = ["source"], this.outputShape = e, this.rank = e.length; - let t6 = _e(this.rank); + let t10 = $e(this.rank); this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let o = yY(this.rank), n, s = e.map((a, i) => `sourceLoc.${Tw[i]} = start[${i}] + coords.${Tw[i]};`); + let o = LY(this.rank), n, s = e.map((a, i) => `sourceLoc.${Lw[i]} = start[${i}] + coords.${Lw[i]};`); n = ` - ${t6} sourceLoc; - ${t6} coords = getOutputCoords(); + ${t10} sourceLoc; + ${t10} coords = getOutputCoords(); ${s.join(` `)} `, this.userCode = ` @@ -19547,18 +19543,18 @@ var mh = class { `; } }; -var Tw = ["x", "y", "z", "w", "u", "v"]; -function yY(r) { +var Lw = ["x", "y", "z", "w", "u", "v"]; +function LY(r) { if (r === 1) return "sourceLoc"; if (r <= 6) - return Tw.slice(0, r).map((e) => "sourceLoc." + e).join(","); + return Lw.slice(0, r).map((e) => "sourceLoc." + e).join(","); throw Error(`Slicing for rank ${r} is not yet supported`); } var dh = class { constructor(e) { this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let t6 = _e(this.rank), o = $t("coords", this.rank), n = $t("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` + let t10 = $e(this.rank), o = Et("coords", this.rank), n = Et("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` result.x = ${a}; if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) { ++${n[this.rank - 1]}; @@ -19576,12 +19572,12 @@ var dh = class { } } `, u = this.rank <= 4 ? `sourceLoc = coords + - ${t6}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(` + ${t10}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(` `); this.userCode = ` void main() { - ${t6} coords = getOutputCoords(); - ${t6} sourceLoc; + ${t10} coords = getOutputCoords(); + ${t10} sourceLoc; ${u} vec4 result = vec4(0.); ${i} @@ -19591,92 +19587,92 @@ var dh = class { `; } }; -function bY(r, e, t6, o) { - let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t6, r.dtype), a = o.texData.get(s.dataId); - Object.assign(a, n), a.refCount = 1, a.shape = t6, a.dtype = r.dtype; +function BY(r, e, t10, o) { + let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t10, r.dtype), a = o.texData.get(s.dataId); + Object.assign(a, n), a.refCount = 1, a.shape = t10, a.dtype = r.dtype; let i = ut.computeFlatOffset(e, y.computeStrides(r.shape)); n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId }; let p = o.dataRefCount.get(a.slice.origDataId) || 1; return o.dataRefCount.set(a.slice.origDataId, p + 1), s; } -function cs(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a); +function ls(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a); if (ut.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0) - return t6.makeTensorInfo(p, n.dtype, []); - if (t6.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = t6.texData.get(n.dataId), m = l$(l.values, i, p, n.shape, n.dtype); - return t6.makeTensorInfo(p, n.dtype, m); + return t10.makeTensorInfo(p, n.dtype, []); + if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string") { + let l = t10.texData.get(n.dataId), m = xE(l.values, i, p, n.shape, n.dtype); + return t10.makeTensorInfo(p, n.dtype, m); } - let { isPacked: u } = t6.texData.get(n.dataId), c = ut.isSliceContinous(n.shape, i, p); + let { isPacked: u } = t10.texData.get(n.dataId), c = ut.isSliceContinous(n.shape, i, p); if (u || !c) { let l = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new dh(p) : new mh(p), m = [i]; - return t6.runWebGLProgram(l, [n], n.dtype, m); + return t10.runWebGLProgram(l, [n], n.dtype, m); } - return t6.uploadToGPU(n.dataId), bY(n, i, p, t6); + return t10.uploadToGPU(n.dataId), BY(n, i, p, t10); } -var SA = { kernelName: _s, backendName: "webgl", kernelFunc: cs }; -var CY = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; +var TA = { kernelName: Es, backendName: "webgl", kernelFunc: ls }; +var VY = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); - let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: t6, attrs: { shape: p } }), h = xt({ inputs: { x: f }, backend: t6, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: t6, attrs: { shape: c } }), x = cs({ inputs: { x: g }, backend: t6, attrs: { begin: l, size: m } }); - return d.push(f), d.push(h), d.push(g), d.forEach((b) => t6.disposeIntermediateTensorInfo(b)), x; + let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = re({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = yt({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = ls({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } }); + return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x; }; -var wA = { kernelName: xs, backendName: "webgl", kernelFunc: CY }; -function SY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t6.readSync(n.dataId), p = t6.readSync(s.dataId), u = qf(i, p, s.dtype, s.shape, a); - return t6.makeTensorInfo([a], s.dtype, u); +var _A = { kernelName: bs, backendName: "webgl", kernelFunc: VY }; +function zY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), u = Kf(i, p, s.dtype, s.shape, a); + return t10.makeTensorInfo([a], s.dtype, u); } -var IA = { kernelName: Ja, backendName: "webgl", kernelFunc: SY }; -function wY(r) { - let { inputs: e, backend: t6 } = r, { s0: o, s1: n } = e, s = t6.readSync(o.dataId), a = t6.readSync(n.dataId), i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); - return t6.makeTensorInfo([i.length], "int32", Int32Array.from(i)); +var $A = { kernelName: ti, backendName: "webgl", kernelFunc: zY }; +function WY(r) { + let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.readSync(o.dataId), a = t10.readSync(n.dataId), i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); + return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var vA = { kernelName: up, backendName: "webgl", kernelFunc: wY }; -var IY = "return float(a != b);"; -var _w = tt({ opSnippet: IY, cpuKernelImpl: r$, dtype: "bool" }); -var kA = { kernelName: Nn, backendName: "webgl", kernelFunc: _w }; -function Ua(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.texData.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.real }, backend: t6 }); +var EA = { kernelName: fp, backendName: "webgl", kernelFunc: WY }; +var UY = "return float(a != b);"; +var Bw = tt({ opSnippet: UY, cpuKernelImpl: uE, dtype: "bool" }); +var AA = { kernelName: Nn, backendName: "webgl", kernelFunc: Bw }; +function Ka(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.real }, backend: t10 }); } -var NA = { kernelName: ai, backendName: "webgl", kernelFunc: Ua }; -var vY = "return float(int(x));"; -function TA(r, e) { - let t6 = new Jt(r.shape, vY), o = e.runWebGLProgram(t6, [r], "int32"); +var RA = { kernelName: di, backendName: "webgl", kernelFunc: Ka }; +var GY = "return float(int(x));"; +function DA(r, e) { + let t10 = new Jt(r.shape, GY), o = e.runWebGLProgram(t10, [r], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function Ew(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function Vw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return At({ inputs: { x: n }, backend: t6 }); - let a = Vr(n.shape), i = Ew({ inputs: { x: n }, backend: t6, attrs: { dtype: "float32" } }), p = Rr({ inputs: { real: i, imag: a }, backend: t6 }); - return a.dispose(), t6.disposeIntermediateTensorInfo(i), p; + return At({ inputs: { x: n }, backend: t10 }); + let a = Br(n.shape), i = Vw({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = Ar({ inputs: { real: i, imag: a }, backend: t10 }); + return a.dispose(), t10.disposeIntermediateTensorInfo(i), p; } if (n.dtype === "complex64") { - let a = Ua({ inputs: { input: n }, backend: t6 }), i = Ew({ inputs: { x: a }, backend: t6, attrs: { dtype: s } }); - return t6.disposeIntermediateTensorInfo(a), i; + let a = Ka({ inputs: { input: n }, backend: t10 }), i = Vw({ inputs: { x: a }, backend: t10, attrs: { dtype: s } }); + return t10.disposeIntermediateTensorInfo(a), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = At({ inputs: { x: n }, backend: t6 }); + let a = At({ inputs: { x: n }, backend: t10 }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } - if (t6.shouldExecuteOnCPU([n])) { - let a = t6.texData.get(n.dataId).values, [i, p, u] = PE(a, n.shape, n.dtype, s); - return t6.makeTensorInfo(i, p, u); + if (t10.shouldExecuteOnCPU([n])) { + let a = t10.texData.get(n.dataId).values, [i, p, u] = W$(a, n.shape, n.dtype, s); + return t10.makeTensorInfo(i, p, u); } if (s === "int32") - return TA(n, t6); + return DA(n, t10); if (s === "bool") { - let a = t6.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = _w({ inputs: { a: n, b: a }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(a), p; + let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = Bw({ inputs: { a: n, b: a }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(a), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var _A = { kernelName: co, backendName: "webgl", kernelFunc: Ew }; -var EA = "return ceil(x);"; -var kY = ge({ opSnippet: EA, packedOpSnippet: EA, cpuKernelImpl: ME }); -var $A = { kernelName: Uo, backendName: "webgl", kernelFunc: kY }; +var FA = { kernelName: co, backendName: "webgl", kernelFunc: Vw }; +var OA = "return ceil(x);"; +var HY = he({ opSnippet: OA, packedOpSnippet: OA, cpuKernelImpl: U$ }); +var PA = { kernelName: Wo, backendName: "webgl", kernelFunc: HY }; var fh = class { constructor(e) { this.variableNames = ["A"], this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = ` @@ -19709,13 +19705,13 @@ var hh = class { `; } }; -function NY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i; +function KY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i; O().getBool("WEBGL_PACK_CLIP") ? i = new hh(n.shape) : i = new fh(n.shape); let p = [[s], [a]]; - return t6.runWebGLProgram(i, [n], n.dtype, p); + return t10.runWebGLProgram(i, [n], n.dtype, p); } -var AA = { kernelName: lo, backendName: "webgl", kernelFunc: NY }; +var MA = { kernelName: lo, backendName: "webgl", kernelFunc: KY }; var gh = class { constructor(e) { this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = ` @@ -19734,27 +19730,27 @@ var gh = class { `; } }; -function RA(r, e) { +function LA(r, e) { return { dataId: e.dataId, dtype: e.dtype, shape: r.shape }; } -function TY(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e, n = t6.texData.get(o.dataId), s = new gh(o.shape), a = [RA(o, n.complexTensorInfos.real), RA(o, n.complexTensorInfos.imag)]; - return t6.runWebGLProgram(s, a, a[0].dtype); +function qY(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.texData.get(o.dataId), s = new gh(o.shape), a = [LA(o, n.complexTensorInfos.real), LA(o, n.complexTensorInfos.imag)]; + return t10.runWebGLProgram(s, a, a[0].dtype); } -var FA = { kernelName: pp, backendName: "webgl", kernelFunc: TY }; +var BA = { kernelName: hp, backendName: "webgl", kernelFunc: qY }; var xh = class { constructor(e) { this.outputShape = [], this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`); - let t6 = new Array(e.length - 1); - t6[0] = e[0][1]; - for (let a = 1; a < t6.length; a++) - t6[a] = t6[a - 1] + e[a][1]; - let o = [`if (yC < ${t6[0]}) setOutput(getT0(yR, yC));`]; - for (let a = 1; a < t6.length; a++) { - let i = t6[a - 1]; - o.push(`else if (yC < ${t6[a]}) setOutput(getT${a}(yR, yC-${i}));`); + let t10 = new Array(e.length - 1); + t10[0] = e[0][1]; + for (let a = 1; a < t10.length; a++) + t10[a] = t10[a - 1] + e[a][1]; + let o = [`if (yC < ${t10[0]}) setOutput(getT0(yR, yC));`]; + for (let a = 1; a < t10.length; a++) { + let i = t10[a - 1]; + o.push(`else if (yC < ${t10[a]}) setOutput(getT${a}(yR, yC-${i}));`); } - let n = t6.length, s = t6[t6.length - 1]; + let n = t10.length, s = t10[t10.length - 1]; o.push(`else setOutput(getT${n}(yR, yC-${s}));`), this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -19768,15 +19764,15 @@ var xh = class { } }; var bh = class { - constructor(e, t6) { - this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = S.computeOutShape(e, t6); - let o = this.outputShape, n = o.length, s = _e(n), a = $t("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n); + constructor(e, t10) { + this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = S.computeOutShape(e, t10); + let o = this.outputShape, n = o.length, s = $e(n), a = Et("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n); this.variableNames = e.map((h, g) => `T${g}`); let p = new Array(e.length - 1); - p[0] = e[0][t6]; + p[0] = e[0][t10]; for (let h = 1; h < p.length; h++) - p[h] = p[h - 1] + e[h][t6]; - let u = i[t6], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) { + p[h] = p[h - 1] + e[h][t10]; + let u = i[t10], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) { return getChannel( getT0(${l}), vec2(${c.join()})); }`; @@ -19822,71 +19818,71 @@ var bh = class { `; } }; -function yh(r, e, t6) { +function yh(r, e, t10) { let o = r.indexOf(e); - return r.map((s, a) => a === o ? `${s} - ${t6}` : s).join(); + return r.map((s, a) => a === o ? `${s} - ${t10}` : s).join(); } -function Mu(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.texData.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t6 }); +function Wu(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 }); } -var DA = { kernelName: si, backendName: "webgl", kernelFunc: Mu }; -function fc(r, e, t6) { +var VA = { kernelName: ci, backendName: "webgl", kernelFunc: Wu }; +function hc(r, e, t10) { let o = r[0].dtype; if (o === "complex64") { - let d = r.map((b) => Ua({ inputs: { input: b }, backend: t6 })), f = r.map((b) => Mu({ inputs: { input: b }, backend: t6 })), h = fc(d, e, t6), g = fc(f, e, t6), x = Rr({ inputs: { real: h, imag: g }, backend: t6 }); - return d.forEach((b) => t6.disposeIntermediateTensorInfo(b)), f.forEach((b) => t6.disposeIntermediateTensorInfo(b)), t6.disposeIntermediateTensorInfo(h), t6.disposeIntermediateTensorInfo(g), x; + let d = r.map((b) => Ka({ inputs: { input: b }, backend: t10 })), f = r.map((b) => Wu({ inputs: { input: b }, backend: t10 })), h = hc(d, e, t10), g = hc(f, e, t10), x = Ar({ inputs: { real: h, imag: g }, backend: t10 }); + return d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), f.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), x; } - let n = t6.shouldExecuteOnCPU(r); + let n = t10.shouldExecuteOnCPU(r); if (o === "string" && (n = true), n) { let d = r.map((w) => { let _ = [-1, y.sizeFromShape(w.shape.slice(e))]; - return te({ inputs: { x: w }, backend: t6, attrs: { shape: _ } }); - }), f = d.map((w) => ({ vals: t6.readSync(w.dataId), shape: w.shape })), h = S.computeOutShape(d.map((w) => w.shape), 1), g = d[0].shape[0] === 1, x = LE(f, h, o, g), b = S.computeOutShape(r.map((w) => w.shape), e), C = t6.makeTensorInfo(b, o, x); - return d.forEach((w) => t6.disposeIntermediateTensorInfo(w)), C; + return re({ inputs: { x: w }, backend: t10, attrs: { shape: _ } }); + }), f = d.map((w) => ({ vals: t10.readSync(w.dataId), shape: w.shape })), h = S.computeOutShape(d.map((w) => w.shape), 1), g = d[0].shape[0] === 1, x = G$(f, h, o, g), b = S.computeOutShape(r.map((w) => w.shape), e), C = t10.makeTensorInfo(b, o, x); + return d.forEach((w) => t10.disposeIntermediateTensorInfo(w)), C; } let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1; if (s.length === 1) { - let d = a ? new Jt(r[0].shape, Qs) : new Ar(r[0].shape, Qs); - return t6.runWebGLProgram(d, r, o); + let d = a ? new Jt(r[0].shape, Zs) : new Er(r[0].shape, Zs); + return t10.runWebGLProgram(d, r, o); } let i = O().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); if (s.length > i) { let d = []; for (let h = 0; h < s.length; h += i) { let g = s.slice(h, h + i); - d.push(fc(g, e, t6)); + d.push(hc(g, e, t10)); } - let f = fc(d, e, t6); + let f = hc(d, e, t10); for (let h of d) - t6.disposeIntermediateTensorInfo(h); + t10.disposeIntermediateTensorInfo(h); return f; } if (a) { let d = new bh(s.map((f) => f.shape), e); - return t6.runWebGLProgram(d, s, o); + return t10.runWebGLProgram(d, s, o); } - let { tensors2D: p, outShape: u } = _Y(s, e, t6), c = new xh(p.map((d) => d.shape)), l = t6.runWebGLProgram(c, p, o); - p.forEach((d) => t6.disposeIntermediateTensorInfo(d)); - let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(l), m; + let { tensors2D: p, outShape: u } = jY(s, e, t10), c = new xh(p.map((d) => d.shape)), l = t10.runWebGLProgram(c, p, o); + p.forEach((d) => t10.disposeIntermediateTensorInfo(d)); + let m = re({ inputs: { x: l }, attrs: { shape: u }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(l), m; } -function _Y(r, e, t6) { +function jY(r, e, t10) { let o = S.computeOutShape(r.map((s) => s.shape), e); - return { tensors2D: r.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t6 })), outShape: o }; + return { tensors2D: r.map((s) => re({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t10 })), outShape: o }; } -function $w(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); +function zw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); S.assertParamsConsistent(a, s); let i = S.computeOutShape(e.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) - return t6.makeTensorInfo(i, e[0].dtype, []); + return t10.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t6 }) : fc(p, s, t6); + return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t10 }) : hc(p, s, t10); } -var OA = { kernelName: ys, backendName: "webgl", kernelFunc: $w }; -var hc = class { - constructor(e, t6 = false, o = null, n = false, s = false) { +var zA = { kernelName: Cs, backendName: "webgl", kernelFunc: zw }; +var gc = class { + constructor(e, t10 = false, o = null, n = false, s = false) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; let a = e.padInfo.top, i = e.padInfo.left, p = e.strideHeight, u = e.strideWidth, c = e.dilationHeight, l = e.dilationWidth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, C = g ? 3 : 1, w = "", k = ""; o && (n ? w = `float activation(float a) { @@ -19900,8 +19896,8 @@ var hc = class { ${o} } `, k = "result = activation(result);"); - let _ = t6 ? "result += getBiasAtOutCoords();" : ""; - t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let _ = t10 ? "result += getBiasAtOutCoords();" : ""; + t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${w} const ivec2 strides = ivec2(${p}, ${u}); @@ -20031,10 +20027,10 @@ var hc = class { var Ch = class { constructor(e) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; - let t6 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4; + let t10 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4; this.userCode = ` const ivec3 strides = ivec3(${s}, ${a}, ${i}); - const ivec3 pads = ivec3(${t6}, ${o}, ${n}); + const ivec3 pads = ivec3(${t10}, ${o}, ${n}); void main() { ivec5 coords = getOutputCoords(); @@ -20123,9 +20119,9 @@ var Ch = class { `; } }; -var gc = class { - constructor(e, t6 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ct(this.outputShape.length); +var xc = class { + constructor(e, t10 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length); let a = e.padInfo.left, i = e.strideWidth, p = e.dilationWidth, u = e.filterHeight, c = e.filterWidth, l = c, m = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -20322,8 +20318,8 @@ var gc = class { }` : d = `vec4 activation(vec4 x) { ${o} }`, f = "result = activation(result);"); - let h = t6 ? "result += getBiasAtOutCoords();" : ""; - t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let h = t10 ? "result += getBiasAtOutCoords();" : ""; + t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${d} void main() { @@ -20348,9 +20344,9 @@ var gc = class { } }; var Sh = class { - constructor(e, t6) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = ct(this.outputShape.length); - let { dataFormat: o } = t6, n = St(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = ""; + constructor(e, t10) { + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length); + let { dataFormat: o } = t10, n = wt(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = ""; for (let c = 0; c <= 1; c++) for (let l = 0; l <= 1; l++) u += ` @@ -20405,80 +20401,80 @@ var Sh = class { } }; function wh(r, e) { - let t6 = r.length; - return t6 >= 3 ? e ? [...r.slice(0, -3), r[t6 - 3] * r[t6 - 2], r[t6 - 1]] : [...r.slice(0, -3), r[t6 - 3], r[t6 - 2] * r[t6 - 1]] : !e && t6 === 1 && r[0] > 1 ? [r[0], 1] : null; + let t10 = r.length; + return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null; } -function Ih({ x: r, filter: e, convInfo: t6, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = r.shape, u = o.texData.get(r.dataId), c = t6.inChannels, l = p[0] * p[1] * p[2], m = t6.outChannels, d = t6.dataFormat === "channelsLast", f = false, h = false, g, x = []; +function Ih({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = r.shape, u = o.texData.get(r.dataId), c = t10.inChannels, l = p[0] * p[1] * p[2], m = t10.outChannels, d = t10.dataFormat === "channelsLast", f = false, h = false, g, x = []; if (s != null) { let w = wh(s.shape, d); - w != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: w } }), x.push(s)); + w != null && (s = re({ inputs: { x: s }, backend: o, attrs: { shape: w } }), x.push(s)); } if (n != null) { let w = wh(n.shape, d); - w != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: w } }), x.push(n)); + w != null && (n = re({ inputs: { x: n }, backend: o, attrs: { shape: w } }), x.push(n)); } - if (!((l === 1 || m === 1) && c > Nw) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { - let w = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, w, t6.inChannels], dtype: r.dtype }, _ = u.shape; - u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(Li(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); - let $ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t6.inChannels, t6.outChannels] } }); - x.push($); - let A = Pu({ a: k, b: $, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), R = o.texData.get(A.dataId); - y.assert(R.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, R.shape = t6.outShape, g = At({ inputs: { x: A }, backend: o }), g.shape = t6.outShape, x.push(A); + if (!((l === 1 || m === 1) && c > Mw) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { + let w = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, w, t10.inChannels], dtype: r.dtype }, _ = u.shape; + u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(Bi(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); + let E = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }); + x.push(E); + let A = zu({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), R = o.texData.get(A.dataId); + y.assert(R.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, R.shape = t10.outShape, g = At({ inputs: { x: A }, backend: o }), g.shape = t10.outShape, x.push(A); } else { - let w = t6.outHeight * t6.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t6.batchSize, w, t6.inChannels] : [t6.batchSize, t6.inChannels, w] } }), _ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t6.inChannels, t6.outChannels] } }), $ = Pu({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); - g = te({ inputs: { x: $ }, backend: o, attrs: { shape: t6.outShape } }), x.push(k), x.push(_), x.push($); + let w = t10.outHeight * t10.outWidth, k = re({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t10.batchSize, w, t10.inChannels] : [t10.batchSize, t10.inChannels, w] } }), _ = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }), E = zu({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + g = re({ inputs: { x: E }, backend: o, attrs: { shape: t10.outShape } }), x.push(k), x.push(_), x.push(E); } for (let w of x) o.disposeIntermediateTensorInfo(w); return g; } -function vh({ x: r, filter: e, convInfo: t6, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t6, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t6.batchSize, h, g], b = true, C = false, w = []; +function vh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t10, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t10.batchSize, h, g], b = true, C = false, w = []; if (s != null) { let H = wh(s.shape, f); - H != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: H } }), w.push(s)); + H != null && (s = re({ inputs: { x: s }, backend: o, attrs: { shape: H } }), w.push(s)); } if (n != null) { let H = wh(n.shape, f); - H != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: H } }), w.push(n)); + H != null && (n = re({ inputs: { x: n }, backend: o, attrs: { shape: H } }), w.push(n)); } - let k = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(e.shape) / h] } }); + let k = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(e.shape) / h] } }); w.push(k); - let _ = new Sh(x, t6), $ = [r.shape, [t6.padInfo.top, t6.padInfo.left], [t6.strideHeight, t6.strideWidth], [t6.dilationHeight, t6.dilationWidth], [t6.inChannels], [t6.filterWidth * t6.inChannels], [t6.outWidth]], A = o.runWebGLProgram(_, [r], "float32", $), R = te({ inputs: { x: A }, backend: o, attrs: { shape: x } }); + let _ = new Sh(x, t10), E = [r.shape, [t10.padInfo.top, t10.padInfo.left], [t10.strideHeight, t10.strideWidth], [t10.dilationHeight, t10.dilationWidth], [t10.inChannels], [t10.filterWidth * t10.inChannels], [t10.outWidth]], A = o.runWebGLProgram(_, [r], "float32", E), R = re({ inputs: { x: A }, backend: o, attrs: { shape: x } }); w.push(A), w.push(R); - let D = n != null, P = s != null, M = i === "leakyrelu", L = i ? Wa(i, true) : null, W = new dc(f ? R.shape : k.shape, f ? k.shape : R.shape, f ? [t6.batchSize, g, t6.outChannels] : [t6.batchSize, t6.outChannels, g], b, C, D, L, P, M), V = f ? [R, k] : [k, R]; - if (n && V.push(n), P && V.push(s), M) { + let D = n != null, P = s != null, M = i === "leakyrelu", L = i ? Ha(i, true) : null, V = new fc(f ? R.shape : k.shape, f ? k.shape : R.shape, f ? [t10.batchSize, g, t10.outChannels] : [t10.batchSize, t10.outChannels, g], b, C, D, L, P, M), z = f ? [R, k] : [k, R]; + if (n && z.push(n), P && z.push(s), M) { let H = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32")); - V.push(H), w.push(H); + z.push(H), w.push(H); } - let U = o.runWebGLProgram(W, V, "float32"), q = te({ inputs: { x: U }, backend: o, attrs: { shape: t6.outShape } }); + let U = o.runWebGLProgram(V, z, "float32"), K = re({ inputs: { x: U }, backend: o, attrs: { shape: t10.outShape } }); w.push(U); for (let H of w) o.disposeIntermediateTensorInfo(H); - return q; + return K; } -function EY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; +function XY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; if (m.filterHeight === 1 && m.filterWidth === 1 && m.dilationHeight === 1 && m.dilationWidth === 1 && m.strideHeight === 1 && m.strideWidth === 1 && (m.padInfo.type === "SAME" || m.padInfo.type === "VALID")) - d = Ih({ x: n, filter: s, convInfo: m, backend: t6 }); + d = Ih({ x: n, filter: s, convInfo: m, backend: t10 }); else if (m.strideWidth <= 2 && l === "channelsLast" && O().getBool("WEBGL_EXP_CONV")) { - let h = new gc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; - d = t6.runWebGLProgram(h, [n, s], "float32", g); + let h = new xc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; + d = t10.runWebGLProgram(h, [n, s], "float32", g); } else if (O().getBool("WEBGL_CONV_IM2COL")) - d = vh({ x: n, filter: s, convInfo: m, backend: t6 }); + d = vh({ x: n, filter: s, convInfo: m, backend: t10 }); else { - let h = new hc(m); - d = t6.runWebGLProgram(h, [n, s], "float32"); + let h = new gc(m); + d = t10.runWebGLProgram(h, [n, s], "float32"); } - let f = te({ inputs: { x: d }, backend: t6, attrs: { shape: m.outShape } }); - return t6.disposeIntermediateTensorInfo(d), f; + let f = re({ inputs: { x: d }, backend: t10, attrs: { shape: m.outShape } }); + return t10.disposeIntermediateTensorInfo(d), f; } -var PA = { kernelName: Go, backendName: "webgl", kernelFunc: EY }; +var WA = { kernelName: Uo, backendName: "webgl", kernelFunc: XY }; var kh = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t6 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.dataFormat === "channelsLast"; + let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.dataFormat === "channelsLast"; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -20493,7 +20489,7 @@ var kh = class { for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t6} - ${n}; + int xR = wR + yR * ${t10} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; @@ -20527,7 +20523,7 @@ var kh = class { var Nh = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t6 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t6 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1; + let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t10 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1; this.userCode = ` const ivec2 pads = ivec2(${i}, ${p}); @@ -20543,7 +20539,7 @@ var Nh = class { // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${t6}; wR++) { + for (int wR = 0; wR < ${t10}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -20551,7 +20547,7 @@ var Nh = class { } int idyR = int(dyR); - int wRPerm = ${t6} - 1 - wR; + int wRPerm = ${t10} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; @@ -20587,7 +20583,7 @@ var Nh = class { var Th = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t6 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left; + let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left; this.userCode = ` void main() { ivec5 coords = getOutputCoords(); @@ -20601,7 +20597,7 @@ var Th = class { for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { - int xF = wF + yF * ${t6} - ${s}; + int xF = wF + yF * ${t10} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; @@ -20636,7 +20632,7 @@ var Th = class { var _h = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t6 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t6 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left; + let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t10 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left; this.userCode = ` const ivec3 pads = ivec3(${p}, ${u}, ${c}); @@ -20652,7 +20648,7 @@ var _h = class { int dyCCorner = dyCorner.z; float dotProd = 0.0; - for (int wF = 0; wF < ${t6}; wF++) { + for (int wF = 0; wF < ${t10}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { @@ -20660,7 +20656,7 @@ var _h = class { } int idyF = int(dyF); - int wFPerm = ${t6} - 1 - wF; + int wFPerm = ${t10} - 1 - wF; for (int wR = 0; wR < ${o}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; @@ -20697,46 +20693,46 @@ var _h = class { `; } }; -function $Y(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new kh(m); - return t6.runWebGLProgram(d, [n, s], "float32"); +function YY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new kh(m); + return t10.runWebGLProgram(d, [n, s], "float32"); } -var MA = { kernelName: cp, backendName: "webgl", kernelFunc: $Y }; -function AY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = new Nh(m); - return t6.runWebGLProgram(d, [n, s], "float32"); +var UA = { kernelName: oi, backendName: "webgl", kernelFunc: YY }; +function QY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = new Nh(m); + return t10.runWebGLProgram(d, [n, s], "float32"); } -var LA = { kernelName: Ho, backendName: "webgl", kernelFunc: AY }; -function RY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Ch(u); - return t6.runWebGLProgram(c, [n, s], "float32"); +var GA = { kernelName: Go, backendName: "webgl", kernelFunc: QY }; +function ZY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Ch(u); + return t10.runWebGLProgram(c, [n, s], "float32"); } -var BA = { kernelName: lp, backendName: "webgl", kernelFunc: RY }; -function FY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = S.computeConv3DInfo(n.shape, p, a, 1, i), c = new Th(u); - return t6.runWebGLProgram(c, [n, s], "float32"); +var HA = { kernelName: gp, backendName: "webgl", kernelFunc: ZY }; +function JY(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = S.computeConv3DInfo(n.shape, p, a, 1, i), c = new Th(u); + return t10.runWebGLProgram(c, [n, s], "float32"); } -var VA = { kernelName: vm, backendName: "webgl", kernelFunc: FY }; -function DY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = S.computeConv3DInfo(p, s.shape, i, 1, a), c = new _h(u); - return t6.runWebGLProgram(c, [n, s], "float32"); +var KA = { kernelName: km, backendName: "webgl", kernelFunc: JY }; +function eQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = S.computeConv3DInfo(p, s.shape, i, 1, a), c = new _h(u); + return t10.runWebGLProgram(c, [n, s], "float32"); } -var zA = { kernelName: mp, backendName: "webgl", kernelFunc: DY }; -var OY = _o + ` +var qA = { kernelName: xp, backendName: "webgl", kernelFunc: eQ }; +var tQ = $o + ` return cos(x); `; -var PY = ge({ opSnippet: OY }); -var WA = { kernelName: qo, backendName: "webgl", kernelFunc: PY }; -var MY = ` +var rQ = he({ opSnippet: tQ }); +var jA = { kernelName: Ho, backendName: "webgl", kernelFunc: rQ }; +var oQ = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `; -var LY = ge({ opSnippet: MY }); -var UA = { kernelName: Ko, backendName: "webgl", kernelFunc: LY }; -var Eh = class { - constructor(e, t6, o, n, s) { +var nQ = he({ opSnippet: oQ }); +var XA = { kernelName: Ko, backendName: "webgl", kernelFunc: nQ }; +var $h = class { + constructor(e, t10, o, n, s) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = []; - let [a, i, p, u] = e, [c] = t6, [l, m] = o; + let [a, i, p, u] = e, [c] = t10, [l, m] = o; this.outputShape = [c, l, m, u]; let d = n === "bilinear" ? 1 : 0, [f, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, x, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${f} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${f}`], [C, w, k] = m > 1 ? [`${(p - 1) / (m - 1)}`, "(x2-x1) * width_ratio", `x1*${h} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${h}`]; this.userCode = ` @@ -20803,36 +20799,36 @@ var Eh = class { `; } }; -var BY = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Eh(n.shape, s.shape, i, p, u); - return t6.runWebGLProgram(c, [n, s, a], "float32"); +var sQ = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new $h(n.shape, s.shape, i, p, u); + return t10.runWebGLProgram(c, [n, s, a], "float32"); }; -var GA = { kernelName: Yo, backendName: "webgl", kernelFunc: BY }; -var Lu; +var YA = { kernelName: Xo, backendName: "webgl", kernelFunc: sQ }; +var Uu; (function(r) { r.Prod = "*", r.Sum = "+"; -})(Lu || (Lu = {})); -var El = class { - constructor(e, t6, o, n) { - this.op = e, this.outputShape = t6, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }]; - let s = this.outputShape.length, a = this.op === Lu.Prod ? "1.0" : "0.0", i = o ? a : `getX(${HA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; +})(Uu || (Uu = {})); +var Rl = class { + constructor(e, t10, o, n) { + this.op = e, this.outputShape = t10, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }]; + let s = this.outputShape.length, a = this.op === Uu.Prod ? "1.0" : "0.0", i = o ? a : `getX(${QA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; o ? (u = n ? `end != ${p - 1}` : "end != 0", c = n ? "end + 1" : "end - 1") : (u = n ? `end + pow2 < ${p}` : "end >= pow2", c = n ? "end + pow2" : "end - pow2"), this.userCode = ` void main() { - ${_e(s)} coords = getOutputCoords(); - int end = ${qA(s, "coords", this.op)}; + ${$e(s)} coords = getOutputCoords(); + int end = ${ZA(s, "coords", this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; - ${qA(s, "coords", this.op)} = idx; - val ${this.op}= getX(${HA(s, "coords", this.op)}); + ${ZA(s, "coords", this.op)} = idx; + val ${this.op}= getX(${QA(s, "coords", this.op)}); } setOutput(val); } `; } }; -function HA(r, e, t6) { +function QA(r, e, t10) { if (r === 1) return `${e}`; if (r === 2) @@ -20841,9 +20837,9 @@ function HA(r, e, t6) { return `${e}.x, ${e}.y, ${e}.z`; if (r === 4) return `${e}.x, ${e}.y, ${e}.z, ${e}.w`; - throw new Error(`Cumulative ${t6} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`); } -function qA(r, e, t6) { +function ZA(r, e, t10) { if (r === 1) return `${e}`; if (r === 2) @@ -20852,54 +20848,54 @@ function qA(r, e, t6) { return `${e}.z`; if (r === 4) return `${e}.w`; - throw new Error(`Cumulative ${t6} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`); } -function $h(r, e, t6, o, n, s) { +function Eh(r, e, t10, o, n, s) { let a = e.shape.length, i = S.getAxesPermutation([o], a), p = e; - i != null && (p = xt({ inputs: { x: e }, backend: t6, attrs: { perm: i } })); + i != null && (p = yt({ inputs: { x: e }, backend: t10, attrs: { perm: i } })); let u = S.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = At({ inputs: { x: p }, backend: t6 }); + let c = p.shape[u], l = At({ inputs: { x: p }, backend: t10 }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { - let d = new El(r, p.shape, false, s), f = [[m]], h = l; - l = t6.runWebGLProgram(d, [l], l.dtype, f), t6.disposeIntermediateTensorInfo(h); + let d = new Rl(r, p.shape, false, s), f = [[m]], h = l; + l = t10.runWebGLProgram(d, [l], l.dtype, f), t10.disposeIntermediateTensorInfo(h); } if (n) { - let m = new El(r, p.shape, n, s), d = l; - l = t6.runWebGLProgram(m, [l], l.dtype), t6.disposeIntermediateTensorInfo(d); + let m = new Rl(r, p.shape, n, s), d = l; + l = t10.runWebGLProgram(m, [l], l.dtype), t10.disposeIntermediateTensorInfo(d); } if (i != null) { - let m = S.getUndoAxesPermutation(i), d = xt({ inputs: { x: l }, backend: t6, attrs: { perm: m } }); - return t6.disposeIntermediateTensorInfo(l), t6.disposeIntermediateTensorInfo(p), d; + let m = S.getUndoAxesPermutation(i), d = yt({ inputs: { x: l }, backend: t10, attrs: { perm: m } }); + return t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(p), d; } return l; } -function VY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return $h(Lu.Prod, n, t6, s, a, i); +function aQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return Eh(Uu.Prod, n, t10, s, a, i); } -var KA = { kernelName: jo, backendName: "webgl", kernelFunc: VY }; -function zY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return $h(Lu.Sum, n, t6, s, a, i); +var JA = { kernelName: qo, backendName: "webgl", kernelFunc: aQ }; +function iQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return Eh(Uu.Sum, n, t10, s, a, i); } -var jA = { kernelName: Xo, backendName: "webgl", kernelFunc: zY }; -function WY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; +var eR = { kernelName: jo, backendName: "webgl", kernelFunc: iQ }; +function uQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = t6.readSync(n.dataId), u = t6.readSync(s.dataId), c = qf(p, u, s.dtype, s.shape, a); - return t6.makeTensorInfo([a], s.dtype, c); + let p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = Kf(p, u, s.dtype, s.shape, a); + return t10.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = t6.bufferSync(n), u = t6.bufferSync(s), c = OE(p, u, a, i); - return t6.makeTensorInfo(c.shape, s.dtype, c.values); + let p = t10.bufferSync(n), u = t10.bufferSync(s), c = z$(p, u, a, i); + return t10.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var XA = { kernelName: ti, backendName: "webgl", kernelFunc: WY }; +var tR = { kernelName: ni, backendName: "webgl", kernelFunc: uQ }; var Ah = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t6, this.dataFormat = o, this.userCode = ` + constructor(e, t10, o) { + this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t10, this.dataFormat = o, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -20907,11 +20903,11 @@ var Ah = class { int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; - int in_h = h / ${t6}; - int offset_h = imod(h, ${t6}); - int in_w = w / ${t6}; - int offset_w = imod(w, ${t6}); - int offset_d = (offset_h * ${t6} + offset_w) * + int in_h = h / ${t10}; + int offset_h = imod(h, ${t10}); + int in_w = w / ${t10}; + int offset_w = imod(w, ${t10}); + int offset_d = (offset_h * ${t10} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; @@ -20936,14 +20932,14 @@ var Ah = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function UY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Ah(f, s, a); - return t6.runWebGLProgram(h, [n], n.dtype); +function pQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Ah(f, s, a); + return t10.runWebGLProgram(h, [n], n.dtype); } -var YA = { kernelName: Qo, backendName: "webgl", kernelFunc: UY }; -var xc = class { - constructor(e, t6 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ct(this.outputShape.length); +var rR = { kernelName: Yo, backendName: "webgl", kernelFunc: pQ }; +var yc = class { + constructor(e, t10 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length); let a = e.filterHeight, i = e.filterWidth, p = e.outChannels / e.inChannels, u = "", c = ""; o && (n ? u = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); @@ -20956,8 +20952,8 @@ var xc = class { ${o} } `, c = "result = activation(result);"); - let l = t6 ? "result += getBiasAtOutCoords();" : ""; - t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let l = t10 ? "result += getBiasAtOutCoords();" : ""; + t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${u} void main() { @@ -21003,9 +20999,9 @@ var xc = class { `; } }; -var yc = class { - constructor(e, t6 = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ct(this.outputShape.length); +var bc = class { + constructor(e, t10 = false, o = null, n = false, s = false) { + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length); let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, d = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -21193,8 +21189,8 @@ var yc = class { }` : f = `vec4 activation(vec4 x) { ${o} }`, h = "result = activation(result);"); - let g = t6 ? "result += getBiasAtOutCoords();" : ""; - t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` + let g = t10 ? "result += getBiasAtOutCoords();" : ""; + t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = ` ${f} void main() { @@ -21220,19 +21216,19 @@ var yc = class { `; } }; -function GY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; +function cQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; c == null && (c = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`); let l = S.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m; - O().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new yc(l) : m = new xc(l); + O().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new bc(l) : m = new yc(l); let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]]; - return t6.runWebGLProgram(m, [n, s], "float32", d); + return t10.runWebGLProgram(m, [n, s], "float32", d); } -var QA = { kernelName: Zo, backendName: "webgl", kernelFunc: GY }; +var oR = { kernelName: Qo, backendName: "webgl", kernelFunc: cQ }; var Rh = class { constructor(e) { this.variableNames = ["x", "dy"], this.outputShape = e.filterShape; - let t6 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.outChannels / e.inChannels; + let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = e.padInfo.left, a = e.outChannels / e.inChannels; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -21247,7 +21243,7 @@ var Rh = class { // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t6} - ${n}; + int xR = wR + yR * ${t10} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; @@ -21271,10 +21267,10 @@ var Rh = class { `; } }; -var Fh = class { +var Dh = class { constructor(e) { this.variableNames = ["dy", "W"], this.outputShape = e.inShape; - let t6 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t6 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels; + let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t10 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels; this.userCode = ` const ivec2 pads = ivec2(${a}, ${i}); @@ -21288,7 +21284,7 @@ var Fh = class { float dotProd = 0.0; - for (int wR = 0; wR < ${t6}; wR++) { + for (int wR = 0; wR < ${t10}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -21296,7 +21292,7 @@ var Fh = class { } int idyR = int(dyR); - int wRPerm = ${t6} - 1 - wR; + int wRPerm = ${t10} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; @@ -21323,17 +21319,17 @@ var Fh = class { `; } }; -function HY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Rh(l); - return t6.runWebGLProgram(m, [n, s], "float32"); +function lQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Rh(l); + return t10.runWebGLProgram(m, [n, s], "float32"); } -var ZA = { kernelName: dp, backendName: "webgl", kernelFunc: HY }; -function qY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Fh(l); - return t6.runWebGLProgram(m, [n, s], "float32"); +var nR = { kernelName: yp, backendName: "webgl", kernelFunc: lQ }; +function mQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Dh(l); + return t10.runWebGLProgram(m, [n, s], "float32"); } -var JA = { kernelName: fp, backendName: "webgl", kernelFunc: qY }; -var Dh = class { +var sR = { kernelName: bp, backendName: "webgl", kernelFunc: mQ }; +var Fh = class { constructor(e) { this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = ` void main() { @@ -21344,15 +21340,15 @@ var Dh = class { `; } }; -function KY(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: t6, attrs: { shape: [s] } }), i = new Dh(s), p = t6.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: t6, attrs: { shape: n } }); - return t6.disposeIntermediateTensorInfo(a), t6.disposeIntermediateTensorInfo(p), u; +function dQ(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = re({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new Fh(s), p = t10.runWebGLProgram(i, [a], a.dtype), u = re({ inputs: { x: p }, backend: t10, attrs: { shape: n } }); + return t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(p), u; } -var eR = { kernelName: hp, backendName: "webgl", kernelFunc: KY }; +var aR = { kernelName: si, backendName: "webgl", kernelFunc: dQ }; var Oh = class { constructor(e) { this.variableNames = ["x", "W"], this.outputShape = e.outShape; - let { inHeight: t6, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n; + let { inHeight: t10, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n; this.userCode = ` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); @@ -21371,7 +21367,7 @@ var Oh = class { for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${u}; - if (hIn >= 0 && hIn < ${t6}) { + if (hIn >= 0 && hIn < ${t10}) { for (int w = 0; w < ${p}; w++) { int wIn = wBeg + w * ${c}; @@ -21394,35 +21390,35 @@ var Oh = class { `; } }; -function jY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Oh(u); - c = t6.runWebGLProgram(l, [n, s], "float32"); - let m = te({ inputs: { x: c }, backend: t6, attrs: { shape: u.outShape } }); - return t6.disposeIntermediateTensorInfo(c), m; +function fQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Oh(u); + c = t10.runWebGLProgram(l, [n, s], "float32"); + let m = re({ inputs: { x: c }, backend: t10, attrs: { shape: u.outShape } }); + return t10.disposeIntermediateTensorInfo(c), m; } -var tR = { kernelName: gp, backendName: "webgl", kernelFunc: jY }; -function XY(r) { - let { inputs: e, backend: t6, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); +var iR = { kernelName: ai, backendName: "webgl", kernelFunc: fQ }; +function hQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); S.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C; - S.isIdentityPermutation(x) ? C = s[g] : (C = xt({ inputs: { x: s[g] }, backend: t6, attrs: { perm: x } }), f.push(C)); + S.isIdentityPermutation(x) ? C = s[g] : (C = yt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C)); let w = C.shape.slice(); for (let k = 0; k < b.length; ++k) w.splice(b[k], 0, 1); - y.arraysEqual(C.shape, w) || (C = te({ inputs: { x: C }, backend: t6, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = Tl({ inputs: { a: C, b: m }, backend: t6 }), f.push(m)); + y.arraysEqual(C.shape, w) || (C = re({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = El({ inputs: { a: C, b: m }, backend: t10 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = Ou({ inputs: { x: m }, backend: t6, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = Vu({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t6.disposeIntermediateTensorInfo(h); + h !== m && t10.disposeIntermediateTensorInfo(h); return m; } -var rR = { kernelName: ri, backendName: "webgl", kernelFunc: XY }; -var YY = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; -var QY = ` +var uR = { kernelName: ii, backendName: "webgl", kernelFunc: hQ }; +var gQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; +var xQ = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -21432,25 +21428,25 @@ var QY = ` return result; `; -var ZY = ge({ opSnippet: YY, packedOpSnippet: QY }); -var oR = { kernelName: en, backendName: "webgl", kernelFunc: ZY }; -var JY = "return (b >= 1.0) ? a : a * (b + 1.0);"; -var eQ = ` +var yQ = he({ opSnippet: gQ, packedOpSnippet: xQ }); +var pR = { kernelName: Jo, backendName: "webgl", kernelFunc: yQ }; +var bQ = "return (b >= 1.0) ? a : a * (b + 1.0);"; +var CQ = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `; -var tQ = (r) => { - let { inputs: e, backend: t6 } = r, { dy: o, y: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new To(eQ, o.shape, n.shape) : new io(JY, o.shape, n.shape); - return t6.runWebGLProgram(s, [o, n], o.dtype); +var SQ = (r) => { + let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(CQ, o.shape, n.shape) : new io(bQ, o.shape, n.shape); + return t10.runWebGLProgram(s, [o, n], o.dtype); }; -var nR = { kernelName: km, backendName: "webgl", kernelFunc: tQ }; -var rQ = ` +var cR = { kernelName: Nm, backendName: "webgl", kernelFunc: SQ }; +var wQ = ` return vec4(equal(a, b)); `; -var oQ = "return float(a == b);"; -var nQ = tt({ opSnippet: oQ, packedOpSnippet: rQ, dtype: "bool", cpuKernelImpl: BE }); -var sR = { kernelName: tn, backendName: "webgl", kernelFunc: nQ }; -var sQ = ` +var IQ = "return float(a == b);"; +var vQ = tt({ opSnippet: IQ, packedOpSnippet: wQ, dtype: "bool", cpuKernelImpl: H$ }); +var lR = { kernelName: en, backendName: "webgl", kernelFunc: vQ }; +var kQ = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -21466,12 +21462,12 @@ var sQ = ` float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `; -var aQ = ge({ opSnippet: sQ }); -var aR = { kernelName: ma, backendName: "webgl", kernelFunc: aQ }; -var iQ = _o + ` +var NQ = he({ opSnippet: kQ }); +var mR = { kernelName: da, backendName: "webgl", kernelFunc: NQ }; +var TQ = $o + ` return exp(x); `; -var uQ = ` +var _Q = ` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -21481,21 +21477,21 @@ var uQ = ` return result; `; -var Aw = ge({ opSnippet: iQ, packedOpSnippet: uQ, cpuKernelImpl: VE, dtype: "float32" }); -var iR = { kernelName: rn, backendName: "webgl", kernelFunc: Aw }; +var Ww = he({ opSnippet: TQ, packedOpSnippet: _Q, cpuKernelImpl: K$, dtype: "float32" }); +var dR = { kernelName: tn, backendName: "webgl", kernelFunc: Ww }; function Ph(r) { - let { inputs: e, attrs: t6, backend: o } = r, { dim: n } = t6, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; - return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } }); + let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; + return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), re({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var uR = { kernelName: bs, backendName: "webgl", kernelFunc: Ph }; -var pR = "return exp(x) - 1.0;"; -var pQ = ge({ opSnippet: pR, packedOpSnippet: pR, cpuKernelImpl: zE }); -var cR = { kernelName: da, backendName: "webgl", kernelFunc: pQ }; -var $l = class { - constructor(e, t6, o) { +var fR = { kernelName: Ss, backendName: "webgl", kernelFunc: Ph }; +var hR = "return exp(x) - 1.0;"; +var $Q = he({ opSnippet: hR, packedOpSnippet: hR, cpuKernelImpl: q$ }); +var gR = { kernelName: fa, backendName: "webgl", kernelFunc: $Q }; +var Dl = class { + constructor(e, t10, o) { this.variableNames = ["real", "imag"]; - let n = t6[1]; - this.outputShape = t6; + let n = t10[1]; + this.outputShape = t10; let s = o ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = o ? `${n}.0` : "1.0", i; if (e === "real") i = "return real * expR - imag * expI;"; @@ -21539,19 +21535,19 @@ var $l = class { `; } }; -function Mh(r, e, t6) { - let o = t6.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = te({ inputs: { x: r }, backend: t6, attrs: { shape: [a, s] } }), p = i.shape, u = new $l("real", p, e), c = new $l("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t6.runWebGLProgram(u, l, "float32"), d = t6.runWebGLProgram(c, l, "float32"), f = Rr({ inputs: { real: m, imag: d }, backend: t6 }); - t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d); - let h = te({ inputs: { x: f }, backend: t6, attrs: { shape: r.shape } }); - return t6.disposeIntermediateTensorInfo(i), t6.disposeIntermediateTensorInfo(f), h; +function Mh(r, e, t10) { + let o = t10.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = re({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }), p = i.shape, u = new Dl("real", p, e), c = new Dl("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Ar({ inputs: { real: m, imag: d }, backend: t10 }); + t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d); + let h = re({ inputs: { x: f }, backend: t10, attrs: { shape: r.shape } }); + return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(f), h; } -function cQ(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e; - return Mh(o, false, t6); +function EQ(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e; + return Mh(o, false, t10); } -var lR = { kernelName: oi, backendName: "webgl", kernelFunc: cQ }; +var xR = { kernelName: ui, backendName: "webgl", kernelFunc: EQ }; var Lh = class { - constructor(e, t6) { + constructor(e, t10) { this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = ` void main() { // Input can be obtained from uniform value. @@ -21560,8 +21556,8 @@ var Lh = class { `; } }; -function Ga(r) { - let { backend: e, attrs: t6 } = r, { shape: o, value: n } = t6, { dtype: s } = t6; +function qa(r) { + let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), e.makeTensorInfo(o, s, a); @@ -21570,19 +21566,19 @@ function Ga(r) { return e.runWebGLProgram(a, [], s, i); } } -var mR = { kernelName: Cs, backendName: "webgl", kernelFunc: Ga }; +var yR = { kernelName: ws, backendName: "webgl", kernelFunc: qa }; var Bh = class { constructor(e) { this.variableNames = ["Image"], this.outputShape = []; - let t6 = e[2]; + let t10 = e[2]; this.outputShape = e, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; - int coordX = ${t6} - x - 1; + int coordX = ${t10} - x - 1; float outputValue; - if(coordX >= 0 && coordX < ${t6}) { + if(coordX >= 0 && coordX < ${t10}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); @@ -21592,14 +21588,14 @@ var Bh = class { `; } }; -var dR = { kernelName: on, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => { - let { image: t6 } = r, o = e, n = new Bh(t6.shape); - return o.runWebGLProgram(n, [t6], t6.dtype); +var bR = { kernelName: rn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => { + let { image: t10 } = r, o = e, n = new Bh(t10.shape); + return o.runWebGLProgram(n, [t10], t10.dtype); } }; -var fR = "return floor(x);"; -var lQ = ge({ opSnippet: fR, packedOpSnippet: fR, cpuKernelImpl: WE }); -var hR = { kernelName: nn, backendName: "webgl", kernelFunc: lQ }; -var mQ = ` +var CR = "return floor(x);"; +var AQ = he({ opSnippet: CR, packedOpSnippet: CR, cpuKernelImpl: j$ }); +var SR = { kernelName: on, backendName: "webgl", kernelFunc: AQ }; +var RQ = ` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -21610,7 +21606,7 @@ var mQ = ` return NAN; } `; -var dQ = ` +var DQ = ` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -21632,12 +21628,12 @@ var dQ = ` } return vec4(result); `; -var fQ = tt({ opSnippet: mQ, packedOpSnippet: dQ, dtype: "int32" }); -var gR = { kernelName: sn, backendName: "webgl", kernelFunc: fQ }; +var FQ = tt({ opSnippet: RQ, packedOpSnippet: DQ, dtype: "int32" }); +var wR = { kernelName: nn, backendName: "webgl", kernelFunc: FQ }; var Vh = class { constructor(e) { this.variableNames = ["A"]; - let t6 = St(), [o, n] = e; + let t10 = wt(), [o, n] = e; this.outputShape = e, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -21646,7 +21642,7 @@ var Vh = class { int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); - vec4 values = ${t6.texture2D}(A, uv); + vec4 values = ${t10.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -21666,7 +21662,7 @@ var Vh = class { var zh = class { constructor(e) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true; - let t6 = St(), [o, n] = e; + let t10 = wt(), [o, n] = e; this.outputShape = e, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -21683,7 +21679,7 @@ var zh = class { vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); - vec4 values = ${t6.texture2D}(A, uv); + vec4 values = ${t10.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -21699,73 +21695,73 @@ var zh = class { } } - ${t6.output} = result; + ${t10.output} = result; } `; } }; -var xR = { kernelName: Zi, backendName: "webgl", kernelFunc: hQ }; -var bc; -var Rw = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -function hQ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; +var IR = { kernelName: Zi, backendName: "webgl", kernelFunc: OQ }; +var Cc; +var Uw = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +function OQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; if (i || a) { let h = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (bc == null || h !== Rw) && (Rw = h, bc = document.createElement("canvas").getContext("2d", { willReadFrequently: Rw })), bc.canvas.width = p, bc.canvas.height = u, bc.drawImage(n, 0, 0, p, u), n = bc.canvas; + (Cc == null || h !== Uw) && (Uw = h, Cc = document.createElement("canvas").getContext("2d", { willReadFrequently: Uw })), Cc.canvas.width = p, Cc.canvas.height = u, Cc.drawImage(n, 0, 0, p, u), n = Cc.canvas; } - let m = t6.makeTensorInfo(c, "int32"); - t6.texData.get(m.dataId).usage = ir.PIXELS, t6.gpgpu.uploadPixelDataToTexture(t6.getTexture(m.dataId), n); - let d = O().getBool("WEBGL_PACK") ? new zh(l) : new Vh(l), f = t6.runWebGLProgram(d, [m], "int32"); - return t6.disposeData(m.dataId), f; + let m = t10.makeTensorInfo(c, "int32"); + t10.texData.get(m.dataId).usage = ur.PIXELS, t10.gpgpu.uploadPixelDataToTexture(t10.getTexture(m.dataId), n); + let d = O().getBool("WEBGL_PACK") ? new zh(l) : new Vh(l), f = t10.runWebGLProgram(d, [m], "int32"); + return t10.disposeData(m.dataId), f; } -function gQ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, w = i != null, k = d === "leakyrelu", _ = () => { +function PQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, w = i != null, k = d === "leakyrelu", _ = () => { let A = [n, s], R = (D, P) => { if (P === "NCHW" && D.shape.length === 1 && D.shape[0] !== 1) { - let M = te({ inputs: { x: D }, backend: t6, attrs: { shape: [D.shape[0], 1, 1] } }); + let M = re({ inputs: { x: D }, backend: t10, attrs: { shape: [D.shape[0], 1, 1] } }); return b.push(M), M; } return D; }; if (C && A.push(R(a, c)), w && A.push(R(i, c)), k) { - let D = t6.makeTensorInfo([], "float32", y.createScalarValue(f, "float32")); + let D = t10.makeTensorInfo([], "float32", y.createScalarValue(f, "float32")); A.push(D), b.push(D); } return A; }; if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID")) - x = Ih({ x: n, filter: s, convInfo: g, backend: t6, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + x = Ih({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); else if (g.strideWidth <= 2 && h === "channelsLast" && O().getBool("WEBGL_EXP_CONV")) { - let A = d ? Wa(d, true) : null, R = new gc(g, C, A, w, k), D = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], P = _(); - x = t6.runWebGLProgram(R, P, "float32", D); + let A = d ? Ha(d, true) : null, R = new xc(g, C, A, w, k), D = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], P = _(); + x = t10.runWebGLProgram(R, P, "float32", D); } else if (O().getBool("WEBGL_CONV_IM2COL")) - x = vh({ x: n, filter: s, convInfo: g, backend: t6, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + x = vh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); else { - let A = d ? Wa(d, false) : null, R = new hc(g, C, A, w, k), D = _(); - x = t6.runWebGLProgram(R, D, "float32"); + let A = d ? Ha(d, false) : null, R = new gc(g, C, A, w, k), D = _(); + x = t10.runWebGLProgram(R, D, "float32"); } - let $ = te({ inputs: { x }, backend: t6, attrs: { shape: g.outShape } }); - return b.push(x), b.forEach((A) => t6.disposeIntermediateTensorInfo(A)), $; + let E = re({ inputs: { x }, backend: t10, attrs: { shape: g.outShape } }); + return b.push(x), b.forEach((A) => t10.disposeIntermediateTensorInfo(A)), E; } -var yR = { kernelName: ho, backendName: "webgl", kernelFunc: gQ }; -function xQ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; +var vR = { kernelName: go, backendName: "webgl", kernelFunc: PQ }; +function MQ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; h == null && (h = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`); - let g = S.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = O().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? Wa(m, x) : null, C = [n, s], w = a != null, k = i != null, _ = m === "leakyrelu"; + let g = S.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = O().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? Ha(m, x) : null, C = [n, s], w = a != null, k = i != null, _ = m === "leakyrelu"; if (w && C.push(a), k && C.push(i), _) { - let D = t6.makeTensorInfo([], "float32", y.createScalarValue(d, "float32")); + let D = t10.makeTensorInfo([], "float32", y.createScalarValue(d, "float32")); C.push(D), f.push(D); } - let $; - x ? $ = new yc(g, w, b, k, _) : $ = new xc(g, w, b, k, _); - let A = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], R = t6.runWebGLProgram($, C, "float32", A); - return f.forEach((D) => t6.disposeIntermediateTensorInfo(D)), R; + let E; + x ? E = new bc(g, w, b, k, _) : E = new yc(g, w, b, k, _); + let A = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], R = t10.runWebGLProgram(E, C, "float32", A); + return f.forEach((D) => t10.disposeIntermediateTensorInfo(D)), R; } -var bR = { kernelName: go, backendName: "webgl", kernelFunc: xQ }; +var kR = { kernelName: xo, backendName: "webgl", kernelFunc: MQ }; var Wh = class { - constructor(e, t6, o, n) { - this.sliceDim = e, this.strides = t6, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o; - let s = _e(o.length), a = ` + constructor(e, t10, o, n) { + this.sliceDim = e, this.strides = t10, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o; + let s = $e(o.length), a = ` int index;`; for (let i = 0; i < this.sliceDim; i++) a += ` @@ -21786,20 +21782,20 @@ var Wh = class { `; } }; -function yQ(r) { - let { inputs: e, backend: t6 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: t6, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: t6, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); - if (t6.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let x = t6.readSync(n.dataId), b = t6.bufferSync(o), C = UE(x, b, o.dtype, u, a, c, l, o.shape, i); - return t6.makeTensorInfo(p, o.dtype, C.values); +function LQ(r) { + let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = re({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); + if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { + let x = t10.readSync(n.dataId), b = t10.bufferSync(o), C = X$(x, b, o.dtype, u, a, c, l, o.shape, i); + return t10.makeTensorInfo(p, o.dtype, C.values); } - let f = new Wh(a, l, [u, c], o.shape), h = t6.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: t6, attrs: { shape: p } }); - return t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(h), g; + let f = new Wh(a, l, [u, c], o.shape), h = t10.runWebGLProgram(f, [d, m], d.dtype), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: p } }); + return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g; } -var CR = { kernelName: un, backendName: "webgl", kernelFunc: yQ }; +var NR = { kernelName: an, backendName: "webgl", kernelFunc: LQ }; var Uh = class { - constructor(e, t6) { - this.variableNames = ["A", "indices"], this.outputShape = t6, this.rank = t6.length; - let o = _e(this.rank), n = bQ(e, 2); + constructor(e, t10) { + this.variableNames = ["A", "indices"], this.outputShape = t10, this.rank = t10.length; + let o = $e(this.rank), n = BQ(e, 2); this.userCode = ` void main() { ${o} resRC = getOutputCoords(); @@ -21810,81 +21806,81 @@ var Uh = class { `; } }; -function bQ(r, e) { - let t6 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; +function BQ(r, e) { + let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; for (let n = 0; n < r.length; n++) - n === 2 ? o.push("index") : o.push(`${t6[n]}`); + n === 2 ? o.push("index") : o.push(`${t10[n]}`); return o.join(); } -function Fw(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; +function Gw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; if (O().get("DEBUG")) { - let b = t6.readSync(s.dataId), C = n.shape[p]; + let b = t10.readSync(s.dataId), C = n.shape[p]; for (let w = 0; w < b.length; ++w) { let k = b[w]; y.assert(k <= C - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${C - 1}]`); } } - let u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = te({ inputs: { x: n }, backend: t6, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = te({ inputs: { x: s }, backend: t6, attrs: { shape: [u.batchSize, c / u.batchSize] } }); + let u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = re({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } }); l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; - if (t6.shouldExecuteOnCPU([n, s]) || n.dtype === "string") { - let b = t6.bufferSync(d), C = t6.bufferSync(m), w = GE(C, b, f); - return l.forEach((k) => t6.disposeIntermediateTensorInfo(k)), t6.makeTensorInfo(u.outputShape, w.dtype, w.values); + if (t10.shouldExecuteOnCPU([n, s]) || n.dtype === "string") { + let b = t10.bufferSync(d), C = t10.bufferSync(m), w = Y$(C, b, f); + return l.forEach((k) => t10.disposeIntermediateTensorInfo(k)), t10.makeTensorInfo(u.outputShape, w.dtype, w.values); } - let h = new Uh(m.shape, f), g = t6.runWebGLProgram(h, [m, d], m.dtype); + let h = new Uh(m.shape, f), g = t10.runWebGLProgram(h, [m, d], m.dtype); l.push(g); - let x = te({ inputs: { x: g }, backend: t6, attrs: { shape: u.outputShape } }); - return l.forEach((b) => t6.disposeIntermediateTensorInfo(b)), x; + let x = re({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } }); + return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x; } -var SR = { kernelName: Ss, backendName: "webgl", kernelFunc: Fw }; -var CQ = "return float(a > b);"; -var SQ = ` +var TR = { kernelName: Is, backendName: "webgl", kernelFunc: Gw }; +var VQ = "return float(a > b);"; +var zQ = ` return vec4(greaterThan(a, b)); `; -var wQ = tt({ opSnippet: CQ, packedOpSnippet: SQ, cpuKernelImpl: HE, dtype: "bool" }); -var wR = { kernelName: pn, backendName: "webgl", kernelFunc: wQ }; -var IQ = "return float(a >= b);"; -var vQ = ` +var WQ = tt({ opSnippet: VQ, packedOpSnippet: zQ, cpuKernelImpl: Q$, dtype: "bool" }); +var _R = { kernelName: un, backendName: "webgl", kernelFunc: WQ }; +var UQ = "return float(a >= b);"; +var GQ = ` return vec4(greaterThanEqual(a, b)); `; -var kQ = tt({ opSnippet: IQ, packedOpSnippet: vQ, dtype: "bool", cpuKernelImpl: qE }); -var IR = { kernelName: cn, backendName: "webgl", kernelFunc: kQ }; -function NQ(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e; - return Mh(o, true, t6); +var HQ = tt({ opSnippet: UQ, packedOpSnippet: GQ, dtype: "bool", cpuKernelImpl: Z$ }); +var $R = { kernelName: pn, backendName: "webgl", kernelFunc: HQ }; +function KQ(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e; + return Mh(o, true, t10); } -var vR = { kernelName: ni, backendName: "webgl", kernelFunc: NQ }; -var TQ = "return float(!isnan(x) && !isinf(x));"; -var _Q = ge({ opSnippet: TQ, dtype: "bool" }); -var kR = { kernelName: fa, backendName: "webgl", kernelFunc: _Q }; -var EQ = "return float(isinf(x));"; -var $Q = ge({ opSnippet: EQ, dtype: "bool" }); -var NR = { kernelName: ha, backendName: "webgl", kernelFunc: $Q }; -var AQ = "return float(isnan(x));"; -var RQ = ge({ opSnippet: AQ, dtype: "bool" }); -var TR = { kernelName: ln, backendName: "webgl", kernelFunc: RQ }; -var FQ = "return float(a < b);"; -var DQ = ` +var ER = { kernelName: pi, backendName: "webgl", kernelFunc: KQ }; +var qQ = "return float(!isnan(x) && !isinf(x));"; +var jQ = he({ opSnippet: qQ, dtype: "bool" }); +var AR = { kernelName: ha, backendName: "webgl", kernelFunc: jQ }; +var XQ = "return float(isinf(x));"; +var YQ = he({ opSnippet: XQ, dtype: "bool" }); +var RR = { kernelName: ga, backendName: "webgl", kernelFunc: YQ }; +var QQ = "return float(isnan(x));"; +var ZQ = he({ opSnippet: QQ, dtype: "bool" }); +var DR = { kernelName: cn, backendName: "webgl", kernelFunc: ZQ }; +var JQ = "return float(a < b);"; +var e7 = ` return vec4(lessThan(a, b)); `; -var OQ = tt({ opSnippet: FQ, packedOpSnippet: DQ, cpuKernelImpl: KE, dtype: "bool" }); -var _R = { kernelName: dn, backendName: "webgl", kernelFunc: OQ }; -var PQ = "return float(a <= b);"; -var MQ = ` +var t7 = tt({ opSnippet: JQ, packedOpSnippet: e7, cpuKernelImpl: J$, dtype: "bool" }); +var FR = { kernelName: mn, backendName: "webgl", kernelFunc: t7 }; +var r7 = "return float(a <= b);"; +var o7 = ` return vec4(lessThanEqual(a, b)); `; -var LQ = tt({ opSnippet: PQ, packedOpSnippet: MQ, cpuKernelImpl: jE, dtype: "bool" }); -var ER = { kernelName: fn, backendName: "webgl", kernelFunc: LQ }; -function BQ(r) { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, num: s } = t6, a = XE(o, n, s); +var n7 = tt({ opSnippet: r7, packedOpSnippet: o7, cpuKernelImpl: eE, dtype: "bool" }); +var OR = { kernelName: dn, backendName: "webgl", kernelFunc: n7 }; +function s7(r) { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = tE(o, n, s); return e.makeTensorInfo([a.length], "float32", a); } -var $R = { kernelName: xp, backendName: "webgl", kernelFunc: BQ }; -var VQ = _o + ` +var PR = { kernelName: li, backendName: "webgl", kernelFunc: s7 }; +var a7 = $o + ` return x < 0.0 ? 0./0. : log(x); `; -var zQ = ` +var i7 = ` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -21893,37 +21889,37 @@ var zQ = ` result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `; -var WQ = ge({ opSnippet: VQ, packedOpSnippet: zQ, cpuKernelImpl: YE }); -var AR = { kernelName: hn, backendName: "webgl", kernelFunc: WQ }; -var UQ = _o + ` +var u7 = he({ opSnippet: a7, packedOpSnippet: i7, cpuKernelImpl: rE }); +var MR = { kernelName: fn, backendName: "webgl", kernelFunc: u7 }; +var p7 = $o + ` return log(1.0 + x); `; -var GQ = ge({ opSnippet: UQ }); -var RR = { kernelName: ga, backendName: "webgl", kernelFunc: GQ }; -var HQ = "return float(a >= 1.0 && b >= 1.0);"; -var qQ = ` +var c7 = he({ opSnippet: p7 }); +var LR = { kernelName: xa, backendName: "webgl", kernelFunc: c7 }; +var l7 = "return float(a >= 1.0 && b >= 1.0);"; +var m7 = ` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `; -var KQ = tt({ opSnippet: HQ, packedOpSnippet: qQ, dtype: "bool" }); -var FR = { kernelName: gn, backendName: "webgl", kernelFunc: KQ }; -var jQ = "return float(!(x >= 1.0));"; -var XQ = ge({ opSnippet: jQ }); -var DR = { kernelName: xn, backendName: "webgl", kernelFunc: XQ }; -var YQ = "return float(a >= 1.0 || b >= 1.0);"; -var QQ = ` +var d7 = tt({ opSnippet: l7, packedOpSnippet: m7, dtype: "bool" }); +var BR = { kernelName: hn, backendName: "webgl", kernelFunc: d7 }; +var f7 = "return float(!(x >= 1.0));"; +var h7 = he({ opSnippet: f7 }); +var VR = { kernelName: gn, backendName: "webgl", kernelFunc: h7 }; +var g7 = "return float(a >= 1.0 || b >= 1.0);"; +var x7 = ` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `; -var ZQ = tt({ opSnippet: YQ, packedOpSnippet: QQ, dtype: "bool" }); -var OR = { kernelName: xa, backendName: "webgl", kernelFunc: ZQ }; +var y7 = tt({ opSnippet: g7, packedOpSnippet: x7, dtype: "bool" }); +var zR = { kernelName: xn, backendName: "webgl", kernelFunc: y7 }; var Gh = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["x"], this.outputShape = []; - let a = t6, i = e[3] - 1; + let a = t10, i = e[3] - 1; this.outputShape = e; let p, u = `float(${o}) + float(${n}) * sum`; s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, this.userCode = ` @@ -21949,9 +21945,9 @@ var Gh = class { } }; var Hh = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true; - let a = t6, i = e[3] - 1; + let a = t10, i = e[3] - 1; this.outputShape = e; let p, u = `float(${o}) + float(${n}) * sum`; s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, this.userCode = ` @@ -22019,14 +22015,14 @@ var Hh = class { `; } }; -var JQ = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = O().getBool("WEBGL_PACK_NORMALIZATION") ? new Hh(n.shape, s, a, i, p) : new Gh(n.shape, s, a, i, p); - return t6.runWebGLProgram(u, [n], n.dtype); +var b7 = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = O().getBool("WEBGL_PACK_NORMALIZATION") ? new Hh(n.shape, s, a, i, p) : new Gh(n.shape, s, a, i, p); + return t10.runWebGLProgram(u, [n], n.dtype); }; -var PR = { kernelName: yp, backendName: "webgl", kernelFunc: JQ }; -var qh = class { - constructor(e, t6, o, n, s) { - this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t6, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` +var WR = { kernelName: mi, backendName: "webgl", kernelFunc: b7 }; +var Kh = class { + constructor(e, t10, o, n, s) { + this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t10, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -22035,9 +22031,9 @@ var qh = class { float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { - int depthBegin = int(max(0.0, float(d - ${t6}))); + int depthBegin = int(max(0.0, float(d - ${t10}))); int depthEnd = int(min(float(${this.depth}), - float(d + ${t6} + 1))); + float(d + ${t10} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; @@ -22064,7 +22060,7 @@ var qh = class { else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${n}) * float(${s}) - * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); @@ -22084,28 +22080,28 @@ var qh = class { `; } }; -var e7 = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new qh(n.shape, i, p, u, c); - return t6.runWebGLProgram(l, [n, s, a], n.dtype); +var C7 = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Kh(n.shape, i, p, u, c); + return t10.runWebGLProgram(l, [n, s, a], n.dtype); }; -var MR = { kernelName: Nm, backendName: "webgl", kernelFunc: e7 }; -function LR(r, e, t6, o) { - let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Gr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: t6 }, backend: o }); +var UR = { kernelName: Tm, backendName: "webgl", kernelFunc: C7 }; +function GR(r, e, t10, o) { + let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = re({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Ur(i, r.dtype, "max", o), u = re({ inputs: { x: p }, attrs: { shape: t10 }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -function Dw(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = t6.shouldExecuteOnCPU([n]), d = n; +function Hw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = t10.shouldExecuteOnCPU([n]), d = n; if (l) { if (m) { - let C = t6.texData.get(d.dataId).values, w = new Array(i); - for (let $ = 0; $ < w.length; $++) - w[$] = n.shape[c[$]]; - let k = Du(C, n.shape, n.dtype, c, w); - d = t6.makeTensorInfo(w, n.dtype); - let _ = t6.texData.get(d.dataId); + let C = t10.texData.get(d.dataId).values, w = new Array(i); + for (let E = 0; E < w.length; E++) + w[E] = n.shape[c[E]]; + let k = Bu(C, n.shape, n.dtype, c, w); + d = t10.makeTensorInfo(w, n.dtype); + let _ = t10.texData.get(d.dataId); _.values = k; } else - d = Vi(n, c, t6); + d = zi(n, c, t10); u = S.getInnerMostAxes(u.length, i); } S.assertAxesAreInnerMostDims("max", u, i); @@ -22113,49 +22109,49 @@ function Dw(r) { a && (g = S.expandShapeToKeepDim(f, p)); let x; if (m) { - let C = t6.texData.get(d.dataId).values, w = QE(C, y.sizeFromShape(h), g, n.dtype); - x = t6.makeTensorInfo(g, n.dtype); - let k = t6.texData.get(x.dataId); + let C = t10.texData.get(d.dataId).values, w = oE(C, y.sizeFromShape(h), g, n.dtype); + x = t10.makeTensorInfo(g, n.dtype); + let k = t10.texData.get(x.dataId); k.values = w; } else - x = LR(d, h, g, t6); - return l && t6.disposeIntermediateTensorInfo(d), x; + x = GR(d, h, g, t10); + return l && t10.disposeIntermediateTensorInfo(d), x; } -var BR = { kernelName: yn, backendName: "webgl", kernelFunc: Dw }; -var t7 = mc + ` +var HR = { kernelName: yn, backendName: "webgl", kernelFunc: Hw }; +var S7 = dc + ` return max(a, b); `; -var r7 = ` +var w7 = ` vec4 result = vec4(max(a, b)); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Zs + ` + ` + Js + ` return result; `; -var o7 = tt({ opSnippet: t7, packedOpSnippet: r7, cpuKernelImpl: ZE }); -var VR = { kernelName: bn, backendName: "webgl", kernelFunc: o7 }; -function n7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e; - is(n, "maxPool"); +var I7 = tt({ opSnippet: S7, packedOpSnippet: w7, cpuKernelImpl: nE }); +var KR = { kernelName: bn, backendName: "webgl", kernelFunc: I7 }; +function v7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e; + us(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = S.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return At({ inputs: { x: n }, backend: t6 }); - let l = new ps(c, "max", false); - return t6.runWebGLProgram(l, [n], n.dtype); + return At({ inputs: { x: n }, backend: t10 }); + let l = new cs(c, "max", false); + return t10.runWebGLProgram(l, [n], n.dtype); } -var zR = { kernelName: Cn, backendName: "webgl", kernelFunc: n7 }; -function s7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new zi(l, "max", false); - return t6.runWebGLProgram(m, [n], n.dtype); +var qR = { kernelName: Cn, backendName: "webgl", kernelFunc: v7 }; +function k7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new Wi(l, "max", false); + return t10.runWebGLProgram(m, [n], n.dtype); } -var WR = { kernelName: bp, backendName: "webgl", kernelFunc: s7 }; -var Kh = class { +var jR = { kernelName: Cp, backendName: "webgl", kernelFunc: k7 }; +var qh = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape; - let t6 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1; + let t10 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1; this.userCode = ` const ivec2 pads = ivec2(${i}, ${p}); @@ -22173,7 +22169,7 @@ var Kh = class { float dotProd = 0.0; for (int wR = 0; wR < ${s}; wR += ${n}) { - float dyR = float(dyRCorner + wR) / ${t6}.0; + float dyR = float(dyRCorner + wR) / ${t10}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; @@ -22208,7 +22204,7 @@ var Kh = class { var jh = class { constructor(e) { this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape; - let t6 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1; + let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1; this.userCode = ` const ivec3 pads = ivec3(${l}, ${m}, ${d}); @@ -22229,7 +22225,7 @@ var jh = class { for (int wD = 0; wD < ${p}; wD += ${s}) { - float dyD = float(dyDCorner + wD) / ${t6}.0; + float dyD = float(dyDCorner + wD) / ${t10}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; @@ -22276,88 +22272,88 @@ var jh = class { `; } }; -function a7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new zi(m, "max", true), f = t6.runWebGLProgram(d, [a], a.dtype), h = new jh(m), g = t6.runWebGLProgram(h, [n, f], a.dtype); - return t6.disposeIntermediateTensorInfo(f), g; +function N7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new Wi(m, "max", true), f = t10.runWebGLProgram(d, [a], a.dtype), h = new jh(m), g = t10.runWebGLProgram(h, [n, f], a.dtype); + return t10.disposeIntermediateTensorInfo(f), g; } -var UR = { kernelName: _m, backendName: "webgl", kernelFunc: a7 }; -function i7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; - is([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new ps(m, "max", d), h = t6.runWebGLProgram(f, [i], i.dtype), g = new Kh(m), x = t6.runWebGLProgram(g, [n, h], i.dtype); - return t6.disposeIntermediateTensorInfo(h), x; +var XR = { kernelName: $m, backendName: "webgl", kernelFunc: N7 }; +function T7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s; + us([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new cs(m, "max", d), h = t10.runWebGLProgram(f, [i], i.dtype), g = new qh(m), x = t10.runWebGLProgram(g, [n, h], i.dtype); + return t10.disposeIntermediateTensorInfo(h), x; } -var GR = { kernelName: Tm, backendName: "webgl", kernelFunc: i7 }; -function HR(r, e, t6, o) { - let n = new ps(t6, "max", false), s = o.runWebGLProgram(n, [r], "float32"); - n = new ps(t6, "max", true, true, e); +var YR = { kernelName: _m, backendName: "webgl", kernelFunc: T7 }; +function QR(r, e, t10, o) { + let n = new cs(t10, "max", false), s = o.runWebGLProgram(n, [r], "float32"); + n = new cs(t10, "max", true, true, e); let a = o.runWebGLProgram(n, [r], "float32"); return [s, a]; } -var qR = { kernelName: Cp, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t6; +var ZR = { kernelName: Sp, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10; y.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`); let u = [1, 1]; y.assert(S.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); - let c = S.computePool2DInfo(o.shape, n, s, u, a), [l, m] = HR(o, i, c, p); + let c = S.computePool2DInfo(o.shape, n, s, u, a), [l, m] = QR(o, i, c, p); return [l, m]; } }; -function KR(r, e, t6, o) { - let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Gr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: t6 }, backend: o }); +function JR(r, e, t10, o) { + let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = re({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Ur(i, "float32", "mean", o), u = re({ inputs: { x: p }, attrs: { shape: t10 }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -var jR = { kernelName: Sn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { x: o } = r, { keepDims: n, axis: s } = e, a = t6, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; +var eD = { kernelName: Sn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { x: o } = r, { keepDims: n, axis: s } = e, a = t10, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; if (l) { if (m) { let w = a.texData.get(f.dataId).values, k = new Array(i); for (let A = 0; A < k.length; A++) k[A] = o.shape[c[A]]; - let _ = Du(w, o.shape, o.dtype, c, k); + let _ = Bu(w, o.shape, o.dtype, c, k); f = a.makeTensorInfo(k, o.dtype); - let $ = a.texData.get(f.dataId); - $.values = _; + let E = a.texData.get(f.dataId); + E.values = _; } else - f = Vi(o, c, a); + f = zi(o, c, a); d.push(f), u = S.getInnerMostAxes(u.length, i); } S.assertAxesAreInnerMostDims("sum", u, i); let [h, g] = S.computeOutAndReduceShapes(f.shape, u), x = h; n && (x = S.expandShapeToKeepDim(h, p)); - let b = KR(f, g, x, a); + let b = JR(f, g, x, a); for (let C of d) a.disposeIntermediateTensorInfo(C); return b; } }; -function u7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; - c != null && (l = xt({ inputs: { x: n }, backend: t6, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", u, i); - let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t6, attrs: { shape: [-1, f] } }), g = Gr(h, h.dtype, "min", t6), x; +function _7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n; + c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", u, i); + let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "min", t10), x; if (a) { let b = S.expandShapeToKeepDim(m, p); - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: b } }); + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } }); } else - x = te({ inputs: { x: g }, backend: t6, attrs: { shape: m } }); - return t6.disposeIntermediateTensorInfo(h), t6.disposeIntermediateTensorInfo(g), c != null && t6.disposeIntermediateTensorInfo(l), x; + x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); + return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var XR = { kernelName: wn, backendName: "webgl", kernelFunc: u7 }; -var p7 = mc + ` +var tD = { kernelName: wn, backendName: "webgl", kernelFunc: _7 }; +var $7 = dc + ` return min(a, b); `; -var c7 = ` +var E7 = ` vec4 result = vec4(min(a, b)); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Zs + ` + ` + Js + ` return result; `; -var l7 = tt({ opSnippet: p7, packedOpSnippet: c7, cpuKernelImpl: JE }); -var YR = { kernelName: In, backendName: "webgl", kernelFunc: l7 }; +var A7 = tt({ opSnippet: $7, packedOpSnippet: E7, cpuKernelImpl: sE }); +var rD = { kernelName: In, backendName: "webgl", kernelFunc: A7 }; var Xh = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.outputShape = t6.map((c, l) => c[0] + e[l] + c[1]); - let n = e.length, s = _e(n), a = t6.map((c) => c[0]).join(","), i = t6.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1; + constructor(e, t10, o) { + this.variableNames = ["x"], this.outputShape = t10.map((c, l) => c[0] + e[l] + c[1]); + let n = e.length, s = $e(n), a = t10.map((c) => c[0]).join(","), i = t10.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1; if (n === 1) { this.userCode = ` int start = ${a}; @@ -22395,9 +22391,9 @@ var Xh = class { } }; var Yh = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t6.map((f, h) => f[0] + e[h] + f[1]); - let n = e.length, s = _e(n), a = t6.map((f) => f[0]).join(","), i = t6.map((f, h) => f[0] + e[h]).join(","), p = $t("rc", n), u = $t("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; + constructor(e, t10, o) { + this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t10.map((f, h) => f[0] + e[h] + f[1]); + let n = e.length, s = $e(n), a = t10.map((f) => f[0]).join(","), i = t10.map((f, h) => f[0] + e[h]).join(","), p = Et("rc", n), u = Et("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; if (n === 1) { let f = ` ${s} source = rc; @@ -22464,23 +22460,23 @@ var Yh = class { `; } }; -var m7 = ({ inputs: r, backend: e, attrs: t6 }) => { - let { x: o } = r, { paddings: n, mode: s } = t6, a = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Yh(o.shape, n, s) : new Xh(o.shape, n, s); +var R7 = ({ inputs: r, backend: e, attrs: t10 }) => { + let { x: o } = r, { paddings: n, mode: s } = t10, a = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Yh(o.shape, n, s) : new Xh(o.shape, n, s); return e.runWebGLProgram(a, [o], o.dtype); }; -var QR = { kernelName: vn, backendName: "webgl", kernelFunc: m7 }; -var d7 = `if (b == 0.0) return NAN; +var oD = { kernelName: vn, backendName: "webgl", kernelFunc: R7 }; +var D7 = `if (b == 0.0) return NAN; return mod(a, b);`; -var f7 = ` +var F7 = ` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - ` + Zs + ` + ` + Js + ` return result; `; -var h7 = tt({ opSnippet: d7, packedOpSnippet: f7 }); -var ZR = { kernelName: ya, backendName: "webgl", kernelFunc: h7 }; +var O7 = tt({ opSnippet: D7, packedOpSnippet: F7 }); +var nD = { kernelName: ya, backendName: "webgl", kernelFunc: O7 }; var Qh = class { - constructor(e, t6, o) { + constructor(e, t10, o) { this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, o], this.userCode = ` void main() { ivec2 coords = getOutputCoords(); @@ -22489,7 +22485,7 @@ var Qh = class { float r = random(seed); float cdf = 0.0; - for (int i = 0; i < ${t6 - 1}; i++) { + for (int i = 0; i < ${t10 - 1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { @@ -22499,17 +22495,17 @@ var Qh = class { } // If no other event happened, last event happened. - setOutput(float(${t6 - 1})); + setOutput(float(${t10 - 1})); } `; } }; -var g7 = ` +var P7 = ` if (a == b) { return 1.0; }; return a / b;`; -var x7 = ` +var M7 = ` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -22528,25 +22524,25 @@ var x7 = ` return result; `; -var Ow = tt({ opSnippet: g7, packedOpSnippet: x7, checkOutOfBounds: true }); -var JR = { kernelName: Jo, backendName: "webgl", kernelFunc: Ow }; -var eF = "return a - b;"; -var Pw = tt({ opSnippet: eF, packedOpSnippet: eF, supportsComplex: true, cpuKernelImpl: b$ }); -var tF = { kernelName: Xn, backendName: "webgl", kernelFunc: Pw }; -function Mw(r) { - let { inputs: e, backend: t6, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Dw({ inputs: { x: n }, backend: t6, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t6, attrs: { shape: p } }), c = Pw({ inputs: { a: n, b: u }, backend: t6 }), l = Aw({ inputs: { x: c }, backend: t6 }), m = Ou({ inputs: { x: l }, backend: t6, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t6, attrs: { shape: p } }), f = Ow({ inputs: { a: l, b: d }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(i), t6.disposeIntermediateTensorInfo(u), t6.disposeIntermediateTensorInfo(c), t6.disposeIntermediateTensorInfo(l), t6.disposeIntermediateTensorInfo(m), t6.disposeIntermediateTensorInfo(d), f; +var Kw = tt({ opSnippet: P7, packedOpSnippet: M7, checkOutOfBounds: true }); +var sD = { kernelName: Zo, backendName: "webgl", kernelFunc: Kw }; +var aD = "return a - b;"; +var qw = tt({ opSnippet: aD, packedOpSnippet: aD, supportsComplex: true, cpuKernelImpl: kE }); +var iD = { kernelName: Yn, backendName: "webgl", kernelFunc: qw }; +function jw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Hw({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = re({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = qw({ inputs: { a: n, b: u }, backend: t10 }), l = Ww({ inputs: { x: c }, backend: t10 }), m = Vu({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = re({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = Kw({ inputs: { a: l, b: d }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f; } -var rF = { kernelName: qn, backendName: "webgl", kernelFunc: Mw }; -function y7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : Mw({ inputs: { logits: n }, backend: t6, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new Qh(u, c, s), m = [[a]], d = t6.runWebGLProgram(l, [p], "int32", m); - return i || t6.disposeIntermediateTensorInfo(p), d; +var uD = { kernelName: qn, backendName: "webgl", kernelFunc: jw }; +function L7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : jw({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new Qh(u, c, s), m = [[a]], d = t10.runWebGLProgram(l, [p], "int32", m); + return i || t10.disposeIntermediateTensorInfo(p), d; } -var oF = { kernelName: Sp, backendName: "webgl", kernelFunc: y7 }; -var b7 = Bt + ` +var pD = { kernelName: wp, backendName: "webgl", kernelFunc: L7 }; +var B7 = zt + ` return -x; `; -var C7 = ` +var V7 = ` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -22557,40 +22553,40 @@ var C7 = ` return result; `; -function S7(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - if (t6.shouldExecuteOnCPU([o])) { - let s = t6.texData.get(o.dataId), [a, i] = t$(s.values, o.shape, o.dtype); - return t6.makeTensorInfo(i, o.dtype, a); +function z7(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; + if (t10.shouldExecuteOnCPU([o])) { + let s = t10.texData.get(o.dataId), [a, i] = iE(s.values, o.shape, o.dtype); + return t10.makeTensorInfo(i, o.dtype, a); } let n; - return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, C7) : n = new Jt(o.shape, b7), t6.runWebGLProgram(n, [o], o.dtype); + return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Er(o.shape, V7) : n = new Jt(o.shape, B7), t10.runWebGLProgram(n, [o], o.dtype); } -var nF = { kernelName: ws, backendName: "webgl", kernelFunc: S7 }; -var w7 = Lt.nonMaxSuppressionV3Impl; -function I7(r) { +var cD = { kernelName: vs, backendName: "webgl", kernelFunc: z7 }; +var W7 = Vt.nonMaxSuppressionV3Impl; +function U7(r) { S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t6.readSync(n.dataId), c = t6.readSync(s.dataId), { selectedIndices: l } = w7(u, c, a, i, p); - return t6.makeTensorInfo([l.length], "int32", new Int32Array(l)); + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = W7(u, c, a, i, p); + return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var sF = { kernelName: Tn, backendName: "webgl", kernelFunc: I7 }; -var v7 = Lt.nonMaxSuppressionV4Impl; -function k7(r) { +var lD = { kernelName: Tn, backendName: "webgl", kernelFunc: U7 }; +var G7 = Vt.nonMaxSuppressionV4Impl; +function H7(r) { S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t6.readSync(n.dataId), l = t6.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = v7(c, l, a, i, p, u); - return [t6.makeTensorInfo([m.length], "int32", new Int32Array(m)), t6.makeTensorInfo([], "int32", new Int32Array([d]))]; + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = G7(c, l, a, i, p, u); + return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var aF = { kernelName: ba, backendName: "webgl", kernelFunc: k7 }; -var N7 = Lt.nonMaxSuppressionV5Impl; -function T7(r) { +var mD = { kernelName: ba, backendName: "webgl", kernelFunc: H7 }; +var K7 = Vt.nonMaxSuppressionV5Impl; +function q7(r) { S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t6.readSync(n.dataId), l = t6.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = N7(c, l, m, d, f, h); - return [t6.makeTensorInfo([g.length], "int32", new Int32Array(g)), t6.makeTensorInfo([x.length], "float32", new Float32Array(x))]; + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = K7(c, l, m, d, f, h); + return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var iF = { kernelName: _n, backendName: "webgl", kernelFunc: T7 }; +var dD = { kernelName: _n, backendName: "webgl", kernelFunc: q7 }; var Zh = class { - constructor(e, t6, o, n) { - this.variableNames = ["indices"], this.outputShape = [e, t6], this.userCode = ` + constructor(e, t10, o, n) { + this.variableNames = ["indices"], this.outputShape = [e, t10], this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); @@ -22600,52 +22596,52 @@ var Zh = class { `; } }; -var _7 = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Zh(u, a, i, p), l = te({ inputs: { x: n }, backend: t6, attrs: { shape: [u] } }), m = t6.runWebGLProgram(c, [l], s); - t6.disposeIntermediateTensorInfo(l); - let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: t6, attrs: { shape: d } }); - return t6.disposeIntermediateTensorInfo(m), f; +var j7 = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Zh(u, a, i, p), l = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = t10.runWebGLProgram(c, [l], s); + t10.disposeIntermediateTensorInfo(l); + let d = [...n.shape, a], f = re({ inputs: { x: m }, backend: t10, attrs: { shape: d } }); + return t10.disposeIntermediateTensorInfo(m), f; }; -var uF = { kernelName: En, backendName: "webgl", kernelFunc: _7 }; -function Al(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var fD = { kernelName: $n, backendName: "webgl", kernelFunc: j7 }; +function Fl(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "complex64") { - let n = Ua({ inputs: { input: o }, backend: t6 }), s = Al({ inputs: { x: n }, backend: t6 }), a = Mu({ inputs: { input: o }, backend: t6 }), i = Al({ inputs: { x: a }, backend: t6 }), p = Rr({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(n), t6.disposeIntermediateTensorInfo(s), t6.disposeIntermediateTensorInfo(a), t6.disposeIntermediateTensorInfo(i), p; + let n = Ka({ inputs: { input: o }, backend: t10 }), s = Fl({ inputs: { x: n }, backend: t10 }), a = Wu({ inputs: { input: o }, backend: t10 }), i = Fl({ inputs: { x: a }, backend: t10 }), p = Ar({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; } else - return Ga({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t6 }); + return qa({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 }); } -var pF = { kernelName: Fs, backendName: "webgl", kernelFunc: Al }; -function cF(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var hD = { kernelName: Os, backendName: "webgl", kernelFunc: Fl }; +function gD(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = Ua({ inputs: { input: o }, backend: t6 }), s = cF({ inputs: { x: n }, backend: t6 }), a = Mu({ inputs: { input: o }, backend: t6 }), i = Al({ inputs: { x: a }, backend: t6 }), p = Rr({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeIntermediateTensorInfo(n), t6.disposeIntermediateTensorInfo(s), t6.disposeIntermediateTensorInfo(a), t6.disposeIntermediateTensorInfo(i), p; + let n = Ka({ inputs: { input: o }, backend: t10 }), s = gD({ inputs: { x: n }, backend: t10 }), a = Wu({ inputs: { input: o }, backend: t10 }), i = Fl({ inputs: { x: a }, backend: t10 }), p = Ar({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p; } else - return Ga({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t6 }); + return qa({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 }); } -var lF = { kernelName: Is, backendName: "webgl", kernelFunc: cF }; -function E7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o; +var xD = { kernelName: ks, backendName: "webgl", kernelFunc: gD }; +function X7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return Ph({ inputs: { input: e[0] }, backend: t6, attrs: { dim: n } }); + return Ph({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = Ph({ inputs: { input: c }, backend: t6, attrs: { dim: n } }); + let l = Ph({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); return i.push(l), l; - }), u = $w({ inputs: p, backend: t6, attrs: { axis: n } }); - return i.forEach((c) => t6.disposeIntermediateTensorInfo(c)), u; + }), u = zw({ inputs: p, backend: t10, attrs: { axis: n } }); + return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u; } -var mF = { kernelName: vs, backendName: "webgl", kernelFunc: E7 }; +var yD = { kernelName: Ns, backendName: "webgl", kernelFunc: X7 }; var Jh = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t6.map((u, c) => u[0] + e[c] + u[1]); - let n = e.length, s = _e(n), a = t6.map((u) => u[0]).join(","), i = t6.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n); + constructor(e, t10, o) { + this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((u, c) => u[0] + e[c] + u[1]); + let n = e.length, s = $e(n), a = t10.map((u) => u[0]).join(","), i = t10.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n); if (n === 1) { this.userCode = ` int start = ${a}; @@ -22679,9 +22675,9 @@ var Jh = class { } }; var eg = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t6.map((h, g) => h[0] + e[g] + h[1]); - let n = e.length, s = _e(n), a = t6.map((h) => h[0]).join(","), i = t6.map((h, g) => h[0] + e[g]).join(","), p = $t("rc", n), u = $t("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; + constructor(e, t10, o) { + this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((h, g) => h[0] + e[g] + h[1]); + let n = e.length, s = $e(n), a = t10.map((h) => h[0]).join(","), i = t10.map((h, g) => h[0] + e[g]).join(","), p = Et("rc", n), u = Et("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; if(${c}) { `, n === 1 ? "" : `} rc = outputLoc; @@ -22711,17 +22707,17 @@ var eg = class { `; } }; -var Lw = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; +var Xw = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return Ga({ backend: t6, attrs: { shape: u, value: a, dtype: n.dtype } }); + return qa({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } }); } let i = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new eg(n.shape, s, a) : new Jh(n.shape, s, a), p = [[a]]; - return t6.runWebGLProgram(i, [n], n.dtype, p); + return t10.runWebGLProgram(i, [n], n.dtype, p); }; -var dF = { kernelName: $n, backendName: "webgl", kernelFunc: Lw }; -var $7 = ` +var bD = { kernelName: En, backendName: "webgl", kernelFunc: Xw }; +var Y7 = ` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -22731,7 +22727,7 @@ var $7 = ` return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `; -var A7 = ` +var Q7 = ` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -22747,57 +22743,57 @@ var A7 = ` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - ` + Zs + ` + ` + Js + ` return result; `; -var R7 = tt({ opSnippet: $7, packedOpSnippet: A7 }); -var fF = { kernelName: An, backendName: "webgl", kernelFunc: R7 }; -function F7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = S.getAxesPermutation(c, i), m = n; - l != null && (m = xt({ inputs: { x: n }, backend: t6, attrs: { perm: l } }), c = S.getInnerMostAxes(c.length, i), p.push(m)), S.assertAxesAreInnerMostDims("prod", c, i); +var Z7 = tt({ opSnippet: Y7, packedOpSnippet: Q7 }); +var CD = { kernelName: An, backendName: "webgl", kernelFunc: Z7 }; +function J7(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = S.getAxesPermutation(c, i), m = n; + l != null && (m = yt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = S.getInnerMostAxes(c.length, i), p.push(m)), S.assertAxesAreInnerMostDims("prod", c, i); let d; - if (t6.shouldExecuteOnCPU([m])) { - let f = t6.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = o$(m.shape, m.dtype, f, c); - d = t6.makeTensorInfo(g, x, h); + if (t10.shouldExecuteOnCPU([m])) { + let f = t10.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = pE(m.shape, m.dtype, f, c); + d = t10.makeTensorInfo(g, x, h); } else { - let [f, h] = S.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: t6, attrs: { shape: [-1, g] } }), b = ka(n.dtype), C = Gr(x, b, "prod", t6); - d = te({ inputs: { x: C }, backend: t6, attrs: { shape: f } }), p.push(x), p.push(C); + let [f, h] = S.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = re({ inputs: { x: m }, backend: t10, attrs: { shape: [-1, g] } }), b = Ta(n.dtype), C = Ur(x, b, "prod", t10); + d = re({ inputs: { x: C }, backend: t10, attrs: { shape: f } }), p.push(x), p.push(C); } if (a) { p.push(d); let f = S.expandShapeToKeepDim(d.shape, u); - d = te({ inputs: { x: d }, backend: t6, attrs: { shape: f } }); + d = re({ inputs: { x: d }, backend: t10, attrs: { shape: f } }); } - return p.forEach((f) => t6.disposeIntermediateTensorInfo(f)), d; + return p.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d; } -var hF = { kernelName: Fn, backendName: "webgl", kernelFunc: F7 }; -function D7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t6.readSync(x.dataId)), u = n.map((x) => x.shape), c = t6.readSync(s.dataId), l = t6.readSync(a.dataId), [m, d, f] = n$(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t6.makeTensorInfo([x.length], "int32", x)), g = t6.makeTensorInfo(f, s.dtype, d); +var SD = { kernelName: Dn, backendName: "webgl", kernelFunc: J7 }; +function eZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.readSync(x.dataId)), u = n.map((x) => x.shape), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), [m, d, f] = cE(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var gF = { kernelName: wp, backendName: "webgl", kernelFunc: D7 }; -function O7(r) { - let { inputs: e, backend: t6 } = r, { starts: o, limits: n, deltas: s } = e, a = t6.readSync(o.dataId), i = t6.readSync(n.dataId), p = t6.readSync(s.dataId), [u, c] = s$(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t6.makeTensorInfo([u.length], "int32", u), m = t6.makeTensorInfo([c.length], o.dtype, c); +var wD = { kernelName: Ip, backendName: "webgl", kernelFunc: eZ }; +function tZ(r) { + let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = lE(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var xF = { kernelName: Ip, backendName: "webgl", kernelFunc: O7 }; -function P7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t6.readSync(n.dataId), c = t6.readSync(s.dataId), l = t6.readSync(a.dataId), m = i.map((g) => t6.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = a$(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); - return t6.makeTensorInfo(f, s.dtype, h); +var ID = { kernelName: vp, backendName: "webgl", kernelFunc: tZ }; +function rZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), m = i.map((g) => t10.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = mE(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); + return t10.makeTensorInfo(f, s.dtype, h); } -var yF = { kernelName: vp, backendName: "webgl", kernelFunc: P7 }; -var Bw = (r) => { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, step: s, dtype: a } = t6, i = i$(o, n, s, a); +var vD = { kernelName: kp, backendName: "webgl", kernelFunc: rZ }; +var Yw = (r) => { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = dE(o, n, s, a); return e.makeTensorInfo([i.length], a, i); }; -var bF = { kernelName: ks, backendName: "webgl", kernelFunc: Bw }; -var M7 = "return 1.0 / x;"; -var L7 = ge({ opSnippet: M7 }); -var CF = { kernelName: Dn, backendName: "webgl", kernelFunc: L7 }; -var B7 = Bt + ` +var kD = { kernelName: Ts, backendName: "webgl", kernelFunc: Yw }; +var oZ = "return 1.0 / x;"; +var nZ = he({ opSnippet: oZ }); +var ND = { kernelName: Fn, backendName: "webgl", kernelFunc: nZ }; +var sZ = zt + ` return (x < 0.0) ? 0.0 : x; `; -var V7 = ` +var aZ = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -22808,12 +22804,12 @@ var V7 = ` return result; `; -var z7 = ge({ opSnippet: B7, packedOpSnippet: V7 }); -var SF = { kernelName: On, backendName: "webgl", kernelFunc: z7 }; -var W7 = Bt + ` +var iZ = he({ opSnippet: sZ, packedOpSnippet: aZ }); +var TD = { kernelName: On, backendName: "webgl", kernelFunc: iZ }; +var uZ = zt + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; -var U7 = ` +var pZ = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -22824,14 +22820,14 @@ var U7 = ` return result; `; -var G7 = ge({ opSnippet: W7, packedOpSnippet: U7 }); -var wF = { kernelName: Ln, backendName: "webgl", kernelFunc: G7 }; +var cZ = he({ opSnippet: uZ, packedOpSnippet: pZ }); +var _D = { kernelName: Ln, backendName: "webgl", kernelFunc: cZ }; var tg = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t6, o, u]; - let c = [n && t6 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t6 > 1 ? t6 - 1 : t6, n && o > 1 ? o - 1 : o], m; + this.outputShape = [a, t10, o, u]; + let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m; s ? m = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : m = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0] / l[0]}, @@ -22869,11 +22865,11 @@ var tg = class { } }; var rg = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t6, o, u]; - let c = [n && t6 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t6 > 1 ? t6 - 1 : t6, n && o > 1 ? o - 1 : o], m; + this.outputShape = [a, t10, o, u]; + let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m; s ? m = "(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)" : m = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0] / l[0]}, @@ -22954,15 +22950,15 @@ var rg = class { `; } }; -function H7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new rg(n.shape, p, u, s, a) : new tg(n.shape, p, u, s, a); - return t6.runWebGLProgram(c, [n], "float32"); +function lZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new rg(n.shape, p, u, s, a) : new tg(n.shape, p, u, s, a); + return t10.runWebGLProgram(c, [n], "float32"); } -var IF = { kernelName: Mn, backendName: "webgl", kernelFunc: H7 }; +var $D = { kernelName: Mn, backendName: "webgl", kernelFunc: lZ }; var og = class { - constructor(e, t6, o) { - this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t6; - let [, n, s] = t6, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; + constructor(e, t10, o) { + this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10; + let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23047,17 +23043,17 @@ var og = class { `; } }; -function q7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new og(s.shape, n.shape, a); - return t6.runWebGLProgram(i, [s], s.dtype); +function mZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new og(s.shape, n.shape, a); + return t10.runWebGLProgram(i, [s], s.dtype); } -var vF = { kernelName: $m, backendName: "webgl", kernelFunc: q7 }; +var ED = { kernelName: Am, backendName: "webgl", kernelFunc: mZ }; var ng = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t6, o, u]; - let c = [n && t6 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t6 > 1 ? t6 - 1 : t6, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; + this.outputShape = [a, t10, o, u]; + let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; s ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0] / l[0]}, @@ -23084,11 +23080,11 @@ var ng = class { } }; var sg = class { - constructor(e, t6, o, n, s) { + constructor(e, t10, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = e; - this.outputShape = [a, t6, o, u]; - let c = [n && t6 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t6 > 1 ? t6 - 1 : t6, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; + this.outputShape = [a, t10, o, u]; + let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d; s ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0] / l[0]}, @@ -23133,15 +23129,15 @@ var sg = class { `; } }; -function K7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new sg(n.shape, p, u, s, a) : new ng(n.shape, p, u, s, a); - return t6.runWebGLProgram(c, [n], n.dtype); +function dZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new sg(n.shape, p, u, s, a) : new ng(n.shape, p, u, s, a); + return t10.runWebGLProgram(c, [n], n.dtype); } -var kF = { kernelName: Pn, backendName: "webgl", kernelFunc: K7 }; +var AD = { kernelName: Pn, backendName: "webgl", kernelFunc: dZ }; var ag = class { - constructor(e, t6, o) { - this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t6; - let [, n, s] = t6, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; + constructor(e, t10, o) { + this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10; + let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23215,13 +23211,13 @@ var ag = class { `; } }; -function j7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new ag(s.shape, n.shape, a); - return t6.runWebGLProgram(i, [s], s.dtype); +function fZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new ag(s.shape, n.shape, a); + return t10.runWebGLProgram(i, [s], s.dtype); } -var NF = { kernelName: Em, backendName: "webgl", kernelFunc: j7 }; +var RD = { kernelName: Em, backendName: "webgl", kernelFunc: fZ }; var ig = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["x"]; let o = e.length; if (o > 4) @@ -23235,7 +23231,7 @@ var ig = class { `; return; } - let n = (i) => t6.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = _e(o); + let n = (i) => t10.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = $e(o); this.userCode = ` void main() { ${a} coords = getOutputCoords(); @@ -23245,13 +23241,13 @@ var ig = class { } }; var ug = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true; let o = e.length; if (o > 4) throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`); this.outputShape = e; - let n = $t("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = _e(o); + let n = Et("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = $e(o); o === 1 ? this.userCode = ` void main(){ int rc = getOutputCoords(); @@ -23298,26 +23294,26 @@ var ug = class { return `getChannel(getX(${g}), vec2(${x}))`; } function d(f, h) { - return t6.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`; + return t10.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`; } } }; -function X7(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); +function hZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return At({ inputs: { x: n }, backend: t6 }); + return At({ inputs: { x: n }, backend: t10 }); let p = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ug(n.shape, i) : new ig(n.shape, i); - return t6.runWebGLProgram(p, [n], n.dtype); + return t10.runWebGLProgram(p, [n], n.dtype); } -var TF = { kernelName: Bn, backendName: "webgl", kernelFunc: X7 }; +var DD = { kernelName: Bn, backendName: "webgl", kernelFunc: hZ }; var pg = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }]; let o = e[1], n = e[2]; this.outputShape = e; let s = ""; - typeof t6 == "number" ? s = `float outputValue = ${t6.toFixed(2)};` : s = ` - vec3 fill = vec3(${t6.join(",")}); + typeof t10 == "number" ? s = `float outputValue = ${t10.toFixed(2)};` : s = ` + vec3 fill = vec3(${t10.join(",")}); float outputValue = fill[coords[3]];`, this.userCode = ` void main() { ivec4 coords = getOutputCoords(); @@ -23338,11 +23334,11 @@ var pg = class { `; } }; -var _F = { kernelName: es, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t6, p = new pg(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; +var FD = { kernelName: ts, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new pg(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; return i.runWebGLProgram(p, [o], o.dtype, l); } }; -var Y7 = ` +var gZ = ` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -23358,19 +23354,19 @@ var Y7 = ` } } `; -var Q7 = ge({ opSnippet: Y7 }); -var EF = { kernelName: Ca, backendName: "webgl", kernelFunc: Q7 }; -var Z7 = "return inversesqrt(x);"; -var J7 = ge({ opSnippet: Z7, cpuKernelImpl: u$ }); -var $F = { kernelName: Vn, backendName: "webgl", kernelFunc: J7 }; -var Cc = class { - constructor(e, t6, o, n, s, a, i = true) { +var xZ = he({ opSnippet: gZ }); +var OD = { kernelName: Vn, backendName: "webgl", kernelFunc: xZ }; +var yZ = "return inversesqrt(x);"; +var bZ = he({ opSnippet: yZ, cpuKernelImpl: fE }); +var PD = { kernelName: zn, backendName: "webgl", kernelFunc: bZ }; +var Sc = class { + constructor(e, t10, o, n, s, a, i = true) { this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a; - let p = _e(s.length), u = _e(a.length), c = ""; + let p = $e(s.length), u = $e(a.length), c = ""; o === 1 ? c = "i" : o === 2 && (c = "i, j"); let l = `getIndices(${c})`, m = ""; n === 1 ? m = "i" : n === 2 && (m = "i, coords[1]"); - let d = `getUpdates(${m})`, f = t6 > 1 ? "strides[j]" : "strides"; + let d = `getUpdates(${m})`, f = t10 > 1 ? "strides[j]" : "strides"; this.userCode = ` ${p} strides = ${p}(${s}); @@ -23380,7 +23376,7 @@ var Cc = class { bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; - for (int j = 0; j < ${t6}; j++) { + for (int j = 0; j < ${t10}; j++) { int index = round(${l}); flattenedIndex += index * ${f}; } @@ -23394,18 +23390,18 @@ var Cc = class { `; } }; -function eZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u]; +function CZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) - return t6.makeTensorInfo(a, n.dtype); - let d = te({ inputs: { x: n }, backend: t6, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: t6, attrs: { shape: [p, u] } }), h = t6.makeTensorInfo([], "float32", new Float32Array([0])), g = new Cc(p, i, d.shape.length, f.shape.length, c, m), x = t6.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: t6, attrs: { shape: a } }); - return t6.disposeIntermediateTensorInfo(d), t6.disposeIntermediateTensorInfo(f), t6.disposeIntermediateTensorInfo(x), t6.disposeIntermediateTensorInfo(h), b; + return t10.makeTensorInfo(a, n.dtype); + let d = re({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = re({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = t10.makeTensorInfo([], "float32", new Float32Array([0])), g = new Sc(p, i, d.shape.length, f.shape.length, c, m), x = t10.runWebGLProgram(g, [f, d, h], f.dtype), b = re({ inputs: { x }, backend: t10, attrs: { shape: a } }); + return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(h), b; } -var AF = { kernelName: zn, backendName: "webgl", kernelFunc: eZ }; +var MD = { kernelName: Wn, backendName: "webgl", kernelFunc: CZ }; var cg = class { - constructor(e, t6, o, n) { + constructor(e, t10, o, n) { this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, o]; - let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t6 + 1))}; ++i) { if (left >= right) break;`, i = O().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<="; + let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t10 + 1))}; ++i) { if (left >= right) break;`, i = O().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<="; this.userCode = ` int findBound(int batch, float value) { int left = 0; @@ -23434,14 +23430,14 @@ var cg = class { `; } }; -function tZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new cg(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; - return t6.runWebGLProgram(i, [n, s], "int32", p); +function SZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new cg(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; + return t10.runWebGLProgram(i, [n, s], "int32", p); } -var RF = { kernelName: ii, backendName: "webgl", kernelFunc: tZ }; +var LD = { kernelName: fi, backendName: "webgl", kernelFunc: SZ }; var lg = class { - constructor(e, t6, o) { - this.variableNames = ["c", "a", "b"], this.outputShape = t6; + constructor(e, t10, o) { + this.variableNames = ["c", "a", "b"], this.outputShape = t10; let n, s; if (o > 4) throw Error(`Where for rank ${o} is not yet supported`); @@ -23449,11 +23445,11 @@ var lg = class { s = "resRC", n = "resRC"; else { let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], p = [], u = []; - for (let c = 0; c < t6.length; c++) + for (let c = 0; c < t10.length; c++) u.push(`${i[c]}`), c < e && p.push(`${i[c]}`); n = p.join(), s = u.join(); } - let a = _e(o); + let a = $e(o); this.userCode = ` void main() { ${a} resRC = getOutputCoords(); @@ -23467,24 +23463,24 @@ var lg = class { `; } }; -function rZ(r) { - let { inputs: e, backend: t6 } = r, { condition: o, t: n, e: s } = e, a = new lg(o.shape.length, n.shape, n.shape.length); - return t6.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype)); +function wZ(r) { + let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new lg(o.shape.length, n.shape, n.shape.length); + return t10.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var FF = { kernelName: Ts, backendName: "webgl", kernelFunc: rZ }; -var oZ = ` +var BD = { kernelName: $s, backendName: "webgl", kernelFunc: wZ }; +var IZ = ` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${S.SELU_SCALEALPHA}; float scale = ${S.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `; -var nZ = ge({ opSnippet: oZ }); -var DF = { kernelName: Xi, backendName: "webgl", kernelFunc: nZ }; -var sZ = _o + ` +var vZ = he({ opSnippet: IZ }); +var VD = { kernelName: Ca, backendName: "webgl", kernelFunc: vZ }; +var kZ = $o + ` return 1.0 / (1.0 + exp(-1.0 * x)); `; -var aZ = ` +var NZ = ` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -23495,26 +23491,26 @@ var aZ = ` return result; `; -var iZ = ge({ opSnippet: sZ, packedOpSnippet: aZ, cpuKernelImpl: c$ }); -var OF = { kernelName: Un, backendName: "webgl", kernelFunc: iZ }; -var uZ = ` +var TZ = he({ opSnippet: kZ, packedOpSnippet: NZ, cpuKernelImpl: gE }); +var zD = { kernelName: Gn, backendName: "webgl", kernelFunc: TZ }; +var _Z = ` if (isnan(x)) { return 0.0; } return sign(x); `; -var pZ = ge({ opSnippet: uZ }); -var PF = { kernelName: Yi, backendName: "webgl", kernelFunc: pZ }; -var cZ = _o + ` +var $Z = he({ opSnippet: _Z }); +var WD = { kernelName: wa, backendName: "webgl", kernelFunc: $Z }; +var EZ = $o + ` return sin(x); `; -var lZ = ge({ opSnippet: cZ }); -var MF = { kernelName: Wn, backendName: "webgl", kernelFunc: lZ }; -var mZ = ` +var AZ = he({ opSnippet: EZ }); +var UD = { kernelName: Un, backendName: "webgl", kernelFunc: AZ }; +var RZ = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `; -var dZ = ge({ opSnippet: mZ }); -var LF = { kernelName: Sa, backendName: "webgl", kernelFunc: dZ }; -var fZ = ` +var DZ = he({ opSnippet: RZ }); +var GD = { kernelName: Sa, backendName: "webgl", kernelFunc: DZ }; +var FZ = ` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -23535,21 +23531,21 @@ var fZ = ` } return result; `; -var hZ = ge({ opSnippet: fZ }); -var BF = { kernelName: Qi, backendName: "webgl", kernelFunc: hZ }; -var gZ = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; +var OZ = he({ opSnippet: FZ }); +var HD = { kernelName: Ia, backendName: "webgl", kernelFunc: OZ }; +var PZ = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); let i = s.reduce((x, b) => x * b), p = [[0, 0]]; p.push(...a); for (let x = 1 + s.length; x < n.shape.length; ++x) p.push([0, 0]); - let u = [], c = Lw({ inputs: { x: n }, backend: t6, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: t6, attrs: { shape: l } }), h = xt({ inputs: { x: f }, backend: t6, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: t6, attrs: { shape: d } }); - return u.push(c), u.push(f), u.push(h), u.forEach((x) => t6.disposeIntermediateTensorInfo(x)), g; + let u = [], c = Xw({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = re({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = yt({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: d } }); + return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeIntermediateTensorInfo(x)), g; }; -var VF = { kernelName: Es, backendName: "webgl", kernelFunc: gZ }; -function xZ(r) { - let { inputs: e, backend: t6 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; +var KD = { kernelName: As, backendName: "webgl", kernelFunc: PZ }; +function MZ(r) { + let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -23562,24 +23558,24 @@ function xZ(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = t6.readSync(o.dataId), p = t6.readSync(n.dataId), u = t6.readSync(s.dataId), c = t6.readSync(a.dataId)[0], [l, m, d, f, h] = m$(i, o.shape, o.dtype, p, n.dtype, u, c); - return [t6.makeTensorInfo(m, o.dtype, l), t6.makeTensorInfo([m[0]], n.dtype, d), t6.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t6.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; + let i = t10.readSync(o.dataId), p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = t10.readSync(a.dataId)[0], [l, m, d, f, h] = yE(i, o.shape, o.dtype, p, n.dtype, u, c); + return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var zF = { kernelName: ui, backendName: "webgl", kernelFunc: xZ }; -function yZ(r) { - let { inputs: e, backend: t6 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; +var qD = { kernelName: hi, backendName: "webgl", kernelFunc: MZ }; +function LZ(r) { + let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); if (n.shape.length !== 1) throw new Error(`Input shape should be a vector but received shape ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(t6.readSync(n.dataId)), i = t6.readSync(o.dataId), p = Array.from(t6.readSync(s.dataId)), [u, c, l] = d$(i, o.shape, o.dtype, a, p); - return [t6.makeTensorInfo(c, o.dtype, u), t6.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; + let a = Array.from(t10.readSync(n.dataId)), i = t10.readSync(o.dataId), p = Array.from(t10.readSync(s.dataId)), [u, c, l] = bE(i, o.shape, o.dtype, a, p); + return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var WF = { kernelName: wa, backendName: "webgl", kernelFunc: yZ }; -function bZ(r) { - let { inputs: e, backend: t6 } = r, { data: o, indices: n, segmentIds: s } = e; +var jD = { kernelName: va, backendName: "webgl", kernelFunc: LZ }; +function BZ(r) { + let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -23588,12 +23584,12 @@ function bZ(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = t6.readSync(o.dataId), i = t6.readSync(n.dataId), p = t6.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p, true); - return t6.makeTensorInfo(c, o.dtype, u); + let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p, true); + return t10.makeTensorInfo(c, o.dtype, u); } -var UF = { kernelName: pi, backendName: "webgl", kernelFunc: bZ }; -function CZ(r) { - let { inputs: e, backend: t6 } = r, { data: o, indices: n, segmentIds: s } = e; +var XD = { kernelName: gi, backendName: "webgl", kernelFunc: BZ }; +function VZ(r) { + let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -23602,50 +23598,50 @@ function CZ(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = t6.readSync(o.dataId), i = t6.readSync(n.dataId), p = t6.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p); - return t6.makeTensorInfo(c, o.dtype, u); + let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p); + return t10.makeTensorInfo(c, o.dtype, u); } -var GF = { kernelName: ci, backendName: "webgl", kernelFunc: CZ }; -function SZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false; +var YD = { kernelName: xi, backendName: "webgl", kernelFunc: VZ }; +function zZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let x = t6.bufferSync(n), b = t6.bufferSync(s), C = y.decodeString(t6.readSync(a.dataId)[0]), w = p$(x, b, i, m, c, u, p, l, C, d); - return t6.makeTensorInfo(i, w.dtype, w.values); + let x = t10.bufferSync(n), b = t10.bufferSync(s), C = y.decodeString(t10.readSync(a.dataId)[0]), w = hE(x, b, i, m, c, u, p, l, C, d); + return t10.makeTensorInfo(i, w.dtype, w.values); } - let f = new Cc(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t6.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: t6, attrs: { shape: i } }); - return t6.disposeIntermediateTensorInfo(h), g; + let f = new Sc(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t10.runWebGLProgram(f, [s, n, a], s.dtype), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: i } }); + return t10.disposeIntermediateTensorInfo(h), g; } -var HF = { kernelName: li, backendName: "webgl", kernelFunc: SZ }; -function wZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var QD = { kernelName: yi, backendName: "webgl", kernelFunc: zZ }; +function WZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; - let f = cs({ inputs: { x: n }, backend: t6, attrs: { begin: c, size: d } }); + let f = ls({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } }); return c[i] += m, f; }); } -var qF = { kernelName: $s, backendName: "webgl", kernelFunc: wZ }; -var KF = "return sqrt(x);"; -var IZ = ge({ opSnippet: KF, packedOpSnippet: KF, cpuKernelImpl: f$ }); -var jF = { kernelName: Gn, backendName: "webgl", kernelFunc: IZ }; -var vZ = "return x * x;"; -var kZ = ge({ opSnippet: vZ }); -var XF = { kernelName: mi, backendName: "webgl", kernelFunc: kZ }; -var YF = "return (a - b) * (a - b);"; -var NZ = tt({ opSnippet: YF, packedOpSnippet: YF }); -var QF = { kernelName: Kn, backendName: "webgl", kernelFunc: NZ }; -function TZ({ inputs: r, attrs: e, backend: t6 }) { - let { x: o } = r, n = Bt + ` +var ZD = { kernelName: Rs, backendName: "webgl", kernelFunc: WZ }; +var JD = "return sqrt(x);"; +var UZ = he({ opSnippet: JD, packedOpSnippet: JD, cpuKernelImpl: CE }); +var eF = { kernelName: Hn, backendName: "webgl", kernelFunc: UZ }; +var GZ = "return x * x;"; +var HZ = he({ opSnippet: GZ }); +var tF = { kernelName: bi, backendName: "webgl", kernelFunc: HZ }; +var rF = "return (a - b) * (a - b);"; +var KZ = tt({ opSnippet: rF, packedOpSnippet: rF }); +var oF = { kernelName: jn, backendName: "webgl", kernelFunc: KZ }; +function qZ({ inputs: r, attrs: e, backend: t10 }) { + let { x: o } = r, n = zt + ` return x > 0.0 ? 1.0 : float(${e.alpha}); `, s = new Jt(o.shape, n); - return t6.runWebGLProgram(s, [o], o.dtype); + return t10.runWebGLProgram(s, [o], o.dtype); } -var ZF = { kernelName: Ds, backendName: "webgl", kernelFunc: TZ }; +var nF = { kernelName: fo, backendName: "webgl", kernelFunc: qZ }; var mg = class { - constructor(e, t6, o) { + constructor(e, t10, o) { this.variableNames = ["x"], this.outputShape = o; - let n = o.length, s = _e(o.length), a = _e(o.length), i = ""; + let n = o.length, s = $e(o.length), a = $e(o.length), i = ""; if (n === 1) i = "coords * strides + begin"; else { @@ -23654,7 +23650,7 @@ var mg = class { } this.userCode = ` ${s} begin = ${s}(${e}); - ${s} strides = ${s}(${t6}); + ${s} strides = ${s}(${t10}); void main() { ${a} coords = getOutputCoords(); @@ -23663,69 +23659,69 @@ var mg = class { `; } }; -function _Z(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function jZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = te({ inputs: { x: n }, backend: t6, attrs: { shape: f } }); + k = re({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let $ = ut.computeOutShape(b, C, w), A = cs({ inputs: { x: n }, backend: t6, attrs: { begin: b, size: $ } }); - k = te({ inputs: { x: A }, backend: t6, attrs: { shape: f } }), t6.disposeIntermediateTensorInfo(A); - } else if (t6.shouldExecuteOnCPU([n])) { - let A = t6.readSync(n.dataId), R = le(n.shape, n.dtype, A), D = h$(d, R, w, b); - k = t6.makeTensorInfo(f, n.dtype, D.values); + let E = ut.computeOutShape(b, C, w), A = ls({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: E } }); + k = re({ inputs: { x: A }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(A); + } else if (t10.shouldExecuteOnCPU([n])) { + let A = t10.readSync(n.dataId), R = le(n.shape, n.dtype, A), D = SE(d, R, w, b); + k = t10.makeTensorInfo(f, n.dtype, D.values); } else { let A = new mg(b, w, d); - k = t6.runWebGLProgram(A, [n], n.dtype); + k = t10.runWebGLProgram(A, [n], n.dtype); } - let _ = te({ inputs: { x: k }, backend: t6, attrs: { shape: f } }); - return t6.disposeIntermediateTensorInfo(k), _; + let _ = re({ inputs: { x: k }, backend: t10, attrs: { shape: f } }); + return t10.disposeIntermediateTensorInfo(k), _; } -var JF = { kernelName: jn, backendName: "webgl", kernelFunc: _Z }; -function EZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t6.readSync(c.dataId), d = t6.readSync(l.dataId), [f, h] = g$(m, d, n, s, a, i, p, u); - return [t6.makeTensorInfo([f.length], "string", f), t6.makeTensorInfo(l.shape, "int32", h)]; +var sF = { kernelName: Xn, backendName: "webgl", kernelFunc: jZ }; +function XZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = wE(m, d, n, s, a, i, p, u); + return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; } -var eD = { kernelName: As, backendName: "webgl", kernelFunc: EZ }; -function $Z(r) { - let { inputs: e, backend: t6, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; +var aF = { kernelName: Ds, backendName: "webgl", kernelFunc: XZ }; +function YZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = t6.readSync(s.dataId), p = t6.readSync(a.dataId)[0], [u, c, l] = x$(i, p, n), m = c.length; - return [t6.makeTensorInfo([m, 2], "int32", u), t6.makeTensorInfo([m], "string", c), t6.makeTensorInfo([2], "int32", new Int32Array(l))]; + let i = t10.readSync(s.dataId), p = t10.readSync(a.dataId)[0], [u, c, l] = IE(i, p, n), m = c.length; + return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var tD = { kernelName: di, backendName: "webgl", kernelFunc: $Z }; -function AZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; +var iF = { kernelName: Ci, backendName: "webgl", kernelFunc: YZ }; +function QZ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = t6.readSync(s.dataId), i = y$(a, n); - return t6.makeTensorInfo(s.shape, "int32", i); + let a = t10.readSync(s.dataId), i = vE(a, n); + return t10.makeTensorInfo(s.shape, "int32", i); } -var rD = { kernelName: fi, backendName: "webgl", kernelFunc: AZ }; -var RZ = "return tan(x);"; -var FZ = ge({ opSnippet: RZ }); -var oD = { kernelName: Yn, backendName: "webgl", kernelFunc: FZ }; -var DZ = ` +var uF = { kernelName: Si, backendName: "webgl", kernelFunc: QZ }; +var ZZ = "return tan(x);"; +var JZ = he({ opSnippet: ZZ }); +var pF = { kernelName: Qn, backendName: "webgl", kernelFunc: JZ }; +var e9 = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `; -var OZ = ge({ opSnippet: DZ }); -var nD = { kernelName: Qn, backendName: "webgl", kernelFunc: OZ }; +var t9 = he({ opSnippet: e9 }); +var cF = { kernelName: Zn, backendName: "webgl", kernelFunc: t9 }; var dg = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["A"]; let o = new Array(e.length); for (let a = 0; a < o.length; a++) - o[a] = e[a] * t6[a]; + o[a] = e[a] * t10[a]; this.outputShape = o, this.rank = o.length; - let n = _e(this.rank), s = PZ(e); + let n = $e(this.rank), s = r9(e); this.userCode = ` void main() { ${n} resRC = getOutputCoords(); @@ -23734,27 +23730,27 @@ var dg = class { `; } }; -function PZ(r) { +function r9(r) { let e = r.length; if (e > 5) throw Error(`Tile for rank ${e} is not yet supported`); if (e === 1) return `imod(resRC, ${r[0]})`; - let t6 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = []; + let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = []; for (let n = 0; n < r.length; n++) - o.push(`imod(${t6[n]}, ${r[n]})`); + o.push(`imod(${t10[n]}, ${r[n]})`); return o.join(); } -function Vw(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reps: s } = o; +function Qw(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; if (n.dtype === "string" || n.shape.length > 5) { - let p = t6.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = C$(c, s); - return t6.makeTensorInfo(l.shape, l.dtype, l.values); + let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = NE(c, s); + return t10.makeTensorInfo(l.shape, l.dtype, l.values); } let a = new dg(n.shape, s); - return t6.runWebGLProgram(a, [n], n.dtype); + return t10.runWebGLProgram(a, [n], n.dtype); } -var sD = { kernelName: to, backendName: "webgl", kernelFunc: Vw }; +var lF = { kernelName: to, backendName: "webgl", kernelFunc: Qw }; var fg = class { constructor(e) { this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "negativeInf", type: "float" }, { name: "dir", type: "int" }, { name: "inc", type: "int" }], this.outputShape = e, this.userCode = ` @@ -23839,30 +23835,30 @@ var hg = class { `; } }; -function Bu(r, e) { +function Gu(r, e) { e !== null && r.disposeIntermediateTensorInfo(e); } -function aD(r) { +function mF(r) { let e = 1; for (; e < r; ) e *= 2; return e; } -function MZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = O().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = O().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; - if (t6.shouldExecuteOnCPU([n]) || c < i || s > p) { - let D = t6.readSync(n.dataId), [P, M] = S$(D, u, n.dtype, s, a); - return [t6.makeTensorInfo(P.shape, P.dtype, P.values), t6.makeTensorInfo(M.shape, M.dtype, M.values)]; +function o9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = O().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = O().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; + if (t10.shouldExecuteOnCPU([n]) || c < i || s > p) { + let D = t10.readSync(n.dataId), [P, M] = TE(D, u, n.dtype, s, a); + return [t10.makeTensorInfo(P.shape, P.dtype, P.values), t10.makeTensorInfo(M.shape, M.dtype, M.values)]; } if (s === 0) - return u[u.length - 1] = 0, [t6.makeTensorInfo(u, n.dtype, []), t6.makeTensorInfo(u, "int32", [])]; + return u[u.length - 1] = 0, [t10.makeTensorInfo(u, n.dtype, []), t10.makeTensorInfo(u, "int32", [])]; if (c === 1) - return [n, Ga({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t6 })]; - let l = t6.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t6.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t6 }); - m && Bu(t6, d); - let x = aD(s), b = aD(c), C = null, w = () => C === null ? [g, g] : [g, C], k = (D, P, M) => { - let L = w(), W = new fg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [D], [P]], q = C; - C = t6.runWebGLProgram(W, L, "int32", U), Bu(t6, q); + return [n, qa({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t10 })]; + let l = t10.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t10.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = re({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t10 }); + m && Gu(t10, d); + let x = mF(s), b = mF(c), C = null, w = () => C === null ? [g, g] : [g, C], k = (D, P, M) => { + let L = w(), V = new fg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [D], [P]], K = C; + C = t10.runWebGLProgram(V, L, "int32", U), Gu(t10, K); }; for (let D = 1; D < x; D *= 2) { let P = D * 2; @@ -23870,24 +23866,24 @@ function MZ(r) { k(P, M, [h, b]); } for (let D = b; D > x; D /= 2) { - let P = w(), M = new hg([h, D / 2]), W = [[c], [C === null ? 1 : 0], [x]], V = C; - C = t6.runWebGLProgram(M, P, "int32", W), Bu(t6, V); - let U = x / 2, q = U * 2; + let P = w(), M = new hg([h, D / 2]), V = [[c], [C === null ? 1 : 0], [x]], z = C; + C = t10.runWebGLProgram(M, P, "int32", V), Gu(t10, z); + let U = x / 2, K = U * 2; for (let H = U; H >= 1; H /= 2) - k(q, H, C.shape); + k(K, H, C.shape); } let _ = C; - C = cs({ inputs: { x: C }, backend: t6, attrs: { begin: 0, size: [h, s] } }), Bu(t6, _); - let $ = Fw({ inputs: { x: g, indices: C }, backend: t6, attrs: { axis: 1, batchDims: 1 } }); - Bu(t6, g); + C = ls({ inputs: { x: C }, backend: t10, attrs: { begin: 0, size: [h, s] } }), Gu(t10, _); + let E = Gw({ inputs: { x: g, indices: C }, backend: t10, attrs: { axis: 1, batchDims: 1 } }); + Gu(t10, g); let A = u.slice(0, -1); - A.push(s), _ = C, C = te({ inputs: { x: C }, attrs: { shape: A }, backend: t6 }), Bu(t6, _); - let R = $; - return $ = te({ inputs: { x: $ }, attrs: { shape: A }, backend: t6 }), Bu(t6, R), [$, C]; + A.push(s), _ = C, C = re({ inputs: { x: C }, attrs: { shape: A }, backend: t10 }), Gu(t10, _); + let R = E; + return E = re({ inputs: { x: E }, attrs: { shape: A }, backend: t10 }), Gu(t10, R), [E, C]; } -var iD = { kernelName: Zn, backendName: "webgl", kernelFunc: MZ }; +var dF = { kernelName: Jn, backendName: "webgl", kernelFunc: o9 }; var gg = class { - constructor(e, t6, o, n, s, a) { + constructor(e, t10, o, n, s, a) { this.variableNames = ["Image", "Transforms"], this.outputShape = a; let i = o === "nearest" ? 1 : 2, p; switch (n) { @@ -23961,7 +23957,7 @@ var gg = class { float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; - if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t6}) { + if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t10}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${s}); @@ -23992,7 +23988,7 @@ var gg = class { } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; - float mapX = mapCoord(inX, float(${t6})); + float mapX = mapCoord(inX, float(${t10})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { @@ -24022,20 +24018,20 @@ var gg = class { `; } }; -function LZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new gg(l, m, a, i, p, g); - return t6.runWebGLProgram(x, [n, s], "float32"); +function n9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new gg(l, m, a, i, p, g); + return t10.runWebGLProgram(x, [n, s], "float32"); } -var uD = { kernelName: Jn, backendName: "webgl", kernelFunc: LZ }; -function BZ(r) { - let { inputs: e, attrs: t6, backend: o } = r, { axis: n } = t6, { x: s } = e; - is(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); - let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = w$(a, n, s.shape, s.dtype); +var fF = { kernelName: es, backendName: "webgl", kernelFunc: n9 }; +function s9(r) { + let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e; + us(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); + let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = _E(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var pD = { kernelName: kp, backendName: "webgl", kernelFunc: BZ }; -function VZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { value: n } = e, { axis: s } = o; +var hF = { kernelName: Np, backendName: "webgl", kernelFunc: s9 }; +function a9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -24045,14 +24041,14 @@ function VZ(r) { let f = new Array(p); for (let h = 0; h < f.length; h++) { m[s] = h; - let g = cs({ inputs: { x: a }, backend: t6, attrs: { begin: m, size: d } }), x = te({ inputs: { x: g }, backend: t6, attrs: { shape: u } }); + let g = ls({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = re({ inputs: { x: g }, backend: t10, attrs: { shape: u } }); f[h] = x, l.push(g); } - return l.forEach((h) => t6.disposeIntermediateTensorInfo(h)), f; + return l.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f; } -var cD = { kernelName: Rs, backendName: "webgl", kernelFunc: VZ }; +var gF = { kernelName: Fs, backendName: "webgl", kernelFunc: a9 }; var xg = class { - constructor(e, t6) { + constructor(e, t10) { this.variableNames = ["x", "segmentIds"]; let o = e.windowSize, n = e.batchSize, s = e.inSize, a = e.numSegments, i = a * Math.ceil(s / o); this.outputShape = [n, i]; @@ -24168,78 +24164,78 @@ var xg = class { `; } }; -function zZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = S.getAxesPermutation([u], i), l = n; - c != null && (l = xt({ inputs: { x: n }, backend: t6, attrs: { perm: c } }), p.push(l), u = S.getInnerMostAxes(1, i)[0]); - let m = S.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: t6, attrs: { shape: [-1, d] } }); +function i9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = S.getAxesPermutation([u], i), l = n; + c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = S.getInnerMostAxes(1, i)[0]); + let m = S.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }); p.push(f); - let h = ka(n.dtype), g = (w, k, _, $, A) => { - let R = w.shape[0], D = w.shape[1], P = S.segment_util.segOpComputeOptimalWindowSize(D, A), M = { windowSize: P, inSize: D, batchSize: R, numSegments: A }, L = new xg(M, k), W = t6.compileAndRun(L, [w, _], $); - if (p.push(W), W.shape[1] === A) - return W; - let V = Bw({ backend: t6, attrs: { start: 0, stop: A, step: 1, dtype: "float32" } }), U = Vw({ inputs: { x: V }, backend: t6, attrs: { reps: [D / P] } }); - return p.push(V), p.push(U), g(W, k, U, $, A); - }, x = g(f, "unsortedSegmentSum", s, h, a), b = te({ inputs: { x }, backend: t6, attrs: { shape: m } }), C = b; + let h = Ta(n.dtype), g = (w, k, _, E, A) => { + let R = w.shape[0], D = w.shape[1], P = S.segment_util.segOpComputeOptimalWindowSize(D, A), M = { windowSize: P, inSize: D, batchSize: R, numSegments: A }, L = new xg(M, k), V = t10.compileAndRun(L, [w, _], E); + if (p.push(V), V.shape[1] === A) + return V; + let z = Yw({ backend: t10, attrs: { start: 0, stop: A, step: 1, dtype: "float32" } }), U = Qw({ inputs: { x: z }, backend: t10, attrs: { reps: [D / P] } }); + return p.push(z), p.push(U), g(V, k, U, E, A); + }, x = g(f, "unsortedSegmentSum", s, h, a), b = re({ inputs: { x }, backend: t10, attrs: { shape: m } }), C = b; if (c != null) { p.push(b); let w = S.getUndoAxesPermutation(c); - C = xt({ inputs: { x: C }, backend: t6, attrs: { perm: w } }); + C = yt({ inputs: { x: C }, backend: t10, attrs: { perm: w } }); } - return p.forEach((w) => t6.disposeIntermediateTensorInfo(w)), C; + return p.forEach((w) => t10.disposeIntermediateTensorInfo(w)), C; } -var lD = { kernelName: Np, backendName: "webgl", kernelFunc: zZ }; -var WZ = [Y$, Z$, J$, eA, rA, oA, nA, sA, uA, pA, cA, lA, mA, dA, fA, hA, gA, xA, yA, bA, CA, wA, IA, vA, _A, $A, AA, V$, FA, OA, PA, MA, LA, BA, VA, zA, WA, UA, GA, KA, jA, XA, YA, QA, ZA, JA, eR, tR, rR, oR, nR, sR, aR, iR, uR, cR, lR, mR, dR, hR, gR, xR, yR, bR, CR, SR, wR, IR, B$, vR, DA, kR, NR, TR, z$, _R, ER, $R, AR, RR, FR, DR, OR, PR, MR, BR, VR, zR, WR, UR, GR, qR, jR, XR, YR, QR, ZR, oF, G$, nF, sF, aF, iF, kA, uF, lF, mF, dF, fF, W$, hF, gF, xF, yF, bF, NA, JR, CF, SF, wF, q$, IF, vF, kF, NF, TF, _F, EF, $F, AF, RF, FF, DF, OF, PF, MF, LF, SA, rF, BF, VF, zF, WF, UF, GF, HF, qF, jF, XF, QF, ZF, JF, eD, tD, rD, tF, j$, oD, nD, sD, iD, uD, X$, pD, cD, lD, pF]; -for (let r of WZ) - Ia(r); +var xF = { kernelName: Tp, backendName: "webgl", kernelFunc: i9 }; +var u9 = [rA, nA, sA, aA, uA, pA, cA, lA, fA, hA, gA, xA, yA, bA, CA, SA, wA, IA, vA, kA, NA, _A, $A, EA, FA, PA, MA, KE, BA, zA, WA, UA, GA, HA, KA, qA, jA, XA, YA, JA, eR, tR, rR, oR, nR, sR, aR, iR, uR, pR, cR, lR, mR, dR, fR, gR, xR, yR, bR, SR, wR, IR, vR, kR, NR, TR, _R, $R, HE, ER, VA, AR, RR, DR, qE, FR, OR, PR, MR, LR, BR, VR, zR, WR, UR, HR, KR, qR, jR, XR, YR, ZR, eD, tD, rD, oD, nD, pD, YE, cD, lD, mD, dD, AA, fD, xD, yD, bD, CD, jE, SD, wD, ID, vD, kD, RA, sD, ND, TD, _D, ZE, $D, ED, AD, RD, DD, FD, OD, PD, MD, LD, BD, VD, zD, WD, UD, GD, TA, uD, HD, KD, qD, jD, XD, YD, QD, ZD, eF, tF, oF, nF, sF, aF, iF, uF, iD, eA, pF, cF, lF, dF, fF, tA, hF, gF, xF, hD]; +for (let r of u9) + ka(r); var Fe; (function(r) { r[r.float32 = 0] = "float32", r[r.int32 = 1] = "int32", r[r.bool = 2] = "bool", r[r.string = 3] = "string", r[r.complex64 = 4] = "complex64"; })(Fe || (Fe = {})); -var Wi; +var Ui; (function(r) { r[r.linear = 0] = "linear", r[r.relu = 1] = "relu", r[r.relu6 = 2] = "relu6", r[r.prelu = 3] = "prelu", r[r.leakyrelu = 4] = "leakyrelu", r[r.sigmoid = 5] = "sigmoid", r[r.elu = 6] = "elu"; -})(Wi || (Wi = {})); -var mD; -function UZ(r) { - mD = r.wasm.cwrap(fo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); +})(Ui || (Ui = {})); +var yF; +function p9(r) { + yF = r.wasm.cwrap(ho, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function GZ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e; +function c9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("_FusedMatMul for non non-float32 tensors not yet supported."); - let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t6.dataIdMap.get(n.dataId).id, d = t6.dataIdMap.get(s.dataId).id, f = 0; + let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = 0; if (a != null) { - let A = t6.dataIdMap.get(a.dataId); + let A = t10.dataIdMap.get(a.dataId); if (A.shape.length !== 1) throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`); f = A.id; } - let h = i == null ? 0 : t6.dataIdMap.get(i.dataId).id, g = Wi[c]; + let h = i == null ? 0 : t10.dataIdMap.get(i.dataId).id, g = Ui[c]; if (g == null) throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`); - let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), w = t6.makeOutput([...C, x, b], n.dtype), k = t6.dataIdMap.get(w.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), $ = new Uint8Array(new Int32Array(s.shape).buffer); - return mD(m, _, n.shape.length, d, $, s.shape.length, p, u, g, f, h, l || 0, k), w; + let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), w = t10.makeOutput([...C, x, b], n.dtype), k = t10.dataIdMap.get(w.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer); + return yF(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), w; } -var dD = { kernelName: fo, backendName: "wasm", setupFunc: UZ, kernelFunc: GZ }; +var bF = { kernelName: ho, backendName: "wasm", setupFunc: p9, kernelFunc: c9 }; function Ve(r, e) { - let t6; + let t10; function o(s) { - t6 = s.wasm.cwrap(r, null, ["number", "number", "number"]); + t10 = s.wasm.cwrap(r, null, ["number", "number", "number"]); } function n(s) { let { backend: a, inputs: { x: i } } = s, p = a.dataIdMap.get(i.dataId).id, u = a.makeOutput(i.shape, e || i.dtype), c = a.dataIdMap.get(u.dataId).id; - return y.sizeFromShape(u.shape) === 0 || t6(p, Fe[i.dtype], c), u; + return y.sizeFromShape(u.shape) === 0 || t10(p, Fe[i.dtype], c), u; } return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n }; } -var fD = Ve(gs); -function rt(r, e, t6) { +var CF = Ve(ys); +function rt(r, e, t10) { let o; function n(a) { o = a.wasm.cwrap(r, null, ["number", "array", "number", "number", "array", "number", "number", "number"]); } function s(a) { - let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t6 != null ? t6 : u.dtype, f = S.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d); + let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t10 != null ? t10 : u.dtype, f = S.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d); if (y.sizeFromShape(f) === 0) return h; let g = new Uint8Array(new Int32Array(u.shape).buffer), x = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id; @@ -24247,81 +24243,81 @@ function rt(r, e, t6) { } return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s }; } -var HZ = true; -var hD = rt(eo, HZ); -var gD; -function qZ(r) { - gD = r.wasm.cwrap(Mo, null, ["array", "number", "number", "number"]); +var l9 = true; +var SF = rt(eo, l9); +var wF; +function m9(r) { + wF = r.wasm.cwrap(Po, null, ["array", "number", "number", "number"]); } -function KZ(r) { - let { inputs: e, backend: t6 } = r, o = t6.makeOutput(e[0].shape, e[0].dtype); +function d9(r) { + let { inputs: e, backend: t10 } = r, o = t10.makeOutput(e[0].shape, e[0].dtype); if (y.sizeFromShape(o.shape) === 0) return o; - let n = e.map((i) => t6.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t6.dataIdMap.get(o.dataId).id; - return gD(s, n.length, Fe[o.dtype], a), o; + let n = e.map((i) => t10.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t10.dataIdMap.get(o.dataId).id; + return wF(s, n.length, Fe[o.dtype], a), o; } -var xD = { kernelName: Mo, backendName: "wasm", setupFunc: qZ, kernelFunc: KZ }; -function Vu(r) { - let { inputs: { x: e }, backend: t6 } = r; +var IF = { kernelName: Po, backendName: "wasm", setupFunc: m9, kernelFunc: d9 }; +function Hu(r) { + let { inputs: { x: e }, backend: t10 } = r; if (e.dtype === "string") - return nr(t6.readSync(e.dataId), e.shape, e.dtype); - let o = t6.makeOutput(e.shape, e.dtype), n = t6.typedArrayFromHeap(e); - return t6.typedArrayFromHeap(o).set(n), o; + return nr(t10.readSync(e.dataId), e.shape, e.dtype); + let o = t10.makeOutput(e.shape, e.dtype), n = t10.typedArrayFromHeap(e); + return t10.typedArrayFromHeap(o).set(n), o; } -var yD = { kernelName: mo, backendName: "wasm", kernelFunc: Vu }; -var bD; -function jZ(r) { - bD = r.wasm.cwrap(ro, null, ["number", "array", "number", "number", "number", "array", "number"]); +var vF = { kernelName: mo, backendName: "wasm", kernelFunc: Hu }; +var kF; +function f9(r) { + kF = r.wasm.cwrap(ro, null, ["number", "array", "number", "number", "number", "array", "number"]); } function uo(r) { - let { inputs: e, backend: t6, attrs: o } = r, [n, s] = YZ(e.x.shape, o.perm), a = true; + let { inputs: e, backend: t10, attrs: o } = r, [n, s] = g9(e.x.shape, o.perm), a = true; for (let f = 0; f < s.length; f++) s[f] !== f && (a = false); - let i = XZ(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype }; + let i = h9(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype }; if (a) { - let f = Vu({ inputs: e, backend: t6 }); + let f = Hu({ inputs: e, backend: t10 }); return f.shape = i, f; } - let u = t6.makeOutput(i, p.dtype), c = t6.dataIdMap.get(p.dataId).id, l = t6.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer); - return bD(c, d, p.shape.length, Fe[p.dtype], l, m, s.length), u; + let u = t10.makeOutput(i, p.dtype), c = t10.dataIdMap.get(p.dataId).id, l = t10.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer); + return kF(c, d, p.shape.length, Fe[p.dtype], l, m, s.length), u; } -function XZ(r, e) { - let t6 = new Array(r.length); - for (let o = 0; o < t6.length; o++) - t6[o] = r[e[o]]; - return t6; +function h9(r, e) { + let t10 = new Array(r.length); + for (let o = 0; o < t10.length; o++) + t10[o] = r[e[o]]; + return t10; } -function YZ(r, e) { - let t6 = [], o = []; +function g9(r, e) { + let t10 = [], o = []; for (let n = 0; n < r.length; ++n) - r[n] !== 1 && t6.push(r[n]), r[e[n]] !== 1 && o.push(e[n]); + r[n] !== 1 && t10.push(r[n]), r[e[n]] !== 1 && o.push(e[n]); for (let n = 0; n < o.length; ++n) { let s = -1; for (let a = 0; a < o.length; ++a) o[a] >= n && (s === -1 || o[s] > o[a]) && (s = a); o[s] = n; } - return [t6, o]; + return [t10, o]; } -var CD = { kernelName: ro, backendName: "wasm", kernelFunc: uo, setupFunc: jZ }; -function kr(r, e, t6) { +var NF = { kernelName: ro, backendName: "wasm", kernelFunc: uo, setupFunc: f9 }; +function kr(r, e, t10) { let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = S.getAxesPermutation(a, n), p = null, u = false; if (i != null) { let c = new Array(n); for (let d = 0; d < c.length; d++) c[d] = o[i[d]]; - a = S.getInnerMostAxes(a.length, n), p = uo({ inputs: { x: r }, attrs: { perm: i }, backend: t6 }); - let l = t6.dataIdMap.get(r.dataId).id; - t6.dataIdMap.get(p.dataId).id !== l && (u = true); + a = S.getInnerMostAxes(a.length, n), p = uo({ inputs: { x: r }, attrs: { perm: i }, backend: t10 }); + let l = t10.dataIdMap.get(r.dataId).id; + t10.dataIdMap.get(p.dataId).id !== l && (u = true); } return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u }; } -var SD; -function QZ(r) { - SD = r.wasm.cwrap(Lo, null, ["number, number, number"]); +var TF; +function x9(r) { + TF = r.wasm.cwrap(Mo, null, ["number, number, number"]); } -function ZZ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); +function y9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); if (d) { let C = e.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -24331,7 +24327,7 @@ function ZZ(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - SD(p, x, C); + TF(p, x, C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -24339,13 +24335,13 @@ function ZZ(r) { } return b; } -var wD = { kernelName: Lo, backendName: "wasm", setupFunc: QZ, kernelFunc: ZZ }; -var ID; -function JZ(r) { - ID = r.wasm.cwrap(Bo, null, ["number, number, number"]); +var _F = { kernelName: Mo, backendName: "wasm", setupFunc: x9, kernelFunc: y9 }; +var $F; +function b9(r) { + $F = r.wasm.cwrap(Lo, null, ["number, number, number"]); } -function e9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); +function C9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); if (d) { let C = e.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -24355,7 +24351,7 @@ function e9(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - ID(p, x, C); + $F(p, x, C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -24363,146 +24359,146 @@ function e9(r) { } return b; } -var vD = { kernelName: Bo, backendName: "wasm", setupFunc: JZ, kernelFunc: e9 }; -var kD; -function t9(r) { - kD = r.wasm.cwrap(Vo, null, ["number", "number", "number", "number", "number"]); +var EF = { kernelName: Lo, backendName: "wasm", setupFunc: b9, kernelFunc: C9 }; +var AF; +function S9(r) { + AF = r.wasm.cwrap(Bo, null, ["number", "number", "number", "number", "number"]); } -function r9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n } = o, { x: s } = t6, a = e.dataIdMap.get(s.dataId).id, i = a, p = s, { transposed: u, axes: c, inputWasTransposed: l } = kr(s, n, e); +function w9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = a, p = s, { transposed: u, axes: c, inputWasTransposed: l } = kr(s, n, e); if (l) { let x = e.dataIdMap.get(u.dataId).id; x !== a && (p = u, i = x); } let m = p.shape.slice(0, -1), d = e.makeOutput(m, "int32"), f = e.dataIdMap.get(d.dataId).id, h = y.sizeFromShape(d.shape), g = p.shape[c[0]]; - return kD(i, Fe[p.dtype], h, g, f), l && e.disposeData(u.dataId), d; + return AF(i, Fe[p.dtype], h, g, f), l && e.disposeData(u.dataId), d; } -var ND = { kernelName: Vo, backendName: "wasm", kernelFunc: r9, setupFunc: t9 }; -var TD; -function o9(r) { - TD = r.wasm.cwrap(zo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var RF = { kernelName: Bo, backendName: "wasm", kernelFunc: w9, setupFunc: S9 }; +var DF; +function I9(r) { + DF = r.wasm.cwrap(Vo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function n9(r) { - let { inputs: e, attrs: t6, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t6, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels; +function v9(r) { + let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); if (c.dilationWidth !== 1 || c.dilationHeight !== 1) throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`); let w = o.makeOutput(c.outShape, "float32"), k = o.dataIdMap.get(w.dataId).id; - return TD(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), w; + return DF(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), w; } -var _D = { kernelName: zo, backendName: "wasm", setupFunc: o9, kernelFunc: n9 }; -function Mt(r) { - let { inputs: e, attrs: t6 } = r, { x: o } = e, { shape: n } = t6, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); +var FF = { kernelName: Vo, backendName: "wasm", setupFunc: I9, kernelFunc: v9 }; +function Lt(r) { + let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var ED = { kernelName: Ns, backendName: "wasm", kernelFunc: Mt }; -var $D; -function s9(r) { - $D = r.wasm.cwrap(Wo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); +var OF = { kernelName: _s, backendName: "wasm", kernelFunc: Lt }; +var PF; +function k9(r) { + PF = r.wasm.cwrap(zo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); } -function a9(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; +function N9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("BatchMatMul for non non-float32 tensors not yet supported."); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); - let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Mt({ inputs: { x: n }, backend: t6, attrs: { shape: w } }), $ = Mt({ inputs: { x: s }, backend: t6, attrs: { shape: k } }), A = t6.dataIdMap.get(_.dataId).id, R = t6.dataIdMap.get($.dataId).id, D = a ? _.shape[2] : _.shape[1], P = i ? $.shape[1] : $.shape[2], M = Math.max(g, x), L = t6.makeOutput([M, D, P], _.dtype), W = t6.dataIdMap.get(L.dataId).id, V = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array($.shape).buffer); - return $D(A, V, _.shape.length, R, U, $.shape.length, a, i, W), t6.disposeData(_.dataId), t6.disposeData($.dataId), L.shape = C, L; + let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Lt({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Lt({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), A = t10.dataIdMap.get(_.dataId).id, R = t10.dataIdMap.get(E.dataId).id, D = a ? _.shape[2] : _.shape[1], P = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t10.makeOutput([M, D, P], _.dtype), V = t10.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer); + return PF(A, z, _.shape.length, R, U, E.shape.length, a, i, V), t10.disposeData(_.dataId), t10.disposeData(E.dataId), L.shape = C, L; } -var AD = { kernelName: Wo, backendName: "wasm", setupFunc: s9, kernelFunc: a9 }; +var MF = { kernelName: zo, backendName: "wasm", setupFunc: k9, kernelFunc: N9 }; function Eo(r) { - let { inputs: { x: e }, attrs: { begin: t6, size: o }, backend: n } = r, [s, a] = ut.parseSliceParams(e, t6, o), i = ut.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId); + let { inputs: { x: e }, attrs: { begin: t10, size: o }, backend: n } = r, [s, a] = ut.parseSliceParams(e, t10, o), i = ut.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId); if (i) { let f = ut.computeFlatOffset(s, c); return e.dtype === "string" ? l.stringBytes = p.slice(f, f + y.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(f, f + y.sizeFromShape(a))), u; } if (e.dtype === "string") { - let f = vu(p, s, a, e.shape, e.dtype); + let f = $u(p, s, a, e.shape, e.dtype); return l.stringBytes = f, u; } let m = n.typedArrayFromHeap(u), d = e.shape.length; if (d === 2) - i9(p, c[0], m, s, a); + T9(p, c[0], m, s, a); else if (d === 3) - u9(p, c[0], c[1], m, s, a); + _9(p, c[0], c[1], m, s, a); else if (d === 4) - p9(p, c[0], c[1], c[2], m, s, a); + $9(p, c[0], c[1], c[2], m, s, a); else { - let f = vu(p, s, a, e.shape, e.dtype); + let f = $u(p, s, a, e.shape, e.dtype); m.set(f); } return u; } -function i9(r, e, t6, o, n) { +function T9(r, e, t10, o, n) { let s = 0, a = o[0], i = o[1], p = a + n[0]; for (let u = a; u < p; u++) { let c = u * e + i; - t6.set(r.subarray(c, c + n[1]), s), s += n[1]; + t10.set(r.subarray(c, c + n[1]), s), s += n[1]; } } -function u9(r, e, t6, o, n, s) { +function _9(r, e, t10, o, n, s) { let a = 0, i = n[0], p = n[1], u = n[2], c = i + s[0], l = p + s[1]; for (let m = i; m < c; m++) for (let d = p; d < l; d++) { - let f = m * e + d * t6 + u; + let f = m * e + d * t10 + u; o.set(r.subarray(f, f + s[2]), a), a += s[2]; } } -function p9(r, e, t6, o, n, s, a) { +function $9(r, e, t10, o, n, s, a) { let i = 0, p = s[0], u = s[1], c = s[2], l = p + a[0], m = u + a[1], d = c + a[2], f = s[3]; for (let h = p; h < l; h++) for (let g = u; g < m; g++) for (let x = c; x < d; x++) { - let b = h * e + g * t6 + x * o + f; + let b = h * e + g * t10 + x * o + f; n.set(r.subarray(b, b + a[3]), i), i += a[3]; } } -var RD = { kernelName: _s, backendName: "wasm", kernelFunc: Eo }; -function c9(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Mt({ inputs: { x: n }, backend: t6, attrs: { shape: p } }), f = uo({ inputs: { x: d }, backend: t6, attrs: { perm: u } }), h = Mt({ inputs: { x: f }, backend: t6, attrs: { shape: c } }), g = Eo({ inputs: { x: h }, backend: t6, attrs: { begin: l, size: m } }); - return t6.disposeData(d.dataId), t6.disposeData(f.dataId), t6.disposeData(d.dataId), g; +var LF = { kernelName: Es, backendName: "wasm", kernelFunc: Eo }; +function E9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Lt({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = uo({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Lt({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Eo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } }); + return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(d.dataId), g; } -var FD = { kernelName: xs, backendName: "wasm", kernelFunc: c9 }; -function ls(r) { - let { inputs: { x: e }, attrs: { dtype: t6 }, backend: o } = r, n = o.makeOutput(e.shape, t6), s = o.typedArrayFromHeap(e); +var BF = { kernelName: bs, backendName: "wasm", kernelFunc: E9 }; +function ms(r) { + let { inputs: { x: e }, attrs: { dtype: t10 }, backend: o } = r, n = o.makeOutput(e.shape, t10), s = o.typedArrayFromHeap(e); return o.typedArrayFromHeap(n).set(s), n; } -var DD = { kernelName: co, backendName: "wasm", kernelFunc: ls }; -var OD = Ve(Uo); -var PD; -function l9(r) { - PD = r.wasm.cwrap(lo, null, ["number", "number", "number", "number"]); +var VF = { kernelName: co, backendName: "wasm", kernelFunc: ms }; +var zF = Ve(Wo); +var WF; +function A9(r) { + WF = r.wasm.cwrap(lo, null, ["number", "number", "number", "number"]); } -function m9(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t6.dataIdMap.get(n.dataId).id, p = t6.makeOutput(n.shape, n.dtype), u = t6.dataIdMap.get(p.dataId).id; - return PD(i, s, a, u), p; +function R9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t10.dataIdMap.get(n.dataId).id, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(p.dataId).id; + return WF(i, s, a, u), p; } -var MD = { kernelName: lo, backendName: "wasm", setupFunc: l9, kernelFunc: m9 }; -function zw(r) { - let { inputs: e, backend: t6 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape); +var UF = { kernelName: lo, backendName: "wasm", setupFunc: A9, kernelFunc: R9 }; +function Zw(r) { + let { inputs: e, backend: t10 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape); S.assertParamsConsistent(n, o); let s = S.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0); if (a.length === 1) - return Vu({ inputs: { x: a[0] }, backend: t6 }); - let i = t6.makeOutput(s, e[0].dtype); + return Hu({ inputs: { x: a[0] }, backend: t10 }); + let i = t10.makeOutput(s, e[0].dtype); if (y.sizeFromShape(s) === 0) return i; if (a[0].dtype === "string") { let d = a.map((C) => { let k = [-1, y.sizeFromShape(C.shape.slice(o))]; - return Mt({ inputs: { x: C }, backend: t6, attrs: { shape: k } }); - }), f = d.map((C) => ({ vals: t6.readSync(C.dataId), shape: C.shape })); + return Lt({ inputs: { x: C }, backend: t10, attrs: { shape: k } }); + }), f = d.map((C) => ({ vals: t10.readSync(C.dataId), shape: C.shape })); s = S.computeOutShape(d.map((C) => C.shape), 1); - let h = d[0].shape[0] === 1, g = Su(f, s, e[0].dtype, h), x = S.computeOutShape(a.map((C) => C.shape), o); + let h = d[0].shape[0] === 1, g = Nu(f, s, e[0].dtype, h), x = S.computeOutShape(a.map((C) => C.shape), o); i.shape = x; - let b = t6.dataIdMap.get(i.dataId); - return b.stringBytes = S.fromStringArrayToUint8(g), d.forEach((C) => t6.disposeData(C.dataId)), i; + let b = t10.dataIdMap.get(i.dataId); + return b.stringBytes = S.fromStringArrayToUint8(g), d.forEach((C) => t10.disposeData(C.dataId)), i; } let p = y.sizeFromShape(a[0].shape.slice(0, o)), u = 0, c = a.map((d) => { let f = y.sizeFromShape(d.shape.slice(o)); return u += f, f; - }), l = a.map((d) => t6.typedArrayFromHeap(d)), m = t6.typedArrayFromHeap(i); + }), l = a.map((d) => t10.typedArrayFromHeap(d)), m = t10.typedArrayFromHeap(i); for (let d = 0; d < p; d++) { let f = d * u; for (let h = 0; h < l.length; h++) { @@ -24512,259 +24508,259 @@ function zw(r) { } return i; } -var LD = { kernelName: ys, backendName: "wasm", kernelFunc: zw }; -var BD; -function d9(r) { - BD = r.wasm.cwrap(Go, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var GF = { kernelName: Cs, backendName: "wasm", kernelFunc: Zw }; +var HF; +function D9(r) { + HF = r.wasm.cwrap(Uo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function f9(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t6, d = S.convertConv2DDataFormat(m), f = S.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, w = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, $ = f.strideHeight, A = f.strideWidth, R = f.inChannels, D = f.outChannels, P = f.padInfo.type === "SAME" ? 1 : 0; +function F9(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t10, d = S.convertConv2DDataFormat(m), f = S.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, w = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, A = f.strideWidth, R = f.inChannels, D = f.outChannels, P = f.padInfo.type === "SAME" ? 1 : 0; if (f.dataFormat !== "channelsLast") throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`); let M = o.makeOutput(f.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id; - return BD(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, w, P, k, _, $, A, R, D, L), M; + return HF(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, w, P, k, _, E, A, R, D, L), M; } -var VD = { kernelName: Go, backendName: "wasm", setupFunc: d9, kernelFunc: f9 }; -var zD; -function h9(r) { - zD = r.wasm.cwrap(Ho, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var KF = { kernelName: Uo, backendName: "wasm", setupFunc: D9, kernelFunc: F9 }; +var qF; +function O9(r) { + qF = r.wasm.cwrap(Go, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function g9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { dy: n, filter: s } = t6, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = S.convertConv2DDataFormat(p), d = S.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: w, outHeight: k, outWidth: _, strideHeight: $, strideWidth: A } = d, R = h - 1 - d.padInfo.top, D = g - 1 - d.padInfo.left, P = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [W, V, U] = y.computeStrides(s.shape), q = M[0], H = P ? M[1] : M[2], j = P ? M[2] : 1, X = P ? 1 : M[1], Z = L[0], ee = P ? L[1] : L[2], Y = P ? L[2] : 1, J = P ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), pe = e.dataIdMap.get(ie.dataId).id, he = e.dataIdMap.get(n.dataId).id, we = e.dataIdMap.get(s.dataId).id; - return zD(he, we, f, h, g, b, C, x, k, _, w, $, A, R, D, W, V, U, q, H, j, X, Z, ee, Y, J, pe), ie; +function P9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { dy: n, filter: s } = t10, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = S.convertConv2DDataFormat(p), d = S.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: w, outHeight: k, outWidth: _, strideHeight: E, strideWidth: A } = d, R = h - 1 - d.padInfo.top, D = g - 1 - d.padInfo.left, P = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [V, z, U] = y.computeStrides(s.shape), K = M[0], H = P ? M[1] : M[2], q = P ? M[2] : 1, Z = P ? 1 : M[1], ee = L[0], oe = P ? L[1] : L[2], J = P ? L[2] : 1, te = P ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), ce = e.dataIdMap.get(ie.dataId).id, ye = e.dataIdMap.get(n.dataId).id, Ne = e.dataIdMap.get(s.dataId).id; + return qF(ye, Ne, f, h, g, b, C, x, k, _, w, E, A, R, D, V, z, U, K, H, q, Z, ee, oe, J, te, ce), ie; } -var WD = { kernelName: Ho, backendName: "wasm", setupFunc: h9, kernelFunc: g9 }; -var UD = Ve(qo); -var GD = Ve(Ko); -var Ww; +var jF = { kernelName: Go, backendName: "wasm", setupFunc: O9, kernelFunc: P9 }; +var XF = Ve(Ho); +var YF = Ve(Ko); +var Jw; (function(r) { r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest"; -})(Ww || (Ww = {})); -var HD; -function x9(r) { - HD = r.wasm.cwrap(Yo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); +})(Jw || (Jw = {})); +var QF; +function M9(r) { + QF = r.wasm.cwrap(Xo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); } -function y9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t6, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h; - i.dtype !== "float32" && (h = ls({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId)); +function L9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t10, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h; + i.dtype !== "float32" && (h = ms({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId)); let g = f.id, x = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, C = e.makeOutput(d, "float32"), w = e.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer); - return HD(g, x, b, c, k, l, m, Ww[n], s, w), h != null && e.disposeData(h.dataId), C; + return QF(g, x, b, c, k, l, m, Jw[n], s, w), h != null && e.disposeData(h.dataId), C; } -var qD = { kernelName: Yo, backendName: "wasm", setupFunc: x9, kernelFunc: y9 }; -var KD; -function b9(r) { - KD = r.wasm.cwrap(jo, null, ["number", "number", "number", "number", "number", "number"]); +var ZF = { kernelName: Xo, backendName: "wasm", setupFunc: M9, kernelFunc: L9 }; +var JF; +function B9(r) { + JF = r.wasm.cwrap(qo, null, ["number", "number", "number", "number", "number", "number"]); } -function C9(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function V9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`); let u = S.getAxesPermutation([s], p), c = n; - u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t6 })); + u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 })); let l = S.getInnerMostAxes(1, p)[0]; S.assertAxesAreInnerMostDims("cumprod", [l], p); - let m = t6.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t6.dataIdMap.get(c.dataId).id, h = t6.dataIdMap.get(m.dataId).id; - KD(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]); + let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id; + JF(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]); let g = m; if (u !== null) { let x = S.getUndoAxesPermutation(u); - g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t6 }), t6.disposeData(c.dataId), t6.disposeData(m.dataId); + g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId); } return g; } -var jD = { kernelName: jo, backendName: "wasm", setupFunc: b9, kernelFunc: C9 }; -var XD; -function S9(r) { - XD = r.wasm.cwrap(Xo, null, ["number", "number", "number", "number", "number", "number"]); +var eO = { kernelName: qo, backendName: "wasm", setupFunc: B9, kernelFunc: V9 }; +var tO; +function z9(r) { + tO = r.wasm.cwrap(jo, null, ["number", "number", "number", "number", "number", "number"]); } -function w9(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function W9(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`); let u = S.getAxesPermutation([s], p), c = n; - u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t6 })); + u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 })); let l = S.getInnerMostAxes(1, p)[0]; S.assertAxesAreInnerMostDims("cumsum", [l], p); - let m = t6.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t6.dataIdMap.get(c.dataId).id, h = t6.dataIdMap.get(m.dataId).id; - XD(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]); + let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id; + tO(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]); let g = m; if (u !== null) { let x = S.getUndoAxesPermutation(u); - g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t6 }), t6.disposeData(c.dataId), t6.disposeData(m.dataId); + g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId); } return g; } -var YD = { kernelName: Xo, backendName: "wasm", setupFunc: S9, kernelFunc: w9 }; -var QD; -function I9(r) { - QD = r.wasm.cwrap(Qo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); +var rO = { kernelName: jo, backendName: "wasm", setupFunc: z9, kernelFunc: W9 }; +var oO; +function U9(r) { + oO = r.wasm.cwrap(Yo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); } -function v9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { x: n } = t6, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), w = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id; - return QD(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, w, f.length, k), h; +function G9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), w = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id; + return oO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, w, f.length, k), h; } -var ZD = { kernelName: Qo, backendName: "wasm", setupFunc: I9, kernelFunc: v9 }; -var JD; -function k9(r) { - JD = r.wasm.cwrap(Zo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var nO = { kernelName: Yo, backendName: "wasm", setupFunc: U9, kernelFunc: G9 }; +var sO; +function H9(r) { + sO = r.wasm.cwrap(Qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function N9(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t6, m = u == null ? [1, 1] : u, d = S.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, w = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, $ = d.strideWidth, A = d.inChannels, R = d.outChannels, D = d.padInfo.type === "SAME" ? 1 : 0; +function K9(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t10, m = u == null ? [1, 1] : u, d = S.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, w = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, A = d.inChannels, R = d.outChannels, D = d.padInfo.type === "SAME" ? 1 : 0; if (d.dataFormat !== "channelsLast") throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`); let P = o.makeOutput(d.outShape, "float32"), M = o.dataIdMap.get(P.dataId).id; - return JD(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, D, w, k, _, $, A, R, M), P; + return sO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, D, w, k, _, E, A, R, M), P; } -var eO = { kernelName: Zo, backendName: "wasm", setupFunc: k9, kernelFunc: N9 }; -var tO = Ve(en); -var T9 = false; -var rO = rt(tn, T9, "bool"); -var oO = Ve(rn, "float32"); +var aO = { kernelName: Qo, backendName: "wasm", setupFunc: H9, kernelFunc: K9 }; +var iO = Ve(Jo); +var q9 = false; +var uO = rt(en, q9, "bool"); +var pO = Ve(tn, "float32"); function yg(r) { - let { inputs: e, attrs: t6, backend: o } = r, { input: n } = e, { dim: s } = t6, a = n.shape.length, i = n.shape.slice(), p = s; - return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Mt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); + let { inputs: e, attrs: t10, backend: o } = r, { input: n } = e, { dim: s } = t10, a = n.shape.length, i = n.shape.slice(), p = s; + return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Lt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); } -var nO = { kernelName: bs, backendName: "wasm", kernelFunc: yg }; -function Uw(r) { - let { attrs: { shape: e, value: t6, dtype: o }, backend: n } = r, s = n.makeOutput(e, o); - return n.typedArrayFromHeap(s).fill(t6), s; +var cO = { kernelName: Ss, backendName: "wasm", kernelFunc: yg }; +function eI(r) { + let { attrs: { shape: e, value: t10, dtype: o }, backend: n } = r, s = n.makeOutput(e, o); + return n.typedArrayFromHeap(s).fill(t10), s; } -var sO = { kernelName: Cs, backendName: "wasm", kernelFunc: Uw }; -var aO; -function _9(r) { - aO = r.wasm.cwrap(on, null, ["number", "number", "number", "number", "number", "number"]); +var lO = { kernelName: ws, backendName: "wasm", kernelFunc: eI }; +var mO; +function j9(r) { + mO = r.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number"]); } -function E9(r) { - let { inputs: e, backend: t6 } = r, { image: o } = e, n = t6.makeOutput(o.shape, o.dtype), s = t6.dataIdMap.get(o.dataId).id, a = t6.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; - return aO(s, i, p, u, c, a), n; +function X9(r) { + let { inputs: e, backend: t10 } = r, { image: o } = e, n = t10.makeOutput(o.shape, o.dtype), s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; + return mO(s, i, p, u, c, a), n; } -var iO = { kernelName: on, backendName: "wasm", kernelFunc: E9, setupFunc: _9 }; -var uO = Ve(nn); -var $9 = false; -var pO = rt(sn, $9); -var cO; -function A9(r) { - cO = r.wasm.cwrap(an, null, ["number", "number", "number", "number", "number", "number", "number"]); +var dO = { kernelName: rn, backendName: "wasm", kernelFunc: X9, setupFunc: j9 }; +var fO = Ve(on); +var Y9 = false; +var hO = rt(nn, Y9); +var gO; +function Q9(r) { + gO = r.wasm.cwrap(sn, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function R9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t6, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype); +function Z9(r) { + let { backend: e, inputs: t10, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t10, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype); if (y.sizeFromShape(s.shape) === 0) return h; let g = e.dataIdMap.get(h.dataId).id; - return cO(c, l, m, d, f, n, g), h; + return gO(c, l, m, d, f, n, g), h; } -var lO = { kernelName: an, backendName: "wasm", setupFunc: A9, kernelFunc: R9 }; -var mO; -function F9(r) { - mO = r.wasm.cwrap(ho, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var xO = { kernelName: sn, backendName: "wasm", setupFunc: Q9, kernelFunc: Z9 }; +var yO; +function J9(r) { + yO = r.wasm.cwrap(go, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function D9(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t6, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = Wi[d]; +function eJ(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = Ui[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`); let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0; if (a != null) { - let Y = o.dataIdMap.get(a.dataId); - if (Y.shape.length !== 1) - throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Y.shape.length}.`); - if (Y.shape[0] !== C) - throw new Error(`FusedConv2D bias shape (${Y.shape}) does not match the number of output channels (${C})`); - w = Y.id; + let J = o.dataIdMap.get(a.dataId); + if (J.shape.length !== 1) + throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`); + if (J.shape[0] !== C) + throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${C})`); + w = J.id; } - let k = h.filterHeight, _ = h.filterWidth, $ = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, W = h.strideWidth, V = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, q = h.batchSize, H = h.inHeight, j = h.inWidth; + let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, V = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, K = h.batchSize, H = h.inHeight, q = h.inWidth; if (l !== "NHWC") throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); - let X = o.makeOutput(h.outShape, "float32"), Z = o.dataIdMap.get(X.dataId).id, ee = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return mO(x, q, H, j, b, k, _, w, $, A, R, D, U, P, M, L, W, V, C, g, ee, f || 0, Z), X; + let Z = o.makeOutput(h.outShape, "float32"), ee = o.dataIdMap.get(Z.dataId).id, oe = i == null ? 0 : o.dataIdMap.get(i.dataId).id; + return yO(x, K, H, q, b, k, _, w, E, A, R, D, U, P, M, L, V, z, C, g, oe, f || 0, ee), Z; } -var dO = { kernelName: ho, backendName: "wasm", setupFunc: F9, kernelFunc: D9 }; -var fO; -function O9(r) { - fO = r.wasm.cwrap(go, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var bO = { kernelName: go, backendName: "wasm", setupFunc: J9, kernelFunc: eJ }; +var CO; +function tJ(r) { + CO = r.wasm.cwrap(xo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function P9(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t6, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = Wi[d]; +function rJ(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = Ui[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`); let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0; if (a != null) { - let Y = o.dataIdMap.get(a.dataId); - if (Y.shape.length !== 1) - throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Y.shape.length}.`); - if (Y.shape[0] !== C) - throw new Error(`FusedDepthwiseConv2D bias shape (${Y.shape}) does not match the number of output channels (${C})`); - w = Y.id; + let J = o.dataIdMap.get(a.dataId); + if (J.shape.length !== 1) + throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`); + if (J.shape[0] !== C) + throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${C})`); + w = J.id; } - let k = h.filterHeight, _ = h.filterWidth, $ = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, W = h.strideWidth, V = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, q = h.batchSize, H = h.inHeight, j = h.inWidth; + let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, V = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, K = h.batchSize, H = h.inHeight, q = h.inWidth; if (l !== "NHWC") throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); - let X = o.makeOutput(h.outShape, "float32"), Z = o.dataIdMap.get(X.dataId).id, ee = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return fO(x, q, H, j, b, k, _, w, $, A, R, D, U, P, M, L, W, V, C, g, ee, f || 0, Z), X; + let Z = o.makeOutput(h.outShape, "float32"), ee = o.dataIdMap.get(Z.dataId).id, oe = i == null ? 0 : o.dataIdMap.get(i.dataId).id; + return CO(x, K, H, q, b, k, _, w, E, A, R, D, U, P, M, L, V, z, C, g, oe, f || 0, ee), Z; } -var hO = { kernelName: go, backendName: "wasm", setupFunc: O9, kernelFunc: P9 }; -var gO; -function M9(r) { - gO = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); +var SO = { kernelName: xo, backendName: "wasm", setupFunc: tJ, kernelFunc: rJ }; +var wO; +function oJ(r) { + wO = r.wasm.cwrap(an, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); } -function L9(r) { - let { backend: e, inputs: t6 } = r, { params: o, indices: n } = t6, [s, a, i, p] = Ym.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype); +function nJ(r) { + let { backend: e, inputs: t10 } = r, { params: o, indices: n } = t10, [s, a, i, p] = Wd.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype); if (a === 0) return u; let c = n.shape, l = c[c.length - 1], d = e.dataIdMap.get(o.dataId).id, h = e.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), x = e.dataIdMap.get(u.dataId).id; - return gO(d, Fe[o.dtype], h, a, l, i, g, x), u; + return wO(d, Fe[o.dtype], h, a, l, i, g, x), u; } -var xO = { kernelName: un, backendName: "wasm", setupFunc: M9, kernelFunc: L9 }; -var yO; -function B9(r) { - yO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); +var IO = { kernelName: an, backendName: "wasm", setupFunc: oJ, kernelFunc: nJ }; +var vO; +function sJ(r) { + vO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); } -function V9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { x: n, indices: s } = t6, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p]; +function aJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { x: n, indices: s } = t10, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p]; for (let R = 0; R < u.length; ++R) { let D = u[R]; y.assert(D <= c - 1 && D >= 0, () => `GatherV2: the index value ${D} is not in [0, ${c - 1}]`); } - let l = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = Mt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = Mt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype); + let l = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = Lt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = Lt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype); if (y.sizeFromShape(n.shape) === 0) return g; - let x = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, $ = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), A = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer); - return yO(C, Fe[n.dtype], $, x, k, l.batchSize, A, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g; + let x = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), A = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer); + return vO(C, Fe[n.dtype], E, x, k, l.batchSize, A, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g; } -var bO = { kernelName: Ss, backendName: "wasm", setupFunc: B9, kernelFunc: V9 }; -var z9 = false; -var CO = rt(pn, z9, "bool"); -var W9 = false; -var SO = rt(cn, W9, "bool"); -var wO = Ve(ln, "bool"); -var IO; -function U9(r) { - IO = r.wasm.cwrap(mn, null, ["number", "number", "number", "number"]); +var kO = { kernelName: Is, backendName: "wasm", setupFunc: sJ, kernelFunc: aJ }; +var iJ = false; +var NO = rt(un, iJ, "bool"); +var uJ = false; +var TO = rt(pn, uJ, "bool"); +var _O = Ve(cn, "bool"); +var $O; +function pJ(r) { + $O = r.wasm.cwrap(ln, null, ["number", "number", "number", "number"]); } -function G9(r) { - let { inputs: { x: e }, attrs: { alpha: t6 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32"); +function cJ(r) { + let { inputs: { x: e }, attrs: { alpha: t10 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32"); if (y.sizeFromShape(e.shape) !== 0) { let a = o.dataIdMap.get(s.dataId).id; - IO(n, Fe[e.dtype], t6, a); + $O(n, Fe[e.dtype], t10, a); } return s; } -var vO = { kernelName: mn, backendName: "wasm", setupFunc: U9, kernelFunc: G9 }; -var H9 = false; -var kO = rt(dn, H9, "bool"); -var q9 = false; -var NO = rt(fn, q9, "bool"); -var TO = Ve(hn); -var K9 = false; -var _O = rt(gn, K9, "bool"); -var EO = Ve(xn); -var j9 = false; -var $O = rt(xa, j9, "bool"); -var X9 = false; -var AO = rt(GI, X9, "bool"); -var RO; -function Y9(r) { - RO = r.wasm.cwrap(yn, null, ["number", "number", "number", "number"]); +var EO = { kernelName: ln, backendName: "wasm", setupFunc: pJ, kernelFunc: cJ }; +var lJ = false; +var AO = rt(mn, lJ, "bool"); +var mJ = false; +var RO = rt(dn, mJ, "bool"); +var DO = Ve(fn); +var dJ = false; +var FO = rt(hn, dJ, "bool"); +var OO = Ve(gn); +var fJ = false; +var PO = rt(xn, fJ, "bool"); +var hJ = false; +var MO = rt(XI, hJ, "bool"); +var LO; +function gJ(r) { + LO = r.wasm.cwrap(yn, null, ["number", "number", "number", "number"]); } -function Q9(r) { - let { backend: e, inputs: t6, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t6, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); +function xJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); if (d) { let C = e.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -24774,7 +24770,7 @@ function Q9(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - RO(p, Fe[a.dtype], x, C); + LO(p, Fe[a.dtype], x, C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -24782,40 +24778,40 @@ function Q9(r) { } return b; } -var FO = { kernelName: yn, backendName: "wasm", setupFunc: Y9, kernelFunc: Q9 }; -var Z9 = false; -var DO = rt(bn, Z9); -var OO; -function J9(r) { - OO = r.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var BO = { kernelName: yn, backendName: "wasm", setupFunc: gJ, kernelFunc: xJ }; +var yJ = false; +var VO = rt(bn, yJ); +var zO; +function bJ(r) { + zO = r.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function eJ(r) { - let { inputs: e, attrs: t6, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id; +function CJ(r) { + let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id; y.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`); - let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t6, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, w = c.strideWidth, k = c.inChannels, _ = c.outChannels; + let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, w = c.strideWidth, k = c.inChannels, _ = c.outChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); - let $ = o.makeOutput(c.outShape, "float32"), A = o.dataIdMap.get($.dataId).id; - return OO(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, w, k, _, A), $; + let E = o.makeOutput(c.outShape, "float32"), A = o.dataIdMap.get(E.dataId).id; + return zO(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, w, k, _, A), E; } -var PO = { kernelName: Cn, backendName: "wasm", setupFunc: J9, kernelFunc: eJ }; -var MO; -function tJ(r) { - MO = r.wasm.cwrap(Sn, null, ["number, number, number"]); +var WO = { kernelName: Cn, backendName: "wasm", setupFunc: bJ, kernelFunc: CJ }; +var UO; +function SJ(r) { + UO = r.wasm.cwrap(Sn, null, ["number, number, number"]); } -function rJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; +function wJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; if (d) { let w = e.dataIdMap.get(c.dataId).id; w !== i && (u = c, p = w, f = S.getInnerMostAxes(f.length, u.shape.length)); } S.assertAxesAreInnerMostDims("mean", f, u.shape.length); let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u; - u.dtype !== "float32" && (b = ls({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id); + u.dtype !== "float32" && (b = ms({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id); let C = e.makeOutput(h, "float32"); if (y.sizeFromShape(u.shape) !== 0) { let w = e.dataIdMap.get(C.dataId).id; - MO(p, x, w); + UO(p, x, w); } if (d && e.disposeData(c.dataId), s) { let w = S.expandShapeToKeepDim(C.shape, m); @@ -24823,13 +24819,13 @@ function rJ(r) { } return u.dtype !== "float32" && e.disposeData(b.dataId), C; } -var LO = { kernelName: Sn, backendName: "wasm", setupFunc: tJ, kernelFunc: rJ }; -var BO; -function oJ(r) { - BO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number"]); +var GO = { kernelName: Sn, backendName: "wasm", setupFunc: SJ, kernelFunc: wJ }; +var HO; +function IJ(r) { + HO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number"]); } -function nJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); +function vJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e); if (d) { let C = e.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C); @@ -24839,7 +24835,7 @@ function nJ(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - BO(p, Fe[a.dtype], x, C); + HO(p, Fe[a.dtype], x, C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -24847,122 +24843,122 @@ function nJ(r) { } return b; } -var VO = { kernelName: wn, backendName: "wasm", setupFunc: oJ, kernelFunc: nJ }; -var sJ = false; -var zO = rt(In, sJ); -var Gw; +var KO = { kernelName: wn, backendName: "wasm", setupFunc: IJ, kernelFunc: vJ }; +var kJ = false; +var qO = rt(In, kJ); +var tI; (function(r) { r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric"; -})(Gw || (Gw = {})); -var WO; -function aJ(r) { - WO = r.wasm.cwrap(vn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +})(tI || (tI = {})); +var jO; +function NJ(r) { + jO = r.wasm.cwrap(vn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function iJ(r) { - let { inputs: { x: e }, backend: t6, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t6.dataIdMap.get(e.dataId).id, i = t6.makeOutput(s, e.dtype), p = t6.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); - return WO(a, u, e.shape.length, Fe[e.dtype], m, d, Gw[n], p), i; +function TJ(r) { + let { inputs: { x: e }, backend: t10, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), p = t10.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); + return jO(a, u, e.shape.length, Fe[e.dtype], m, d, tI[n], p), i; } -var UO = { kernelName: vn, backendName: "wasm", kernelFunc: iJ, setupFunc: aJ }; -var uJ = true; -var GO = rt(kn, uJ); -var HO = Ve(ws); -function Sc(r, e) { - let t6 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t6[0], n = t6[1], s = t6[2], a = t6[3]; +var XO = { kernelName: vn, backendName: "wasm", kernelFunc: TJ, setupFunc: NJ }; +var _J = true; +var YO = rt(kn, _J); +var QO = Ve(vs); +function wc(r, e) { + let t10 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t10[0], n = t10[1], s = t10[2], a = t10[3]; return r.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a }; } -var qO; -function pJ(r) { - qO = r.wasm.cwrap(Tn, "number", ["number", "number", "number", "number", "number"]); +var ZO; +function $J(r) { + ZO = r.wasm.cwrap(Tn, "number", ["number", "number", "number", "number", "number"]); } -function cJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t6, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = qO(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Sc(e, l); +function EJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t10, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = ZO(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = wc(e, l); return e.wasm._free(f), e.wasm._free(h), e.makeOutput([d], "int32", m); } -var KO = { kernelName: Tn, backendName: "wasm", setupFunc: pJ, kernelFunc: cJ }; -var jO; -function lJ(r) { - jO = r.wasm.cwrap(ba, "number", ["number", "number", "number", "number", "number", "bool"]); +var JO = { kernelName: Tn, backendName: "wasm", setupFunc: $J, kernelFunc: EJ }; +var eP; +function AJ(r) { + eP = r.wasm.cwrap(ba, "number", ["number", "number", "number", "number", "number", "bool"]); } -function mJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t6, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = jO(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Sc(e, m); +function RJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = eP(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = wc(e, m); e.wasm._free(h); let x = e.makeOutput([f], "int32", d), b = e.makeOutput([], "int32", g); return [x, b]; } -var XO = { kernelName: ba, backendName: "wasm", setupFunc: lJ, kernelFunc: mJ }; -var YO; -function dJ(r) { - YO = r.wasm.cwrap(_n, "number", ["number", "number", "number", "number", "number", "number"]); +var tP = { kernelName: ba, backendName: "wasm", setupFunc: AJ, kernelFunc: RJ }; +var rP; +function DJ(r) { + rP = r.wasm.cwrap(_n, "number", ["number", "number", "number", "number", "number", "number"]); } -function fJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t6, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = YO(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Sc(e, m); +function FJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = rP(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = wc(e, m); e.wasm._free(g); let x = e.makeOutput([f], "int32", d), b = e.makeOutput([f], "float32", h); return [x, b]; } -var QO = { kernelName: _n, backendName: "wasm", setupFunc: dJ, kernelFunc: fJ }; -var hJ = false; -var ZO = rt(Nn, hJ, "bool"); -var JO; -function gJ(r) { - JO = r.wasm.cwrap(En, null, ["number", "number", "number", "number", "number"]); +var oP = { kernelName: _n, backendName: "wasm", setupFunc: DJ, kernelFunc: FJ }; +var OJ = false; +var nP = rt(Nn, OJ, "bool"); +var sP; +function PJ(r) { + sP = r.wasm.cwrap($n, null, ["number", "number", "number", "number", "number"]); } -function xJ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t6.makeOutput([...n.shape, a], s), c = t6.dataIdMap.get(u.dataId).id, m = t6.dataIdMap.get(n.dataId).id; - return JO(m, a, i, p, c), u; +function MJ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t10.makeOutput([...n.shape, a], s), c = t10.dataIdMap.get(u.dataId).id, m = t10.dataIdMap.get(n.dataId).id; + return sP(m, a, i, p, c), u; } -var eP = { kernelName: En, backendName: "wasm", setupFunc: gJ, kernelFunc: xJ }; -function yJ(r) { - let { inputs: { x: e }, backend: t6 } = r, o = t6.makeOutput(e.shape, e.dtype); - return t6.typedArrayFromHeap(o).fill(1), o; +var aP = { kernelName: $n, backendName: "wasm", setupFunc: PJ, kernelFunc: MJ }; +function LJ(r) { + let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype); + return t10.typedArrayFromHeap(o).fill(1), o; } -var tP = { kernelName: Is, backendName: "wasm", kernelFunc: yJ }; -function bJ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o; +var iP = { kernelName: ks, backendName: "wasm", kernelFunc: LJ }; +function BJ(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return yg({ inputs: { input: e[0] }, backend: t6, attrs: { dim: n } }); + return yg({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = yg({ inputs: { input: c }, backend: t6, attrs: { dim: n } }); + let l = yg({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); return i.push(l), l; - }), u = zw({ inputs: p, backend: t6, attrs: { axis: n } }); - return i.forEach((c) => t6.disposeData(c.dataId)), u; + }), u = Zw({ inputs: p, backend: t10, attrs: { axis: n } }); + return i.forEach((c) => t10.disposeData(c.dataId)), u; } -var rP = { kernelName: vs, backendName: "wasm", kernelFunc: bJ }; -var oP; -function CJ(r) { - oP = r.wasm.cwrap($n, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +var uP = { kernelName: Ns, backendName: "wasm", kernelFunc: BJ }; +var pP; +function VJ(r) { + pP = r.wasm.cwrap(En, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function SJ(r) { - let { inputs: { x: e }, backend: t6, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]); +function zJ(r) { + let { inputs: { x: e }, backend: t10, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]); if (y.sizeFromShape(e.shape) === 0) - return Uw({ backend: t6, attrs: { shape: s, value: n, dtype: e.dtype } }); - let a = t6.dataIdMap.get(e.dataId).id, i = t6.makeOutput(s, e.dtype), u = t6.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer); - return oP(a, c, e.shape.length, Fe[e.dtype], d, f, n, u), i; + return eI({ backend: t10, attrs: { shape: s, value: n, dtype: e.dtype } }); + let a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer); + return pP(a, c, e.shape.length, Fe[e.dtype], d, f, n, u), i; } -var bg = { kernelName: $n, backendName: "wasm", kernelFunc: SJ, setupFunc: CJ }; -var wJ = false; -var nP = rt(An, wJ); -var sP; -function IJ(r) { - sP = r.wasm.cwrap(Rn, null, ["number", "number", "number"]); +var bg = { kernelName: En, backendName: "wasm", kernelFunc: zJ, setupFunc: VJ }; +var WJ = false; +var cP = rt(An, WJ); +var lP; +function UJ(r) { + lP = r.wasm.cwrap(Rn, null, ["number", "number", "number"]); } -function vJ(r) { - let { inputs: e, backend: t6 } = r, { x: o, alpha: n } = e, s = t6.dataIdMap.get(o.dataId).id, a = t6.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; - p.dtype !== "float32" && (u = ls({ backend: t6, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t6.dataIdMap.get(u.dataId).id); - let c = t6.makeOutput(o.shape, "float32"), l = t6.dataIdMap.get(c.dataId).id; - return sP(i, a, l), p.dtype !== "float32" && t6.disposeData(u.dataId), c; +function GJ(r) { + let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; + p.dtype !== "float32" && (u = ms({ backend: t10, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t10.dataIdMap.get(u.dataId).id); + let c = t10.makeOutput(o.shape, "float32"), l = t10.dataIdMap.get(c.dataId).id; + return lP(i, a, l), p.dtype !== "float32" && t10.disposeData(u.dataId), c; } -var aP = { kernelName: Rn, backendName: "wasm", setupFunc: IJ, kernelFunc: vJ }; -var iP; -function kJ(r) { - iP = r.wasm.cwrap(Fn, null, ["number", "number", "number", "number"]); +var mP = { kernelName: Rn, backendName: "wasm", setupFunc: UJ, kernelFunc: GJ }; +var dP; +function HJ(r) { + dP = r.wasm.cwrap(Dn, null, ["number", "number", "number", "number"]); } -function NJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; +function KJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; if (d) { let C = e.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C, f = S.getInnerMostAxes(f.length, u.shape.length)); @@ -24971,7 +24967,7 @@ function NJ(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - iP(p, x, Fe[b.dtype], C); + dP(p, x, Fe[b.dtype], C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -24979,125 +24975,125 @@ function NJ(r) { } return b; } -var uP = { kernelName: Fn, backendName: "wasm", setupFunc: kJ, kernelFunc: NJ }; -var TJ = (r) => { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, step: s, dtype: a } = t6, i = Iu(o, n, s, a), p = e.makeOutput([i.length], a); +var fP = { kernelName: Dn, backendName: "wasm", setupFunc: HJ, kernelFunc: KJ }; +var qJ = (r) => { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = _u(o, n, s, a), p = e.makeOutput([i.length], a); return e.typedArrayFromHeap(p).set(i), p; }; -var pP = { kernelName: ks, backendName: "wasm", kernelFunc: TJ }; -var _J = true; -var cP = rt(Jo, _J); -var lP = Ve(Dn); -var mP = Ve(On); -var dP = Ve(Ln); -var fP; -function EJ(r) { - fP = r.wasm.cwrap(Mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var hP = { kernelName: Ts, backendName: "wasm", kernelFunc: qJ }; +var jJ = true; +var gP = rt(Zo, jJ); +var xP = Ve(Fn); +var yP = Ve(On); +var bP = Ve(Ln); +var CP; +function XJ(r) { + CP = r.wasm.cwrap(Mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function $J(r) { - let { backend: e, inputs: t6, attrs: o } = r, { images: n } = t6, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g; - h.dtype !== "float32" && (g = ls({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId)); +function YJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g; + h.dtype !== "float32" && (g = ms({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId)); let x = h.id, b = e.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return b; let C = e.dataIdMap.get(b.dataId).id; - return fP(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b; + return CP(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b; } -var hP = { kernelName: Mn, backendName: "wasm", setupFunc: EJ, kernelFunc: $J }; -var gP; -function AJ(r) { - gP = r.wasm.cwrap(Pn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var SP = { kernelName: Mn, backendName: "wasm", setupFunc: XJ, kernelFunc: YJ }; +var wP; +function QJ(r) { + wP = r.wasm.cwrap(Pn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function RJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { images: n } = t6, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32"); +function ZJ(r) { + let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return h; let g = e.dataIdMap.get(n.dataId), x; - g.dtype !== "float32" && (x = ls({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId)); + g.dtype !== "float32" && (x = ms({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId)); let b = g.id, C = e.dataIdMap.get(h.dataId).id; - return gP(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && e.disposeData(x.dataId), h; + return wP(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && e.disposeData(x.dataId), h; } -var xP = { kernelName: Pn, backendName: "wasm", setupFunc: AJ, kernelFunc: RJ }; -var yP; -function FJ(r) { - yP = r.wasm.cwrap(Bn, null, ["number", "array", "number", "array", "number", "number"]); -} -function DJ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape); - if (n.shape.length === 0) - return Vu({ inputs: { x: n }, backend: t6 }); - let i = t6.makeOutput(n.shape, n.dtype), p = t6.dataIdMap.get(n.dataId).id, u = t6.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer); - yP(p, c, a.length, l, n.shape.length, u); - let m = Mt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t6 }); - return t6.disposeData(i.dataId), m; -} -var bP = { kernelName: Bn, backendName: "wasm", kernelFunc: DJ, setupFunc: FJ }; -var CP; -function OJ(r) { - CP = r.wasm.cwrap(es, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); -} -function PJ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t6.makeOutput(n.shape, n.dtype), u = t6.dataIdMap.get(n.dataId).id, c = t6.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = S.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], w = new Uint8Array(new Int32Array(C).buffer); - return CP(u, l, m, d, f, s, h, g, w, C.length, c), p; -} -var SP = { kernelName: es, backendName: "wasm", kernelFunc: PJ, setupFunc: OJ }; -var wP = Ve(Ca); -var IP = Ve(Vn); +var IP = { kernelName: Pn, backendName: "wasm", setupFunc: QJ, kernelFunc: ZJ }; var vP; -function MJ(r) { - vP = r.wasm.cwrap(zn, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); +function JJ(r) { + vP = r.wasm.cwrap(Bn, null, ["number", "array", "number", "array", "number", "number"]); } -function LJ(r) { - let { backend: e, inputs: t6, attrs: o } = r, { indices: n, updates: s } = t6, { shape: a } = o, i = e.makeOutput(a, s.dtype); +function eee(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape); + if (n.shape.length === 0) + return Hu({ inputs: { x: n }, backend: t10 }); + let i = t10.makeOutput(n.shape, n.dtype), p = t10.dataIdMap.get(n.dataId).id, u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer); + vP(p, c, a.length, l, n.shape.length, u); + let m = Lt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t10 }); + return t10.disposeData(i.dataId), m; +} +var kP = { kernelName: Bn, backendName: "wasm", kernelFunc: eee, setupFunc: JJ }; +var NP; +function tee(r) { + NP = r.wasm.cwrap(ts, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); +} +function ree(r) { + let { inputs: e, backend: t10, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(n.dataId).id, c = t10.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = S.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], w = new Uint8Array(new Int32Array(C).buffer); + return NP(u, l, m, d, f, s, h, g, w, C.length, c), p; +} +var TP = { kernelName: ts, backendName: "wasm", kernelFunc: ree, setupFunc: tee }; +var _P = Ve(Vn); +var $P = Ve(zn); +var EP; +function oee(r) { + EP = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); +} +function nee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { indices: n, updates: s } = t10, { shape: a } = o, i = e.makeOutput(a, s.dtype); if (y.sizeFromShape(a) === 0) return i; - let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = rl.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id; - return vP(f, g, Fe[s.dtype], p, u, c, x, m, b), i; + let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = sl.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id; + return EP(f, g, Fe[s.dtype], p, u, c, x, m, b), i; } -var kP = { kernelName: zn, backendName: "wasm", setupFunc: MJ, kernelFunc: LJ }; -var NP; -function BJ(r) { - NP = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); +var AP = { kernelName: Wn, backendName: "wasm", setupFunc: oee, kernelFunc: nee }; +var RP; +function see(r) { + RP = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); } -function VJ(r) { - let { inputs: e, backend: t6 } = r, { condition: o, t: n, e: s } = e, a = t6.dataIdMap.get(o.dataId).id, i = t6.dataIdMap.get(n.dataId).id, p = t6.dataIdMap.get(s.dataId).id, u = t6.makeOutput(n.shape, n.dtype), c = t6.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); - return NP(a, i, p, d, c), u; +function aee(r) { + let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = t10.dataIdMap.get(o.dataId).id, i = t10.dataIdMap.get(n.dataId).id, p = t10.dataIdMap.get(s.dataId).id, u = t10.makeOutput(n.shape, n.dtype), c = t10.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); + return RP(a, i, p, d, c), u; } -var TP = { kernelName: Ts, backendName: "wasm", kernelFunc: VJ, setupFunc: BJ }; -var _P; -function zJ(r) { - _P = r.wasm.cwrap(Un, null, ["number", "number"]); +var DP = { kernelName: $s, backendName: "wasm", kernelFunc: aee, setupFunc: see }; +var FP; +function iee(r) { + FP = r.wasm.cwrap(Gn, null, ["number", "number"]); } -function WJ(r) { - let { backend: e, inputs: { x: t6 } } = r, o = e.dataIdMap.get(t6.dataId).id, n = e.makeOutput(t6.shape, t6.dtype), s = e.dataIdMap.get(n.dataId).id; - return y.sizeFromShape(n.shape) === 0 || _P(o, s), n; +function uee(r) { + let { backend: e, inputs: { x: t10 } } = r, o = e.dataIdMap.get(t10.dataId).id, n = e.makeOutput(t10.shape, t10.dtype), s = e.dataIdMap.get(n.dataId).id; + return y.sizeFromShape(n.shape) === 0 || FP(o, s), n; } -var EP = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: zJ, kernelFunc: WJ }; -var $P = Ve(Wn); -var AP; -function UJ(r) { - AP = r.wasm.cwrap(qn, null, ["number", "number", "number", "number"]); +var OP = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: iee, kernelFunc: uee }; +var PP = Ve(Un); +var MP; +function pee(r) { + MP = r.wasm.cwrap(qn, null, ["number", "number", "number", "number"]); } -function GJ(r) { - let { backend: e, inputs: { logits: t6 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t6.dataId).id, s = e.makeOutput(t6.shape, t6.dtype), a = e.dataIdMap.get(s.dataId).id, i = t6.shape[o], p = y.sizeFromShape(t6.shape) / i; - return y.sizeFromShape(s.shape) === 0 || AP(n, a, i, p), s; +function cee(r) { + let { backend: e, inputs: { logits: t10 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t10.dataId).id, s = e.makeOutput(t10.shape, t10.dtype), a = e.dataIdMap.get(s.dataId).id, i = t10.shape[o], p = y.sizeFromShape(t10.shape) / i; + return y.sizeFromShape(s.shape) === 0 || MP(n, a, i, p), s; } -var RP = { kernelName: qn, backendName: "wasm", setupFunc: UJ, kernelFunc: GJ }; -function HJ(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; +var LP = { kernelName: qn, backendName: "wasm", setupFunc: pee, kernelFunc: cee }; +function lee(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = bg.kernelFunc({ inputs: { x: n }, backend: t6, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Mt({ inputs: { x: u }, backend: t6, attrs: { shape: c } }), b = uo({ inputs: { x: h }, backend: t6, attrs: { perm: l } }), k = Mt({ inputs: { x: b }, backend: t6, attrs: { shape: m } }); - return t6.disposeData(u.dataId), t6.disposeData(h.dataId), t6.disposeData(b.dataId), k; + let u = bg.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Lt({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = uo({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Lt({ inputs: { x: b }, backend: t10, attrs: { shape: m } }); + return t10.disposeData(u.dataId), t10.disposeData(h.dataId), t10.disposeData(b.dataId), k; } -var FP = { kernelName: Es, backendName: "wasm", kernelFunc: HJ }; -var DP; -function qJ(r) { - DP = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var BP = { kernelName: As, backendName: "wasm", kernelFunc: lee }; +var VP; +function mee(r) { + VP = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function KJ(r) { - let { backend: e, inputs: t6 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t6, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), C = e.dataIdMap.get(b.dataId).id, w = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(w.dataId).id, _ = e.makeOutput([4], "int32"), $ = e.dataIdMap.get(_.dataId).id, A = DP(l, m, Fe[n.dtype], i, u, p, d, h, x, C, k, $), R = e.readSync(_.dataId), D; +function dee(r) { + let { backend: e, inputs: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t10, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), C = e.dataIdMap.get(b.dataId).id, w = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(w.dataId).id, _ = e.makeOutput([4], "int32"), E = e.dataIdMap.get(_.dataId).id, A = VP(l, m, Fe[n.dtype], i, u, p, d, h, x, C, k, E), R = e.readSync(_.dataId), D; switch (R[0]) { case 1: { D = S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]); @@ -25118,13 +25114,13 @@ function KJ(r) { let P = f, M = g; return A !== c[0] && (P = Eo({ inputs: { x: f }, attrs: { begin: 0, size: [A, p] }, backend: e }), M = Eo({ inputs: { x: g }, attrs: { begin: 0, size: A }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [P, M, b, w]; } -var OP = { kernelName: ui, backendName: "wasm", setupFunc: qJ, kernelFunc: KJ }; -var PP; -function jJ(r) { - PP = r.wasm.cwrap(wa, null, ["number", "number", "number", "number", "number", "number", "number"]); +var zP = { kernelName: hi, backendName: "wasm", setupFunc: mee, kernelFunc: dee }; +var WP; +function fee(r) { + WP = r.wasm.cwrap(va, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function XJ(r) { - let { backend: e, inputs: t6 } = r, { inputIndices: o, inputShape: n, newShape: s } = t6; +function hee(r) { + let { backend: e, inputs: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = t10; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -25134,7 +25130,7 @@ function XJ(r) { if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); let a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = o.shape[0], c = y.sizeFromShape(s.shape), l = e.makeOutput([u, c], o.dtype), m = e.dataIdMap.get(l.dataId).id, d = e.makeOutput([c], s.dtype), f = e.dataIdMap.get(d.dataId).id, h = e.makeOutput([3], "int32"), g = e.dataIdMap.get(h.dataId).id; - PP(a, i, p, u, m, f, g); + WP(a, i, p, u, m, f, g); let x = e.readSync(h.dataId), b; switch (x[0]) { case 0: { @@ -25165,20 +25161,20 @@ function XJ(r) { throw e.disposeData(l.dataId), e.disposeData(d.dataId), new Error(b); return [l, d]; } -var MP = { kernelName: wa, backendName: "wasm", setupFunc: jJ, kernelFunc: XJ }; -var LP; +var UP = { kernelName: va, backendName: "wasm", setupFunc: fee, kernelFunc: hee }; +var GP; function Cg(r) { - LP = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); + GP = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); } function Sg(r, e) { - let { backend: t6, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t6.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; + let { backend: t10, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t10.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; if (c < 0) throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let l = n.shape.slice(); l[0] = c; - let m = t6.dataIdMap.get(n.dataId).id, d = t6.dataIdMap.get(s.dataId).id, f = t6.dataIdMap.get(a.dataId).id, h = t6.makeOutput(l, n.dtype), g = t6.dataIdMap.get(h.dataId).id, x = t6.makeOutput([4], "int32"), b = t6.dataIdMap.get(x.dataId).id; - LP(m, Fe[n.dtype], n.shape[0], d, f, g, b, e, 0); - let C = t6.readSync(x.dataId), w; + let m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = t10.dataIdMap.get(a.dataId).id, h = t10.makeOutput(l, n.dtype), g = t10.dataIdMap.get(h.dataId).id, x = t10.makeOutput([4], "int32"), b = t10.dataIdMap.get(x.dataId).id; + GP(m, Fe[n.dtype], n.shape[0], d, f, g, b, e, 0); + let C = t10.readSync(x.dataId), w; switch (C[0]) { case 0: { w = S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); @@ -25197,20 +25193,20 @@ function Sg(r, e) { default: w = ""; } - if (t6.disposeData(x.dataId), w) - throw t6.disposeData(h.dataId), new Error(w); + if (t10.disposeData(x.dataId), w) + throw t10.disposeData(h.dataId), new Error(w); return h; } -function YJ(r) { +function gee(r) { return Sg(r, true); } -var BP = { kernelName: pi, backendName: "wasm", setupFunc: Cg, kernelFunc: YJ }; -function QJ(r) { +var HP = { kernelName: gi, backendName: "wasm", setupFunc: Cg, kernelFunc: gee }; +function xee(r) { return Sg(r, false); } -var VP = { kernelName: ci, backendName: "wasm", setupFunc: Cg, kernelFunc: QJ }; -function ZJ(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t6, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var KP = { kernelName: xi, backendName: "wasm", setupFunc: Cg, kernelFunc: xee }; +function yee(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t10, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; @@ -25218,68 +25214,68 @@ function ZJ(r) { return u[i] += l, d; }); } -var zP = { kernelName: $s, backendName: "wasm", kernelFunc: ZJ }; -var WP = Ve(Gn); -var UP = Ve(mi); -var JJ = true; -var GP = rt(Kn, JJ); -var HP; -function eee(r) { - HP = r.wasm.cwrap(Ds, null, ["number", "number", "number", "number"]); +var qP = { kernelName: Rs, backendName: "wasm", kernelFunc: yee }; +var jP = Ve(Hn); +var XP = Ve(bi); +var bee = true; +var YP = rt(jn, bee); +var QP; +function Cee(r) { + QP = r.wasm.cwrap(fo, null, ["number", "number", "number", "number"]); } -function tee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { alpha: n } = o, { x: s } = t6, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id; - return HP(a, n, Fe[s.dtype], p), i; +function See(r) { + let { backend: e, inputs: t10, attrs: o } = r, { alpha: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id; + return QP(a, n, Fe[s.dtype], p), i; } -var qP = { kernelName: Ds, backendName: "wasm", setupFunc: eee, kernelFunc: tee }; -var KP; -function ree(r) { - KP = r.wasm.cwrap(jn, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); +var ZP = { kernelName: fo, backendName: "wasm", setupFunc: Cee, kernelFunc: See }; +var JP; +function wee(r) { + JP = r.wasm.cwrap(Xn, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); } -function oee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { x: n } = t6, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function Iee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = Mt({ inputs: { x: n }, backend: e, attrs: { shape: f } }); + k = Lt({ inputs: { x: n }, backend: e, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ut.computeOutShape(b, C, w), $ = Eo({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); - k = Mt({ inputs: { x: $ }, backend: e, attrs: { shape: f } }), e.disposeData($.dataId); + let _ = ut.computeOutShape(b, C, w), E = Eo({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); + k = Lt({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeData(E.dataId); } else { - let _ = e.makeOutput(d, "float32"), $ = e.dataIdMap.get(n.dataId).id, A = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), R = new Uint8Array(new Int32Array(b).buffer), D = new Uint8Array(new Int32Array(C).buffer), P = new Uint8Array(new Int32Array(w).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), W = e.dataIdMap.get(_.dataId).id; - KP($, A, n.shape.length, R, D, P, M, L, d.length, W), k = Mt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId); + let _ = e.makeOutput(d, "float32"), E = e.dataIdMap.get(n.dataId).id, A = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), R = new Uint8Array(new Int32Array(b).buffer), D = new Uint8Array(new Int32Array(C).buffer), P = new Uint8Array(new Int32Array(w).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), V = e.dataIdMap.get(_.dataId).id; + JP(E, A, n.shape.length, R, D, P, M, L, d.length, V), k = Lt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId); } return k; } -var jP = { kernelName: jn, backendName: "wasm", setupFunc: ree, kernelFunc: oee }; -function nee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { data: n, dataSplits: s } = t6, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = ku(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId); +var e3 = { kernelName: Xn, backendName: "wasm", setupFunc: wee, kernelFunc: Iee }; +function vee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { data: n, dataSplits: s } = t10, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = Eu(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId); x.stringBytes = f; let b = e.makeOutput(s.shape, "int32"); return e.typedArrayFromHeap(b).set(h), [g, b]; } -var XP = { kernelName: As, backendName: "wasm", kernelFunc: nee }; -function see(r) { - let { backend: e, inputs: t6, attrs: o } = r, { input: n, delimiter: s } = t6, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = Nu(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32"); +var t3 = { kernelName: Ds, backendName: "wasm", kernelFunc: vee }; +function kee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { input: n, delimiter: s } = t10, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = Au(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32"); e.typedArrayFromHeap(d).set(u); let h = e.makeOutput([m], "string"), g = e.dataIdMap.get(h.dataId); g.stringBytes = c; let x = e.makeOutput([2], "int32"); return e.typedArrayFromHeap(x).set(l), [d, h, x]; } -var YP = { kernelName: di, backendName: "wasm", kernelFunc: see }; -function aee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { input: n } = t6, { numBuckets: s } = o, a = e.readSync(n.dataId), i = Tu(a, s), p = e.makeOutput(n.shape, "int32"); +var r3 = { kernelName: Ci, backendName: "wasm", kernelFunc: kee }; +function Nee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { input: n } = t10, { numBuckets: s } = o, a = e.readSync(n.dataId), i = Ru(a, s), p = e.makeOutput(n.shape, "int32"); return e.typedArrayFromHeap(p).set(i), p; } -var QP = { kernelName: fi, backendName: "wasm", kernelFunc: aee }; -var iee = true; -var ZP = rt(Xn, iee); -var JP; -function uee(r) { - JP = r.wasm.cwrap(Hn, null, ["number", "number", "number", "number"]); +var o3 = { kernelName: Si, backendName: "wasm", kernelFunc: Nee }; +var Tee = true; +var n3 = rt(Yn, Tee); +var s3; +function _ee(r) { + s3 = r.wasm.cwrap(Kn, null, ["number", "number", "number", "number"]); } -function pee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t6, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; +function $ee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l; if (d) { let C = e.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C, f = S.getInnerMostAxes(f.length, u.shape.length)); @@ -25288,7 +25284,7 @@ function pee(r) { let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = e.dataIdMap.get(b.dataId).id; - JP(p, x, Fe[b.dtype], C); + s3(p, x, Fe[b.dtype], C); } if (d && e.disposeData(c.dataId), s) { let C = S.expandShapeToKeepDim(b.shape, m); @@ -25296,38 +25292,38 @@ function pee(r) { } return b; } -var e3 = { kernelName: Hn, backendName: "wasm", setupFunc: uee, kernelFunc: pee }; -var t3 = Ve(Yn); -var r3 = Ve(Qn); -var o3; -function cee(r) { - o3 = r.wasm.cwrap(to, null, ["number", "array", "number", "array", "number", "number"]); +var a3 = { kernelName: Kn, backendName: "wasm", setupFunc: _ee, kernelFunc: $ee }; +var i3 = Ve(Qn); +var u3 = Ve(Zn); +var p3; +function Eee(r) { + p3 = r.wasm.cwrap(to, null, ["number", "array", "number", "array", "number", "number"]); } -function lee(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, s = t6.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); +function Aee(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, s = t10.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); for (let m = 0; m < i.length; m++) i[m] = n.shape[m] * a[m]; - let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t6.makeOutput(i, n.dtype), l = t6.dataIdMap.get(c.dataId).id; - return o3(s, p, n.shape.length, u, i.length, Fe[c.dtype], l), c; + let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t10.makeOutput(i, n.dtype), l = t10.dataIdMap.get(c.dataId).id; + return p3(s, p, n.shape.length, u, i.length, Fe[c.dtype], l), c; } -var n3 = { kernelName: to, backendName: "wasm", setupFunc: cee, kernelFunc: lee }; -var s3; -function mee(r) { - s3 = r.wasm.cwrap(Zn, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); +var c3 = { kernelName: to, backendName: "wasm", setupFunc: Eee, kernelFunc: Aee }; +var l3; +function Ree(r) { + l3 = r.wasm.cwrap(Jn, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); } -var dee = ({ inputs: r, backend: e, attrs: t6 }) => { - let { x: o } = r, { k: n, sorted: s } = t6, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); +var Dee = ({ inputs: r, backend: e, attrs: t10 }) => { + let { x: o } = r, { k: n, sorted: s } = t10, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); p[p.length - 1] = n; let u = e.makeOutput(p, o.dtype), c = e.dataIdMap.get(u.dataId).id, l = e.makeOutput(p, "int32"), m = e.dataIdMap.get(l.dataId).id; - return s3(a, i, o.shape.length, Fe[o.dtype], n, s, c, m), [u, l]; + return l3(a, i, o.shape.length, Fe[o.dtype], n, s, c, m), [u, l]; }; -var a3 = { kernelName: Zn, backendName: "wasm", setupFunc: mee, kernelFunc: dee }; -var i3; -function fee(r) { - i3 = r.wasm.cwrap(Jn, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); +var m3 = { kernelName: Jn, backendName: "wasm", setupFunc: Ree, kernelFunc: Dee }; +var d3; +function Fee(r) { + d3 = r.wasm.cwrap(es, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); } -function hee(r) { - let { backend: e, inputs: t6, attrs: o } = r, { image: n, transforms: s } = t6, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = e.makeOutput(g, n.dtype), w = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, A = e.dataIdMap.get(s.dataId).id, R = a === "nearest" ? 1 : 2, D; +function Oee(r) { + let { backend: e, inputs: t10, attrs: o } = r, { image: n, transforms: s } = t10, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = e.makeOutput(g, n.dtype), w = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, A = e.dataIdMap.get(s.dataId).id, R = a === "nearest" ? 1 : 2, D; switch (i) { case "constant": D = 1; @@ -25345,11 +25341,11 @@ function hee(r) { D = 1; break; } - return i3(_, A, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, R, D, p, w), C; + return d3(_, A, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, R, D, p, w), C; } -var u3 = { kernelName: Jn, backendName: "wasm", setupFunc: fee, kernelFunc: hee }; -function gee(r) { - let { inputs: e, backend: t6, attrs: o } = r, { value: n } = e, { axis: s } = o; +var f3 = { kernelName: es, backendName: "wasm", setupFunc: Fee, kernelFunc: Oee }; +function Pee(r) { + let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape[s], i = n.shape.length, p = new Array(i - 1), u = 0; for (let d = 0; d < i; d++) @@ -25357,28 +25353,28 @@ function gee(r) { let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice(); m[s] = 1; for (let d = 0; d < c.length; d++) - l[s] = d, c[d] = Eo({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t6 }); + l[s] = d, c[d] = Eo({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t10 }); return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p })); } -var p3 = { kernelName: Rs, backendName: "wasm", kernelFunc: gee }; -function xee(r) { - let { inputs: { x: e }, backend: t6 } = r, o = t6.makeOutput(e.shape, e.dtype); - return t6.typedArrayFromHeap(o).fill(0), o; +var h3 = { kernelName: Fs, backendName: "wasm", kernelFunc: Pee }; +function Mee(r) { + let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype); + return t10.typedArrayFromHeap(o).fill(0), o; } -var c3 = { kernelName: Fs, backendName: "wasm", kernelFunc: xee }; -var yee = [dD, fD, hD, xD, wD, vD, ND, _D, AD, FD, DD, OD, MD, LD, VD, WD, UD, GD, qD, jD, YD, ZD, eO, tO, rO, oO, nO, sO, iO, uO, pO, lO, dO, hO, xO, bO, CO, SO, yD, wO, vO, kO, NO, TO, _O, EO, $O, AO, FO, DO, PO, LO, VO, zO, UO, GO, HO, KO, XO, QO, ZO, eP, tP, rP, bg, nP, aP, uP, pP, cP, lP, mP, dP, ED, hP, xP, bP, SP, wP, IP, kP, TP, EP, $P, RD, RP, FP, OP, MP, BP, VP, zP, WP, UP, GP, qP, jP, XP, YP, QP, ZP, e3, t3, r3, n3, a3, u3, CD, p3, c3]; -for (let r of yee) - Ia(r); -var Hw = O(); -Hw.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { +var g3 = { kernelName: Os, backendName: "wasm", kernelFunc: Mee }; +var Lee = [bF, CF, SF, IF, _F, EF, RF, FF, MF, BF, VF, zF, UF, GF, KF, jF, XF, YF, ZF, eO, rO, nO, aO, iO, uO, pO, cO, lO, dO, fO, hO, xO, bO, SO, IO, kO, NO, TO, vF, _O, EO, AO, RO, DO, FO, OO, PO, MO, BO, VO, WO, GO, KO, qO, XO, YO, QO, JO, tP, oP, nP, aP, iP, uP, bg, cP, mP, fP, hP, gP, xP, yP, bP, OF, SP, IP, kP, TP, _P, $P, AP, DP, OP, PP, LF, LP, BP, zP, UP, HP, KP, qP, jP, XP, YP, ZP, e3, t3, r3, o3, n3, a3, i3, u3, c3, m3, f3, NF, h3, g3]; +for (let r of Lee) + ka(r); +var rI = O(); +rI.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { try { return WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])); } catch (r) { return false; } }); -Hw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { - if (Hw.get("IS_NODE")) +rI.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (rI.get("IS_NODE")) return false; try { return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11])); @@ -25386,51 +25382,51 @@ Hw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { return false; } }); -var Zw = rp(f3()); -var C3 = rp(g3()); -var Jw = rp(x3()); -var y3 = Zw.default || Zw; -var bee = Jw.default || Jw; -var Pl = class extends Zr { +var cI = ap(C3()); +var N3 = ap(w3()); +var lI = ap(I3()); +var v3 = cI.default || cI; +var Bee = lI.default || lI; +var Ll = class extends Zr { constructor(e) { - super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(w3), Qw = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Do(this, cr()); + super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(_3), pI = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Fo(this, sr()); } - write(e, t6, o) { + write(e, t10, o) { let n = { id: this.dataIdNextNumber++ }; - return this.move(n, e, t6, o, 1), n; + return this.move(n, e, t10, o, 1), n; } numDataIds() { return this.dataIdMap.numDataIds(); } async time(e) { - let t6 = y.now(); - return e(), { kernelMs: y.now() - t6 }; + let t10 = y.now(); + return e(), { kernelMs: y.now() - t10 }; } - move(e, t6, o, n, s) { + move(e, t10, o, n, s) { let a = this.dataIdNextNumber++; if (n === "string") { - let c = t6; + let c = t10; this.dataIdMap.set(e, { id: a, stringBytes: c, shape: o, dtype: n, memoryOffset: null, refCount: s }); return; } let i = y.sizeFromShape(o), p = i * y.bytesPerElement(n), u = this.wasm._malloc(p); - this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t6 != null && this.wasm.HEAPU8.set(new Uint8Array(t6.buffer, t6.byteOffset, p), u); + this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t10 != null && this.wasm.HEAPU8.set(new Uint8Array(t10.buffer, t10.byteOffset, p), u); } async read(e) { return this.readSync(e); } - readSync(e, t6, o) { + readSync(e, t10, o) { let { memoryOffset: n, dtype: s, shape: a, stringBytes: i } = this.dataIdMap.get(e); if (s === "string") - return (t6 == null || t6 === 0) && (o == null || o >= i.length) ? i : i.slice(t6, o); - t6 = t6 || 0, o = o || y.sizeFromShape(a); - let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t6 * p, n + o * p); - return See(u.buffer, s); + return (t10 == null || t10 === 0) && (o == null || o >= i.length) ? i : i.slice(t10, o); + t10 = t10 || 0, o = o || y.sizeFromShape(a); + let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t10 * p, n + o * p); + return zee(u.buffer, s); } - disposeData(e, t6 = false) { + disposeData(e, t10 = false) { if (this.dataIdMap.has(e)) { let o = this.dataIdMap.get(e); - if (o.refCount--, !t6 && o.refCount > 0) + if (o.refCount--, !t10 && o.refCount > 0) return false; this.wasm._free(o.memoryOffset), this.wasm.tfjs.disposeData(o.id), this.dataIdMap.delete(e); } @@ -25440,8 +25436,8 @@ var Pl = class extends Zr { return this.dataIdMap.has(e) ? this.dataIdMap.get(e).refCount : 0; } incRef(e) { - let t6 = this.dataIdMap.get(e); - t6 != null && t6.refCount++; + let t10 = this.dataIdMap.get(e); + t10 != null && t10.refCount++; } floatPrecision() { return 32; @@ -25455,21 +25451,21 @@ var Pl = class extends Zr { memory() { return { unreliable: false }; } - makeOutput(e, t6, o) { + makeOutput(e, t10, o) { let n; if (o == null) - n = this.write(null, e, t6); + n = this.write(null, e, t10); else { let s = this.dataIdNextNumber++; - n = { id: s }, this.dataIdMap.set(n, { id: s, memoryOffset: o, shape: e, dtype: t6, refCount: 1 }); + n = { id: s }, this.dataIdMap.set(n, { id: s, memoryOffset: o, shape: e, dtype: t10, refCount: 1 }); let a = y.sizeFromShape(e); this.wasm.tfjs.registerTensor(s, a, o); } - return { dataId: n, shape: e, dtype: t6 }; + return { dataId: n, shape: e, dtype: t10 }; } - typedArrayFromHeap({ shape: e, dtype: t6, dataId: o }) { + typedArrayFromHeap({ shape: e, dtype: t10, dataId: o }) { let n = this.wasm.HEAPU8.buffer, { memoryOffset: s } = this.dataIdMap.get(o), a = y.sizeFromShape(e); - switch (t6) { + switch (t10) { case "float32": return new Float32Array(n, s, a); case "int32": @@ -25477,51 +25473,51 @@ var Pl = class extends Zr { case "bool": return new Uint8Array(n, s, a); default: - throw new Error(`Unknown dtype ${t6}`); + throw new Error(`Unknown dtype ${t10}`); } } }; -function Cee(r) { - return (e, t6) => (y.fetch(r, { credentials: "same-origin" }).then((o) => { +function Vee(r) { + return (e, t10) => (y.fetch(r, { credentials: "same-origin" }).then((o) => { o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => { WebAssembly.instantiate(n, e).then((s) => { - t6(s.instance, s.module); + t10(s.instance, s.module); }); }); }), {}); } -function b3(r, e, t6) { +function k3(r, e, t10) { if (vg != null) return vg; let o = "tfjs-backend-wasm.wasm"; - return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), Dl != null && Dl[o] != null ? Dl[o] : t6 + o; + return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), Pl != null && Pl[o] != null ? Pl[o] : t10 + o; } -async function S3() { +async function T3() { let [r, e] = await Promise.all([O().getAsync("WASM_HAS_SIMD_SUPPORT"), O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]); - return new Promise((t6, o) => { + return new Promise((t10, o) => { let n = {}; n.locateFile = (i, p) => { if (i.endsWith(".worker.js")) { - let u = C3.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); + let u = N3.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); return URL.createObjectURL(c); } - return i.endsWith(".wasm") ? b3(r, e, Fl != null ? Fl : p) : p + i; - }, eI && (n.instantiateWasm = Cee(b3(r, e, Fl != null ? Fl : ""))); + return i.endsWith(".wasm") ? k3(r, e, Ol != null ? Ol : p) : p + i; + }, mI && (n.instantiateWasm = Vee(k3(r, e, Ol != null ? Ol : ""))); let s = false; n.onAbort = () => { - if (s || Ol) + if (s || Ml) return; - Ol = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" }); + Ml = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" }); }; let a; - e && r && vg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + y3.toString()], { type: "text/javascript" }), a = y3(n)) : a = bee(n), a.then((i) => { - s = true, Ol = false; + e && r && vg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + v3.toString()], { type: "text/javascript" }), a = v3(n)) : a = Bee(n), a.then((i) => { + s = true, Ml = false; let p = null; - i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t6({ wasm: i }); + i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t10({ wasm: i }); }).catch(o); }); } -function See(r, e) { +function zee(r, e) { switch (e) { case "float32": return new Float32Array(r); @@ -25533,58 +25529,58 @@ function See(r, e) { throw new Error(`Unknown dtype ${e}`); } } -var wee = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"]; +var Wee = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"]; var vg = null; -var Fl = null; -var Dl = {}; -var Ol = false; -var eI = false; -function Iee(r, e = false) { - if (eC("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Ol) +var Ol = null; +var Pl = {}; +var Ml = false; +var mI = false; +function Uee(r, e = false) { + if (nC("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Ml) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`"); - vg = r, eI = e; + vg = r, mI = e; } -function vee(r, e = false) { - if (Ol) +function Gee(r, e = false) { + if (Ml) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`"); if (typeof r == "string") - Fl = r; + Ol = r; else { - Dl = r; - let t6 = wee.filter((o) => Dl[o] == null); - if (t6.length > 0) - throw new Error(`There were no entries found for the following binaries: ${t6.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`); + Pl = r; + let t10 = Wee.filter((o) => Pl[o] == null); + if (t10.length > 0) + throw new Error(`There were no entries found for the following binaries: ${t10.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`); } - eI = e; + mI = e; } -var w3 = -1; -var Qw = -1; -function kee(r) { - w3 = r; +var _3 = -1; +var pI = -1; +function Hee(r) { + _3 = r; } -function Nee() { - if (Qw === -1) +function Kee() { + if (pI === -1) throw new Error("WASM backend not initialized."); - return Qw; + return pI; } -var Tee = "4.1.0"; -var _ee = 2; -Ci("wasm", async () => { - let { wasm: r } = await S3(); - return new Pl(r); -}, _ee); -var ms = O(); -ms.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); -ms.registerFlag("WEBGPU_CPU_FORWARD", () => true); -ms.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); -ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false); -ms.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); -ms.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); -ms.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); -ms.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); -ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); -ms.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0); -ms.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); +var qee = "4.2.0"; +var jee = 2; +Ti("wasm", async () => { + let { wasm: r } = await T3(); + return new Ll(r); +}, jee); +var ds = O(); +ds.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); +ds.registerFlag("WEBGPU_CPU_FORWARD", () => true); +ds.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); +ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false); +ds.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); +ds.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); +ds.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); +ds.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); +ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); +ds.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0); +ds.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); var kg = class { constructor(e) { e && (this.vendor = e.vendor, this.architecture = e.architecture, this.intelGPUGeneration = this.getIntelGPUGeneration()); @@ -25606,33 +25602,33 @@ var Ng = class { constructor(e) { this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } - acquireUploadBuffer(e, t6) { - return this.acquireBuffer(e, t6, true); + acquireUploadBuffer(e, t10) { + return this.acquireBuffer(e, t10, true); } - acquireBuffer(e, t6, o = false) { - let n = I3(e, t6); + acquireBuffer(e, t10, o = false) { + let n = $3(e, t10); if (this.freeBuffers.has(n) || this.freeBuffers.set(n, []), this.usedBuffers.has(n) || this.usedBuffers.set(n, []), this.numBytesUsed += e, this.numUsedBuffers++, this.freeBuffers.get(n).length > 0) { this.numFreeBuffers--; let a = this.freeBuffers.get(n).shift(); return this.usedBuffers.get(n).push(a), a; } this.numBytesAllocated += e; - let s = this.device.createBuffer({ size: e, usage: t6, mappedAtCreation: o }); + let s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o }); return this.usedBuffers.get(n).push(s), s; } - releaseBuffer(e, t6, o) { + releaseBuffer(e, t10, o) { if (this.freeBuffers.size === 0) return; - let n = I3(t6, o); + let n = $3(t10, o); this.freeBuffers.has(n) || this.freeBuffers.set(n, []), this.freeBuffers.get(n).push(e), this.numFreeBuffers++, this.numUsedBuffers--; let s = this.usedBuffers.get(n), a = s.indexOf(e); if (a < 0) throw new Error("Cannot release a buffer that was never provided by this buffer manager"); - s.splice(a, 1), this.numBytesUsed -= t6; + s.splice(a, 1), this.numBytesUsed -= t10; } - releaseUploadBuffer(e, t6, o) { + releaseUploadBuffer(e, t10, o) { e.mapAsync(GPUMapMode.WRITE).then(() => { - this.releaseBuffer(e, t6, o); + this.releaseBuffer(e, t10, o); }, (n) => { }); } @@ -25643,45 +25639,45 @@ var Ng = class { return this.numFreeBuffers; } dispose() { - this.freeBuffers.forEach((e, t6) => { + this.freeBuffers.forEach((e, t10) => { e.forEach((o) => { o.destroy(); }); - }), this.usedBuffers.forEach((e, t6) => { + }), this.usedBuffers.forEach((e, t10) => { e.forEach((o) => { o.destroy(); }); }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function I3(r, e) { +function $3(r, e) { return `${r}_${e}`; } var Tg = class { constructor(e) { this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } - acquireTexture(e, t6, o, n) { - let s = k3(o), a = e * t6 * s, i = v3(e, t6, o, n); + acquireTexture(e, t10, o, n) { + let s = A3(o), a = e * t10 * s, i = E3(e, t10, o, n); if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) { this.numFreeTextures--; let u = this.freeTextures.get(i).shift(); return this.usedTextures.get(i).push(u), u; } this.numBytesAllocated += a; - let p = this.device.createTexture({ size: [e, t6], format: o, usage: n }); + let p = this.device.createTexture({ size: [e, t10], format: o, usage: n }); return this.usedTextures.get(i).push(p), p; } - releaseTexture(e, t6, o, n, s) { + releaseTexture(e, t10, o, n, s) { if (this.freeTextures.size === 0) return; - let a = v3(t6, o, n, s); + let a = E3(t10, o, n, s); this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--; let i = this.usedTextures.get(a), p = i.indexOf(e); if (p < 0) throw new Error("Cannot release a texture that was never provided by this texture manager"); i.splice(p, 1); - let u = k3(n), c = t6 * o * u; + let u = A3(n), c = t10 * o * u; this.numBytesUsed -= c; } getNumUsedTextures() { @@ -25691,36 +25687,49 @@ var Tg = class { return this.numFreeTextures; } dispose() { - this.freeTextures.forEach((e, t6) => { + this.freeTextures.forEach((e, t10) => { e.forEach((o) => { o.destroy(); }); - }), this.usedTextures.forEach((e, t6) => { + }), this.usedTextures.forEach((e, t10) => { e.forEach((o) => { o.destroy(); }); }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function v3(r, e, t6, o) { - return `${r}_${e}_${t6}_${o}`; +function E3(r, e, t10, o) { + return `${r}_${e}_${t10}_${o}`; } -function k3(r) { +function A3(r) { if (r === "rgba8unorm") return 16; throw new Error(`${r} is not supported!`); } -function N3(r, e) { +function R3(r, e) { if (Math.max(...r) > 3) throw new Error("Cannot symbolically compute strides for rank > 4 tensor."); - let t6 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t6 - 1); - n[t6 - 2] = o[t6 - 1]; - for (let s = t6 - 3; s >= 0; --s) + let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1); + n[t10 - 2] = o[t10 - 1]; + for (let s = t10 - 3; s >= 0; --s) n[s] = `(${n[s + 1]} * ${o[s + 1]})`; return n; } -var A3 = (r, e, t6, o) => { - let n = { dtype: o.dtype, shape: o.shape }, s = $ee(t6, n, e), a = r.createShaderModule({ code: s, label: e.constructor.name }); +var Ic = (r, e, t10) => t10 === "int32" ? `atomicAdd(${r}, bitcast(${e}));` : ` + { + var oldValue = 0; + loop { + let newValueF32 = bitcast(oldValue) + (${e}); + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(${r}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`; +var M3 = (r, e, t10, o) => { + let n = { dtype: o.dtype, shape: o.shape }, s = Yee(t10, n, e), a = r.createShaderModule({ code: s, label: e.constructor.name }); return r.createComputePipeline({ compute: { module: a, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }); }; function Rt(r) { @@ -25738,7 +25747,7 @@ function Rt(r) { return "vec6"; throw Error(`GPU for rank ${r} is not yet supported`); } -function $o(r) { +function Ao(r) { if (r === 0) return "x"; if (r === 1) @@ -25753,7 +25762,7 @@ function $o(r) { return "v"; throw Error(`Index ${r} is not yet supported`); } -function se(...r) { +function Q(...r) { let e; switch (r.length) { case 0: @@ -25771,10 +25780,10 @@ function se(...r) { } return e; } -function T3(r) { - let e; - return e = ` - ${Eee()} +function D3(r, e) { + let t10; + return t10 = ` + ${Xee(e)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @@ -25787,19 +25796,16 @@ function T3(r) { workgroupId = WorkgroupId; ${r ? "main(getGlobalIndex());" : "main();"}; } - `, e; + `, t10; } -function Eee() { +function Xee(r) { return ` - @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ) + @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]}) `; } -function $ee(r, e, t6) { - let o = [], n = t6.workgroupSize[0] * t6.workgroupSize[1] * t6.workgroupSize[2]; +function Yee(r, e, t10) { + let o = [], n = t10.workgroupSize[0] * t10.workgroupSize[1] * t10.workgroupSize[2]; if (o.push(` - const workgroupSizeX = ${t6.workgroupSize[0]}u; - const workgroupSizeY = ${t6.workgroupSize[1]}u; - const workgroupSizeZ = ${t6.workgroupSize[2]}u; var localId: vec3; var localIndex: u32; @@ -25809,12 +25815,12 @@ function $ee(r, e, t6) { // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${F3(t6) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + - workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} + + ${B3(t10) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } - `), t6.isFromPixels) { + `), t10.isFromPixels) { o.push(` struct Uniform { size : i32, @@ -25822,16 +25828,16 @@ function $ee(r, e, t6) { outShapeStrides : vec2, }; - @group(0) @binding(0) var result: array<${wc(e.dtype, t6.isVec4)}>; + @group(0) @binding(0) var result: array<${vc(e.dtype, t10.isVec4)}>; @group(0) @binding(2) var uniforms: Uniform; `); - let f = $3(t6); - return [_3, o.join(` -`), E3(e.shape), t6.getUserCode(), T3(f)].join(` + let f = P3(t10); + return [F3, o.join(` +`), O3(e.shape), t10.getUserCode(), D3(f, t10)].join(` `); } let s = "struct Uniforms { NAN : f32, INFINITY : f32, "; - t6.variableNames.forEach((f, h) => { + t10.variableNames.forEach((f, h) => { let g = Rt(r[h].shape.length); s += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `; }); @@ -25839,35 +25845,35 @@ function $ee(r, e, t6) { s += `outShape : ${a}, `; let i = e.shape.length - 1, p = Rt(i); s += ` - outShapeStrides: ${p}, `, t6.size && (s += "size : i32, "), t6.uniforms && (s += t6.uniforms), s += "};", s = Lee(s), o.push(s), t6.atomic ? o.push(` + outShapeStrides: ${p}, `, t10.size && (s += "size : i32, "), t10.uniforms && (s += t10.uniforms), s += "};", s = nte(s), o.push(s), t10.atomic ? o.push(` @group(0) @binding(0) var result: array>; `) : o.push(` - @group(0) @binding(0) var result: array<${wc(e.dtype, t6.isVec4)}>; - `), t6.variableNames.forEach((f, h) => { + @group(0) @binding(0) var result: array<${vc(e.dtype, t10.isVec4)}>; + `), t10.variableNames.forEach((f, h) => { o.push(` - @group(0) @binding(${1 + h}) var ${f}: array<${t6.variableTypes ? t6.variableTypes[h] : wc(r[h].dtype, t6.isVec4)}>; + @group(0) @binding(${1 + h}) var ${f}: array<${t10.variableTypes ? t10.variableTypes[h] : vc(r[h].dtype, t10.isVec4)}>; `); }), s !== "" && o.push(` - @group(0) @binding(${1 + t6.variableNames.length}) var uniforms: Uniforms; + @group(0) @binding(${1 + t10.variableNames.length}) var uniforms: Uniforms; `); - let u = Oee(e.shape, t6.dispatchLayout), c = [_3 + Aee, o.join(` -`), E3(e.shape), u, Pee(e.shape.length)]; - t6.atomic || c.push(Mee(e.shape, e.dtype, t6.isVec4)); - let l = r.map((f, h) => Dee(f, e.shape, t6.variableTypes ? t6.variableTypes[h] === "vec4" : t6.isVec4, t6.dispatchLayout.x.length === e.shape.length)).join(` + let u = tte(e.shape, t10.dispatchLayout), c = [F3, o.join(` +`) + Qee, O3(e.shape), u, rte(e.shape.length)]; + t10.atomic || c.push(ote(e.shape, e.dtype, t10.isVec4)); + let l = r.map((f, h) => ete(f, e.shape, t10.variableTypes ? t10.variableTypes[h] === "vec4" : t10.isVec4, t10.dispatchLayout.x.length === e.shape.length)).join(` `); - c.push(l), c.push(t6.getUserCode()); - let m = $3(t6); - return c.push(T3(m)), c.join(` + c.push(l), c.push(t10.getUserCode()); + let m = P3(t10); + return c.push(D3(m, t10)), c.join(` `); } -function R3(r, e, t6, o) { +function L3(r, e, t10, o) { let n = r.shaderKey; if (r.isFromPixels) return n; - let s = t6.map((c) => c.dtype).concat(o.dtype), a = t6.map((c) => S.getBroadcastDims(c.shape, o.shape)), i = t6.map((c) => y.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = F3(r) ? "flatDispatch" : ""; + let s = t10.map((c) => c.dtype).concat(o.dtype), a = t10.map((c) => S.getBroadcastDims(c.shape, o.shape)), i = t10.map((c) => y.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = B3(r) ? "flatDispatch" : ""; return n += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + e.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, n; } -var _3 = ` +var F3 = ` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -25923,29 +25929,30 @@ var _3 = ` return (floatToUint & 0x7fffffffu) > 0x7f800000u; } fn isnanVec4(val : vec4) -> vec4 { - return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3])); + let floatToUint: vec4 = bitcast>(val); + return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } `; -var Aee = ` +var Qee = ` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } `; -function E3(r) { +function O3(r) { let e = r.length; if (e <= 1) return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }"; - let t6 = y.computeStrides(r), o = Rt(e), n = []; + let t10 = y.computeStrides(r), o = Rt(e), n = []; for (let a = 0; a < e; a++) n.push(`d${a}`); - if (t6.length === 1) + if (t10.length === 1) return ` fn getCoordsFromIndex(index : i32) -> vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); }`; let s; - return s = "var index2 = index;" + t6.map((a, i) => { - let p = `let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`, u = i === t6.length - 1 ? `let ${n[i + 1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}` : `index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`; + return s = "var index2 = index;" + t10.map((a, i) => { + let p = `let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`, u = i === t10.length - 1 ? `let ${n[i + 1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}` : `index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`; return `${p}; ${u};`; }).join(""), ` fn getCoordsFromIndex(index : i32) -> ${o} { @@ -25954,35 +25961,35 @@ function E3(r) { } `; } -function Ree(r, e) { - let t6 = r.name, o = r.shape.length, n = Rt(o), s = "get" + t6.charAt(0).toUpperCase() + t6.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); +function Zee(r, e) { + let t10 = r.name, o = r.shape.length, n = Rt(o), s = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); if (o < 1) return e ? ` fn ${s}() -> vec4 { - return vec4(${t6}[0]); + return vec4(${t10}[0]); } ` : ` fn ${s}() ->f32 { - return f32(${t6}[0]); + return f32(${t10}[0]); } `; - let p = `uniforms.${t6.charAt(0).toLowerCase() + t6.slice(1)}Shape`, u = `${o}D`; + let p = `uniforms.${t10.charAt(0).toLowerCase() + t10.slice(1)}Shape`, u = `${o}D`; return o === 0 && (u = "1D"), e ? ` fn ${s}(${i}) -> vec4 { - return vec4(${t6}[getIndexFromCoords${u}(${n}(${a.join(",")}), + return vec4(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p}) / 4]); } ` : ` fn ${s}(${i}) -> f32 { - return f32(${t6}[getIndexFromCoords${u}(${n}(${a.join(",")}), + return f32(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})]); } `; } -function Fee(r, e, t6, o) { +function Jee(r, e, t10, o) { let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = e.length, u = Rt(p); if (y.arraysEqual(r.shape, e) && o) - return t6 ? ` + return t10 ? ` fn ${a}Index(globalIndex : i32) -> vec4 { return vec4(${n}[globalIndex]); } @@ -26001,7 +26008,7 @@ function Fee(r, e, t6, o) { `; let c = S.getBroadcastDims(r.shape, e), l = p - i, m = ""; if (i === 0) - return t6 ? ` + return t10 ? ` fn ${a}Index(globalIndex : i32) -> vec4 { return get${s}(); } @@ -26018,18 +26025,18 @@ function Fee(r, e, t6, o) { return get${s}(); } `; - p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${$o(g + l)} = 0;`).join(` + p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Ao(g + l)} = 0;`).join(` `); let d = ""; if (p < 2 && i > 0) d = "coords"; else if (p > 1) { - let g = Rt(i), x = r.shape.map((b, C) => `coords.${$o(C + l)}`).join(", "); + let g = Rt(i), x = r.shape.map((b, C) => `coords.${Ao(C + l)}`).join(", "); d = `${g}(${x})`; } else d = "coords"; let f = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, h = `${i}D`; - return t6 ? ` + return t10 ? ` fn ${a}Index(globalIndex : i32) -> vec4 { var coords = getCoordsFromIndex(globalIndex); ${m} @@ -26055,28 +26062,28 @@ function Fee(r, e, t6, o) { } `; } -function Dee(r, e, t6, o) { - let n = Ree(r, t6); - return r.shape.length <= e.length && (n += Fee(r, e, t6, o)), n; +function ete(r, e, t10, o) { + let n = Zee(r, t10); + return r.shape.length <= e.length && (n += Jee(r, e, t10, o)), n; } -function Oee(r, e) { - let { x: t6, y: o = [], z: n = [] } = e, s = r.length, a = t6.length + o.length + n.length; +function tte(r, e) { + let { x: t10, y: o = [], z: n = [] } = e, s = r.length, a = t10.length + o.length + n.length; if (a !== s) return ""; - if (t6.length === s) + if (t10.length === s) return `fn getOutputCoords() -> ${Rt(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } `; - let i = "", p = [t6, o, n]; + let i = "", p = [t10, o, n]; for (let m = 0; m < p.length; m++) { let d = p[m]; if (d.length !== 0) if (d.length === 1) i += `let d${d[0]} = i32(globalId[${m}]);`; else { - let f = N3(d, "uniforms.outShape"); + let f = R3(d, "uniforms.outShape"); i += `var index${m} = i32(globalId[${m}]);`; for (let h = 0; h < f.length; h++) i += `let d${d[h]} = index${m} / ${f[h]};`, h === f.length - 1 ? i += `let d${d[h + 1]} = index${m} - d${d[h]} * ${f[h]};` : i += `index${m} = index${m} - d${d[h]} * ${f[h]};`; @@ -26090,7 +26097,7 @@ function Oee(r, e) { `; return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l; } -function Pee(r) { +function rte(r) { let e = ""; switch (r) { case 0: @@ -26152,15 +26159,15 @@ function Pee(r) { } return e; } -function F3(r) { +function B3(r) { return r.dispatch[1] === 1 && r.dispatch[2] === 1; } -function wc(r, e) { +function vc(r, e) { return r === "float32" ? e ? "vec4" : "f32" : r === "int32" || r === "bool" ? e ? "vec4" : "i32" : r; } -function Mee(r, e, t6) { - let o = r.length, n = wc(e, t6), s; - if (t6 ? s = `fn setOutputAtIndex(flatIndex : i32, value : vec4) { +function ote(r, e, t10) { + let o = r.length, n = vc(e, t10), s; + if (t10 ? s = `fn setOutputAtIndex(flatIndex : i32, value : vec4) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : vec4) { @@ -26172,7 +26179,7 @@ function Mee(r, e, t6) { result[flatIndex] = ${n}(value); }`, o >= 2) { let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = Rt(o); - t6 ? s += ` + t10 ? s += ` fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex / 4, value); @@ -26194,91 +26201,87 @@ function Mee(r, e, t6) { } return s; } -function Lee(r) { +function nte(r) { let e = /(\w+)\s*:\s*vec(5|6)/g; r = r.replace(e, (o) => "@align(16) " + o); - let t6 = /vec(5|6)\s*,\s*(\w+)/g; - return r = r.replace(t6, (o, n, s) => `vec${n}, @align(16) ${s}`), r; + let t10 = /vec(5|6)\s*,\s*(\w+)/g; + return r = r.replace(t10, (o, n, s) => `vec${n}, @align(16) ${s}`), r; } -function $3(r) { +function P3(r) { return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0); } -var nI = {}; -Ue(nI, { ArrayBufferToTypedArray: () => oI, GPUBytesPerElement: () => rI, MatMulProgramType: () => Ao, computeDispatch: () => re, computeWorkPerThreadForConv2d: () => Ll, computeWorkgroupInfoForMatMul: () => tI, computeWorkgroupSizeForConv2d: () => Ml, flatDispatchLayout: () => ue, isWebGPUSupported: () => Bl, tilesFitEvenlyIntoShape: () => Vee }); -var zu = (r) => { +var hI = {}; +Ge(hI, { GPUBytesPerElement: () => _g, MatMulProgramType: () => Ro, assertNotComplex: () => fI, computeDispatch: () => Y, computeWorkPerThreadForConv2d: () => Vl, computeWorkgroupInfoForMatMul: () => dI, computeWorkgroupSizeForConv2d: () => Bl, flatDispatchLayout: () => ae, isWebGPUSupported: () => zl, tilesFitEvenlyIntoShape: () => ate }); +var Ku = (r) => { let e = 1; - for (let t6 = 0; t6 < r.length; t6++) - e *= r[t6]; + for (let t10 = 0; t10 < r.length; t10++) + e *= r[t10]; return e; }; -function Vee(r, e) { +function ate(r, e) { if (r.length !== e.length) throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`); - return e.every((t6, o) => t6 % r[o] === 0); + return e.every((t10, o) => t10 % r[o] === 0); } -function re(r, e, t6 = [1, 1, 1], o = [1, 1, 1]) { - let [n, s, a] = [Math.ceil(zu(r.x.map((i) => e[i])) / (t6[0] * o[0])), r.y ? Math.ceil(zu(r.y.map((i) => e[i])) / (t6[1] * o[1])) : 1, r.z ? Math.ceil(zu(r.z.map((i) => e[i])) / (t6[2] * o[2])) : 1]; +function Y(r, e, t10 = [1, 1, 1], o = [1, 1, 1]) { + let [n, s, a] = [Math.ceil(Ku(r.x.map((i) => e[i])) / (t10[0] * o[0])), r.y ? Math.ceil(Ku(r.y.map((i) => e[i])) / (t10[1] * o[1])) : 1, r.z ? Math.ceil(Ku(r.z.map((i) => e[i])) / (t10[2] * o[2])) : 1]; return [n, s, a]; } -function tI(r, e, t6, o = false) { +function dI(r, e, t10, o = false) { let n = [8, 8, 1], s = [4, 4, 1]; - return o || (r <= 8 && (s[1] = 1), e <= 16 && t6 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; + return o || (r <= 8 && (s[1] = 1), e <= 16 && t10 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; } -function Ml(r, e, t6 = false) { - if (t6) +function Bl(r, e, t10 = false) { + if (t10) return [8, 8, 1]; - let o = zu(r.x.map((s) => e[s])), n = zu(r.y.map((s) => e[s])); + let o = Ku(r.x.map((s) => e[s])), n = Ku(r.y.map((s) => e[s])); return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1]; } -function Ll(r, e, t6 = false) { - if (t6) +function Vl(r, e, t10 = false) { + if (t10) return [4, 4, 1]; - let o = zu(r.x.map((s) => e[s])), n = zu(r.y.map((s) => e[s])); + let o = Ku(r.x.map((s) => e[s])), n = Ku(r.y.map((s) => e[s])); return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1]; } -function ue(r) { - return { x: r.map((e, t6) => t6) }; +function ae(r) { + return { x: r.map((e, t10) => t10) }; } -function rI(r) { +function _g(r) { if (r === "float32" || r === "int32" || r === "bool" || r === "string") return 4; if (r === "complex64") return 8; throw new Error(`Unknown dtype ${r}`); } -function oI(r, e) { - if (e === "float32") - return new Float32Array(r); - if (e === "int32") - return new Int32Array(r); - if (e === "bool" || e === "string") - return Uint8Array.from(new Int32Array(r)); - throw new Error(`Unknown dtype ${e}`); -} -function Bl() { +function zl() { return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu; } -var Ao; +function fI(r, e) { + Array.isArray(r) || (r = [r]), r.forEach((t10) => { + t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`); + }); +} +var Ro; (function(r) { r[r.MatMulReduceProgram = 0] = "MatMulReduceProgram", r[r.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r[r.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r[r.MatMulPackedProgram = 3] = "MatMulPackedProgram", r[r.MatMulMax = 4] = "MatMulMax"; -})(Ao || (Ao = {})); -var zee = O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); -var Wee = (r, e) => { - let t6 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch; - if (n.every((a) => a <= t6)) +})(Ro || (Ro = {})); +var ite = O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); +var ute = (r, e) => { + let t10 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch; + if (n.every((a) => a <= t10)) return n; - y.assert(n[0] > t6 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); + y.assert(n[0] > t10 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); let s = Math.ceil(Math.sqrt(n[0])); - return s > t6 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t6, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1]; + return s > t10 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t10, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1]; }; -var Ui = class extends Zr { - constructor(e, t6) { - if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchNumberInEncoder = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, !Bl()) +var Gi = class extends Zr { + constructor(e, t10) { + if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchNumberInEncoder = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, !zl()) throw new Error("WebGPU is not supported on this device"); - this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = e.features.has("timestamp-query-inside-passes"), this.adapterInfo = new kg(t6), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Ng(this.device), this.textureManager = new Tg(this.device), this.tensorMap = new Do(this, cr()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), O().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); + this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = e.features.has("timestamp-query-inside-passes"), this.adapterInfo = new kg(t10), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Ng(this.device), this.textureManager = new Tg(this.device), this.tensorMap = new Fo(this, sr()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), O().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); } nextDataId() { - return Ui.nextDataId++; + return Gi.nextDataId++; } floatPrecision() { return 32; @@ -26286,58 +26289,62 @@ var Ui = class extends Zr { defaultGpuBufferUsage() { return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; } - disposeData(e, t6 = false) { + disposeData(e, t10 = false) { if (this.tensorDataPendingDisposal.indexOf(e) >= 0) return false; if (!this.tensorMap.has(e)) return true; let o = this.tensorMap.get(e); - if (this.decRef(e), !t6 && o.refCount > 0) + if (this.decRef(e), !t10 && o.refCount > 0) return false; if (this.commandQueueOwnedIds.has(e)) return this.tensorDataPendingDisposal.push(e), false; let { complexTensorInfos: n } = this.tensorMap.get(e); - return n != null && (this.disposeData(n.real.dataId, t6), this.disposeData(n.imag.dataId, t6)), this.releaseResource(e), this.tensorMap.delete(e), true; + return n != null && (this.disposeData(n.real.dataId, t10), this.disposeData(n.imag.dataId, t10)), this.releaseResource(e), this.tensorMap.delete(e), true; } memory() { return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false }; } releaseResource(e) { - let t6 = this.tensorMap.get(e); - if (!(!t6 || !t6.resourceInfo)) { - if ("texture" in t6.resourceInfo) { - let o = t6.resourceInfo; + let t10 = this.tensorMap.get(e); + if (!(!t10 || !t10.resourceInfo)) { + if (t10.external) { + t10.resourceInfo = null; + return; + } + if ("texture" in t10.resourceInfo) { + let o = t10.resourceInfo; o.texture instanceof GPUTexture && this.textureManager.releaseTexture(o.texture, o.width, o.height, o.format, o.usage), o.texture = null; } else { - let o = t6.resourceInfo; + let o = t10.resourceInfo; this.bufferManager.releaseBuffer(o.buffer, o.size, o.usage), o.buffer = null; } - t6.resourceInfo = null; + t10.resourceInfo = null; } } refCount(e) { return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0; } incRef(e) { - let t6 = this.tensorMap.get(e); - t6.refCount++; + let t10 = this.tensorMap.get(e); + t10.refCount++; } decRef(e) { if (this.tensorMap.has(e)) { - let t6 = this.tensorMap.get(e); - t6.refCount--; + let t10 = this.tensorMap.get(e); + t10.refCount--; } } - write(e, t6, o) { + write(e, t10, o) { if (o === "complex64" && e != null) throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); let n = { id: this.nextDataId() }; - return this.tensorMap.set(n, { dtype: o, shape: t6, values: e, refCount: 1 }), n; + return this.tensorMap.set(n, { dtype: o, shape: t10, values: e, refCount: 1 }), n; } - move(e, t6, o, n, s) { + move(e, t10, o, n, s) { if (n === "complex64") throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); - this.tensorMap.set(e, { dtype: n, shape: o, values: t6, refCount: s }); + this.tensorMap.set(e, { dtype: n, shape: o, values: t10, refCount: s }); } submitQueue() { this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDataPendingDisposal.forEach((e) => { @@ -26353,18 +26360,18 @@ var Ui = class extends Zr { getComputePass() { return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass; } - async getBufferData(e, t6) { - let o = this.bufferManager.acquireBuffer(t6, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); - this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, o, 0, t6), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); + async getBufferData(e, t10) { + let o = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); + this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, o, 0, t10), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); let n = o.getMappedRange().slice(0); - return o.unmap(), o != null && this.bufferManager.releaseBuffer(o, t6, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), O().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n; + return o.unmap(), o != null && this.bufferManager.releaseBuffer(o, t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), O().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n; } - convertAndCacheOnCPU(e, t6) { + convertAndCacheOnCPU(e, t10) { let o = this.tensorMap.get(e); - return this.releaseResource(e), o.values = t6, o.values; + return this.releaseResource(e), o.values = t10, o.values; } readSync(e) { - let t6 = this.tensorMap.get(e), { values: o } = t6; + let t10 = this.tensorMap.get(e), { values: o } = t10; if (o == null) throw new Error("WebGPU readSync is only available for CPU-resident tensors."); return o; @@ -26372,125 +26379,142 @@ var Ui = class extends Zr { async read(e) { if (!this.tensorMap.has(e)) throw new Error(`Tensor ${e} was not registered!`); - let t6 = this.tensorMap.get(e), { values: o } = t6; + let t10 = this.tensorMap.get(e), { values: o } = t10; if (o != null) return this.convertAndCacheOnCPU(e, o); let n; - if (t6.dtype === "complex64") { - let s = await Promise.all([this.read(t6.complexTensorInfos.real.dataId), this.read(t6.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1]; + if (t10.dtype === "complex64") { + let s = await Promise.all([this.read(t10.complexTensorInfos.real.dataId), this.read(t10.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1]; n = S.mergeRealAndImagArrays(a, i); } else { - let s = t6.resourceInfo, a = await this.getBufferData(s.buffer, s.size); - n = oI(a, t6.dtype); + let s = t10.resourceInfo, a = await this.getBufferData(s.buffer, s.size); + n = y.convertBackendValuesAndArrayBuffer(a, t10.dtype); } return this.convertAndCacheOnCPU(e, n), n; } + copyBuffer(e, t10, o) { + let n = this.bufferManager.acquireBuffer(t10, o); + return this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, n, 0, t10), this.submitQueue(), n; + } + createTensorFromGPUData(e, t10, o) { + let n = e.buffer; + if (o === "complex64") + throw new Error("Cannot write to a complex64 dtype. "); + let s = { id: this.nextDataId() }; + this.tensorMap.set(s, { dtype: o, shape: t10, values: null, refCount: 1, external: e.zeroCopy }); + let a = this.tensorMap.get(s), i = _g(a.dtype) * y.sizeFromShape(a.shape); + if (e.buffer.size < i) + throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`); + if ((e.buffer.usage & (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !== (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) + throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!"); + return e.zeroCopy !== true && (n = this.copyBuffer(n, i, n.usage)), a.resourceInfo = { size: n.size, usage: n.usage, buffer: n }, sr().makeTensorFromDataId(s, t10, o, this); + } readToGPU(e) { - let t6 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resourceInfo: a } = t6; + let t10 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resourceInfo: a } = t10; if (n === "complex64") throw new Error("Does not support reading buffer for complex64 dtype."); if (a == null) throw o != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); let i = a.size, p = this.bufferManager.acquireBuffer(i, a.usage); this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, p, 0, i), this.submitQueue(); - let u = this.makeTensorInfo(s, n), c = cr().makeTensorFromTensorInfo(u), l = this.tensorMap.get(u.dataId); + let u = this.makeTensorInfo(s, n), c = sr().makeTensorFromTensorInfo(u), l = this.tensorMap.get(u.dataId); return l.resourceInfo = { size: i, usage: this.defaultGpuBufferUsage(), buffer: p }, { tensorRef: c, buffer: p, bufSize: i }; } bufferSync(e) { - let t6 = this.readSync(e.dataId); + let t10 = this.readSync(e.dataId); if (e.dtype === "string") try { - let o = t6.map((n) => y.decodeString(n)); + let o = t10.map((n) => y.decodeString(n)); return le(e.shape, e.dtype, o); } catch (o) { throw new Error("Failed to decode encoded string bytes into utf-8"); } - return le(e.shape, e.dtype, t6); + return le(e.shape, e.dtype, t10); } async time(e) { this.supportTimeQuery || console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU."); - let t6 = this.activeTimers, o = [], n = false; + let t10 = this.activeTimers, o = [], n = false; this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e(); let s = y.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = y.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null); - this.activeTimers = t6, n && (this.programTimersStack = null); + this.activeTimers = t10, n && (this.programTimersStack = null); let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, p = await Promise.all(s); return i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i; } - makeTensorInfo(e, t6, o) { - return t6 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t6), shape: e, dtype: t6 }; + makeTensorInfo(e, t10, o) { + return t10 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t10), shape: e, dtype: t10 }; } tensorToBinding(e) { if (!e) return null; - let t6 = this.tensorMap.get(e.dataId); - if ("texture" in t6.resourceInfo) { - let n = t6.resourceInfo; + let t10 = this.tensorMap.get(e.dataId); + if ("texture" in t10.resourceInfo) { + let n = t10.resourceInfo; return n.texture instanceof GPUExternalTexture ? n.texture : n.texture.createView(); } - let o = t6.resourceInfo; + let o = t10.resourceInfo; return { offset: 0, size: o.size, buffer: o.buffer }; } async getQueryTime(e) { return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0; } uploadToGPU(e) { - let t6 = this.tensorMap.get(e); - if (t6.resourceInfo) + let t10 = this.tensorMap.get(e); + if (t10.resourceInfo) return; - let o = rI(t6.dtype) * y.sizeFromShape(t6.shape), n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage()); - if (t6.resourceInfo = { size: o, usage: this.defaultGpuBufferUsage(), buffer: n }, t6.values) { + let o = _g(t10.dtype) * y.sizeFromShape(t10.shape), n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage()); + if (t10.resourceInfo = { size: o, usage: this.defaultGpuBufferUsage(), buffer: n }, t10.values) { let s = this.bufferManager.acquireUploadBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC), a = s.getMappedRange(); - t6.dtype === "int32" || t6.dtype === "bool" ? new Int32Array(a).set(t6.values) : new Float32Array(a).set(t6.values), s.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(s, 0, n, 0, o); + t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(a).set(t10.values) : new Float32Array(a).set(t10.values), s.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(s, 0, n, 0, o); let i = { size: o, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: s }; this.stagingPendingDisposal.push(i); } } makeUniforms(e) { - let t6 = 0, o = 0, n = []; - e.forEach((p) => { - p.data.length === 0 && (p.data = [1]); - let u; - switch (p.data.length) { + let t10 = 0, o = 0, n = [], s = 1; + e.forEach((u) => { + u.data.length === 0 && (u.data = [1]); + let c; + switch (u.data.length) { case 1: - u = 4; + c = 4; break; case 2: - u = 8; + c = 8; break; case 3: - u = 16; + c = 16; break; case 4: - u = 16; + c = 16; break; case 5: - u = 16; + c = 16; break; case 6: - u = 16; + c = 16; break; default: - y.assert(false, () => `Unsupported ${p.data.length}D shape`); + y.assert(false, () => `Unsupported ${u.data.length}D shape`); } - (o === 5 || o === 6) && (u = 16), t6 = Math.ceil(t6 / u) * u, o = p.data.length, n.push(t6), t6 += p.data.length * 4; + (o === 5 || o === 6) && (c = 16), c > s && (s = c), t10 = Math.ceil(t10 / c) * c, o = u.data.length, n.push(t10), t10 += u.data.length * 4; + }), t10 = Math.ceil(t10 / s) * s; + let a = new ArrayBuffer(t10); + e.forEach((u, c) => { + let l = n[c]; + u.type === "int32" ? new Int32Array(a, l, u.data.length).set(u.data) : u.type === "uint32" ? new Uint32Array(a, l, u.data.length).set(u.data) : new Float32Array(a, l, u.data.length).set(u.data); }); - let s = new ArrayBuffer(t6); - e.forEach((p, u) => { - let c = n[u]; - p.type === "int32" ? new Int32Array(s, c, p.data.length).set(p.data) : p.type === "uint32" ? new Uint32Array(s, c, p.data.length).set(p.data) : new Float32Array(s, c, p.data.length).set(p.data); - }); - let a = this.bufferManager.acquireBuffer(t6, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM); - this.queue.writeBuffer(a, 0, s, 0, t6); - let i = { size: t6, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: a }; - return this.uniformPendingDisposal.push(i), { offset: 0, size: t6, buffer: a }; + let i = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM); + this.queue.writeBuffer(i, 0, a, 0, t10); + let p = { size: t10, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: i }; + return this.uniformPendingDisposal.push(p), { offset: 0, size: t10, buffer: i }; } - runWebGPUProgram(e, t6, o, n, s) { + runWebGPUProgram(e, t10, o, n, s) { if (s || (s = this.makeTensorInfo(e.outputShape, o)), y.sizeFromShape(s.shape) === 0) return this.tensorMap.get(s.dataId).values = y.getTypedArrayFromDType(s.dtype, 0), s; - this.uploadToGPU(s.dataId), e.dispatch = Wee(this.device, e); + this.uploadToGPU(s.dataId), e.dispatch = ute(this.device, e); let a = [], i = []; if (!e.isFromPixels) { - a.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), i = t6.concat(s).map((x) => x.shape); + a.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), i = t10.concat(s).map((x) => x.shape); let h = "int32"; i.map((x) => { a.push({ type: h, data: x }); @@ -26501,27 +26525,27 @@ var Ui = class extends Zr { a.push({ type: h, data: [e.isVec4 ? x / 4 : x] }); } } - let p = t6.map((h, g) => { + let p = t10.map((h, g) => { if (h.dtype === "complex64") throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); return this.uploadToGPU(h.dataId), { dtype: this.tensorMap.get(h.dataId).dtype, shape: h.shape, name: e.variableNames[g] }; - }), u = R3(e, i, p, s), c; - u in this.pipelineCache ? c = this.pipelineCache[u] : (c = A3(this.device, e, p, s), this.pipelineCache[u] = c), n && (a = [...a, ...n]); - let l = [this.tensorToBinding(s), ...t6.map((h) => this.tensorToBinding(h)), this.makeUniforms(a)], m = this.device.createBindGroup({ layout: c.getBindGroupLayout(0), entries: l.map((h, g) => ({ binding: g, resource: h })) }); + }), u = L3(e, i, p, s), c; + u in this.pipelineCache ? c = this.pipelineCache[u] : (c = M3(this.device, e, p, s), this.pipelineCache[u] = c), n && (a = [...a, ...n]); + let l = [this.tensorToBinding(s), ...t10.map((h) => this.tensorToBinding(h)), this.makeUniforms(a)], m = this.device.createBindGroup({ layout: c.getBindGroupLayout(0), entries: l.map((h, g) => ({ binding: g, resource: h })) }); this.ensureCommandEncoderReady(); let d = this.getComputePass(), f = this.activeTimers != null; - return f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 0), d.setPipeline(c), d.setBindGroup(0, m), d.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t6.forEach((h) => { + return f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 0), d.setPipeline(c), d.setBindGroup(0, m), d.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t10.forEach((h) => { this.commandQueueOwnedIds.add(h.dataId); }), this.commandQueueOwnedIds.add(s.dataId), O().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), s; } async getTimeFromQuerySet(e) { - let t6 = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), o = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); - this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t6, 0), this.currentCommandEncoder.copyBufferToBuffer(t6, 0, o, 0, 16), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); + let t10 = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), o = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); + this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t10, 0), this.currentCommandEncoder.copyBufferToBuffer(t10, 0, o, 0, 16), this.submitQueue(), await o.mapAsync(GPUMapMode.READ); let n = new BigUint64Array(o.getMappedRange()), s = Number(n[1] - n[0]); - return o.unmap(), this.bufferManager.releaseBuffer(o, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t6, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), s / 1e6; + return o.unmap(), this.bufferManager.releaseBuffer(o, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t10, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), s / 1e6; } - shouldExecuteOnCPU(e, t6 = zee) { - return O().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && y.sizeFromShape(o.shape) < t6); + shouldExecuteOnCPU(e, t10 = ite) { + return O().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && y.sizeFromShape(o.shape) < t10); } numDataIds() { return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length; @@ -26530,59 +26554,46 @@ var Ui = class extends Zr { this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true); } }; -Ui.nextDataId = 0; -Bl() && Ci("webgpu", async () => { +Gi.nextDataId = 0; +zl() && Ti("webgpu", async () => { O().set("CHECK_COMPUTATION_FOR_ERRORS", false); - let r = { powerPreference: O().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t6 = {}; - e.features.has("timestamp-query-inside-passes") && (t6.requiredFeatures = ["timestamp-query-inside-passes"]); + let r = { powerPreference: O().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t10 = {}; + e.features.has("timestamp-query-inside-passes") && (t10.requiredFeatures = ["timestamp-query-inside-passes"]); let o = e.limits; - t6.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize }; - let n = await e.requestDevice(t6), s = await e.requestAdapterInfo(); - return new Ui(n, s); + t10.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize }; + let n = await e.requestDevice(t10), s = await e.requestAdapterInfo(); + return new Gi(n, s); }, 3); -var ye; +var ge; (function(r) { - r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.EQUAL = 5] = "EQUAL", r[r.GREATER = 6] = "GREATER", r[r.GREATER_EQUAL = 7] = "GREATER_EQUAL", r[r.INT_DIV = 8] = "INT_DIV", r[r.LESS = 9] = "LESS", r[r.LESS_EQUAL = 10] = "LESS_EQUAL", r[r.LOGICAL_AND = 11] = "LOGICAL_AND", r[r.MAX = 12] = "MAX", r[r.MIN = 13] = "MIN", r[r.MOD = 14] = "MOD", r[r.MUL = 15] = "MUL", r[r.NOT_EQUAL = 16] = "NOT_EQUAL", r[r.POW = 17] = "POW", r[r.PRELU = 18] = "PRELU", r[r.SQUARED_DIFFERENCE = 19] = "SQUARED_DIFFERENCE", r[r.SUB = 20] = "SUB"; -})(ye || (ye = {})); -var D3 = ` + r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.EQUAL = 5] = "EQUAL", r[r.GREATER = 6] = "GREATER", r[r.GREATER_EQUAL = 7] = "GREATER_EQUAL", r[r.INT_DIV = 8] = "INT_DIV", r[r.LESS = 9] = "LESS", r[r.LESS_EQUAL = 10] = "LESS_EQUAL", r[r.LOGICAL_AND = 11] = "LOGICAL_AND", r[r.LOGICAL_OR = 12] = "LOGICAL_OR", r[r.MAX = 13] = "MAX", r[r.MIN = 14] = "MIN", r[r.MOD = 15] = "MOD", r[r.MUL = 16] = "MUL", r[r.NOT_EQUAL = 17] = "NOT_EQUAL", r[r.POW = 18] = "POW", r[r.PRELU = 19] = "PRELU", r[r.SQUARED_DIFFERENCE = 20] = "SQUARED_DIFFERENCE", r[r.SUB = 21] = "SUB"; +})(ge || (ge = {})); +var V3 = ` if (isnan(a)) { return a; } if (isnan(b)) { return b; } `; -var O3 = ` - if (isNaN.r) { - resultTemp.r = valueForNaN; - } - if (isNaN.g) { - resultTemp.g = valueForNaN; - } - if (isNaN.b) { - resultTemp.b = valueForNaN; - } - if (isNaN.a) { - resultTemp.a = valueForNaN; - } +var $g = ` + resultTemp = select( + resultTemp, vec4(valueForNaN), + vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); `; -var aI = ` - let isNaN = isnanVec4(a) | isnanVec4(b); - ${O3} - `; -var Uee = "return a + b;"; -var Gee = "return areal * breal - aimag * bimag;"; -var Hee = "return areal * bimag + aimag * breal;"; -var qee = "return a / b;"; -var Kee = "return f32(a == b);"; -var jee = "return vec4(a == b);"; -var Xee = "return f32(a > b);"; -var Yee = "return vec4(a > b);"; -var Qee = "return f32(a >= b);"; -var Zee = "return vec4(a >= b);"; -var Jee = ` +var pte = "return a + b;"; +var cte = "return areal * breal - aimag * bimag;"; +var lte = "return areal * bimag + aimag * breal;"; +var mte = "return a / b;"; +var dte = "return f32(a == b);"; +var fte = "return vec4(a == b);"; +var hte = "return f32(a > b);"; +var gte = "return vec4(a > b);"; +var xte = "return f32(a >= b);"; +var yte = "return vec4(a >= b);"; +var bte = ` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `; -var ete = ` +var Cte = ` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); @@ -26604,15 +26615,18 @@ var ete = ` } return vec4(resultTemp); `; -var tte = "return f32(a < b);"; -var rte = "return vec4(a < b);"; -var ote = "return f32(a <= b);"; -var nte = "return vec4(a <= b);"; -var ste = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);"; -var ate = `return (vec4(a >= vec4(1.0)) * +var Ste = "return f32(a < b);"; +var wte = "return vec4(a < b);"; +var Ite = "return f32(a <= b);"; +var vte = "return vec4(a <= b);"; +var kte = "return f32(a >= 1.0 && b >= 1.0);"; +var Nte = `return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`; -var ite = ` - ${D3} +var Tte = "return f32(a >= 1.0 || b >= 1.0);"; +var _te = `return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`; +var $te = ` + ${V3} if (b == 0.) { return uniforms.NAN; } @@ -26623,23 +26637,11 @@ var ite = ` return (resultTemp + b) % b; } `; -var ute = ` +var Ete = ` + let isNaN = !vec4(b); let valueForNaN = uniforms.NAN; var resultTemp = vec4(a % b); - ${aI} - - if (b[0] == 0.) { - resultTemp[0] = uniforms.NAN; - } - if (b[1] == 0.) { - resultTemp[1] = uniforms.NAN; - } - if (b[2] == 0.) { - resultTemp[2] = uniforms.NAN; - } - if (b[3] == 0.) { - resultTemp[3] = uniforms.NAN; - } + ${$g} if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { resultTemp[0] = (resultTemp[0] + b[0]) % b[0]; @@ -26656,21 +26658,21 @@ var ute = ` return resultTemp; `; -var pte = "return a * b;"; -var cte = ` +var Ate = "return a * b;"; +var Rte = ` if (isnan(a) || isnan(b)) { return 1.0; } return f32(a != b); `; -var lte = ` +var Dte = ` var resultTemp = vec4(a != b); let valueForNaN = 1.0; - ${aI} + ${$g} return resultTemp; `; -var mte = ` +var Fte = ` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } @@ -26682,7 +26684,7 @@ var mte = ` } return sign(a) * pow(abs(a), b); `; -var dte = ` +var Ote = ` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -26704,20 +26706,20 @@ var dte = ` } let isNaN = (a < vec4(0.0)) & (floor(b) < b); let valueForNaN = uniforms.NAN; - ${O3} + ${$g} return resultTemp; `; -var fte = "if (a < 0.0) { return b * a; } return a;"; -var hte = ` +var Pte = "if (a < 0.0) { return b * a; } return a;"; +var Mte = ` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -var gte = "return (a - b) * (a - b);"; -var xte = "return a - b;"; -function sI(r, e, t6 = "uniforms.NAN") { - let o = e ? aI : D3; +var Lte = "return (a - b) * (a - b);"; +var Bte = "return a - b;"; +function gI(r, e, t10 = "uniforms.NAN") { + let o = e ? $g : V3; return e ? ` - let valueForNaN = ${t6}; + let valueForNaN = ${t10}; var resultTemp = vec4(${r}(a, b)); ` + o + ` return resultTemp; @@ -26725,85 +26727,87 @@ function sI(r, e, t6 = "uniforms.NAN") { return ${r}(a, b); `; } -function Ic(r, e) { +function kc(r, e) { switch (r) { - case ye.ADD: - return Uee; - case ye.ATAN2: - return sI("atan2", e); - case ye.COMPLEX_MULTIPLY_IMAG: - return Hee; - case ye.COMPLEX_MULTIPLY_REAL: - return Gee; - case ye.DIV: - return qee; - case ye.EQUAL: - return e ? jee : Kee; - case ye.GREATER: - return e ? Yee : Xee; - case ye.GREATER_EQUAL: - return e ? Zee : Qee; - case ye.INT_DIV: - return e ? ete : Jee; - case ye.LESS: - return e ? rte : tte; - case ye.LESS_EQUAL: - return e ? nte : ote; - case ye.LOGICAL_AND: - return e ? ate : ste; - case ye.MAX: - return sI("max", e); - case ye.MIN: - return sI("min", e); - case ye.MOD: - return e ? ute : ite; - case ye.MUL: + case ge.ADD: return pte; - case ye.NOT_EQUAL: - return e ? lte : cte; - case ye.POW: - return e ? dte : mte; - case ye.PRELU: - return e ? hte : fte; - case ye.SQUARED_DIFFERENCE: - return gte; - case ye.SUB: - return xte; + case ge.ATAN2: + return gI("atan2", e); + case ge.COMPLEX_MULTIPLY_IMAG: + return lte; + case ge.COMPLEX_MULTIPLY_REAL: + return cte; + case ge.DIV: + return mte; + case ge.EQUAL: + return e ? fte : dte; + case ge.GREATER: + return e ? gte : hte; + case ge.GREATER_EQUAL: + return e ? yte : xte; + case ge.INT_DIV: + return e ? Cte : bte; + case ge.LESS: + return e ? wte : Ste; + case ge.LESS_EQUAL: + return e ? vte : Ite; + case ge.LOGICAL_AND: + return e ? Nte : kte; + case ge.LOGICAL_OR: + return e ? _te : Tte; + case ge.MAX: + return gI("max", e); + case ge.MIN: + return gI("min", e); + case ge.MOD: + return e ? Ete : $te; + case ge.MUL: + return Ate; + case ge.NOT_EQUAL: + return e ? Dte : Rte; + case ge.POW: + return e ? Ote : Fte; + case ge.PRELU: + return e ? Mte : Pte; + case ge.SQUARED_DIFFERENCE: + return Lte; + case ge.SUB: + return Bte; default: throw new Error(`BinaryType ${r} is not implemented!`); } } -var Q; +var X; (function(r) { - r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.RSQRT = 27] = "RSQRT", r[r.SIN = 28] = "SIN", r[r.SINH = 29] = "SINH", r[r.SIGMOID = 30] = "SIGMOID", r[r.SQRT = 31] = "SQRT", r[r.SQUARE = 32] = "SQUARE", r[r.TAN = 33] = "TAN", r[r.TANH = 34] = "TANH", r[r.TO_INT = 35] = "TO_INT"; -})(Q || (Q = {})); -var yte = "return abs(a);"; -var bte = ` + r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.ROUND = 27] = "ROUND", r[r.RSQRT = 28] = "RSQRT", r[r.SELU = 29] = "SELU", r[r.SIGMOID = 30] = "SIGMOID", r[r.SIGN = 31] = "SIGN", r[r.SIN = 32] = "SIN", r[r.SINH = 33] = "SINH", r[r.SOFTPLUS = 34] = "SOFTPLUS", r[r.SQRT = 35] = "SQRT", r[r.SQUARE = 36] = "SQUARE", r[r.STEP = 37] = "STEP", r[r.TAN = 38] = "TAN", r[r.TANH = 39] = "TANH", r[r.TO_INT = 40] = "TO_INT"; +})(X || (X = {})); +var Vte = "return abs(a);"; +var zte = ` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); `; -var Cte = ` +var Wte = ` if (a < 1.) { return uniforms.NAN; } return acosh(a); `; -var Ste = ` +var Ute = ` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); `; -var wte = "return asinh(a);"; -var Ite = ` +var Gte = "return asinh(a);"; +var Hte = ` if (isnan(a)) { return uniforms.NAN; } return atan(a); `; -var vte = ` +var Kte = ` if (abs(a) > 1.) { return uniforms.NAN; } @@ -26815,15 +26819,15 @@ var vte = ` } return atanh(a); `; -var kte = "return ceil(a);"; -var Nte = "return cos(a);"; -var Tte = ` +var qte = "return ceil(a);"; +var jte = "return cos(a);"; +var Xte = ` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `; -var _te = "return exp(a) - 1.0;"; -var Ete = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);"; -var $te = ` +var Yte = "return exp(a) - 1.0;"; +var Qte = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);"; +var Zte = ` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -26839,7 +26843,7 @@ var $te = ` } return resFloat; `; -var Ate = ` +var Jte = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -26855,121 +26859,163 @@ var Ate = ` let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); `; -var Rte = "return exp(a);"; -var Fte = "return floor(a);"; -var Dte = "return f32(!isnan(a) && !isinf(a));"; -var Ote = "return f32(isinf(a));"; -var Pte = "return f32(isnan(a));"; -var Mte = "return a;"; -var Lte = `if (a < 0.0) { return uniforms.NAN; } +var ere = "return exp(a);"; +var tre = "return floor(a);"; +var rre = "return f32(!isnan(a) && !isinf(a));"; +var ore = "return f32(isinf(a));"; +var nre = "return f32(isnan(a));"; +var sre = "return a;"; +var are = `if (a < 0.0) { return uniforms.NAN; } return log(a);`; -var Bte = ` +var ire = ` if (isnan(a)) { return a; } return log(1.0 + a); `; -var Vte = "return f32(!(a >= 1.0));"; -var zte = "return -a;"; -var Wte = "if (a < 0.0) { return uniforms.alpha * a; } return a;"; -var Ute = ` +var ure = "return f32(!(a >= 1.0));"; +var pre = "return -a;"; +var cre = "if (a < 0.0) { return uniforms.alpha * a; } return a;"; +var lre = ` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -var Gte = "return 1.0 / a;"; -var Hte = "return select(a, 0.0, a < 0.0);"; -var qte = "return clamp(a, 0.0, 6.0);"; -var Kte = "return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));"; -var jte = ` +var mre = "return 1.0 / a;"; +var dre = "return select(a, 0.0, a < 0.0);"; +var fre = "return clamp(a, 0.0, 6.0);"; +var hre = "return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));"; +var gre = ` return select(a, vec4(0.0), a < vec4(0.0)); `; -var Xte = "return inverseSqrt(a);"; -var Yte = "return 1.0 / (1.0 + exp(-1.0 * a));"; -var Qte = "return sin(a);"; -var Zte = ` +var xre = "return round(a);"; +var yre = "return inverseSqrt(a);"; +var bre = ` + if (a >= 0.0) { + return ${S.SELU_SCALE} * a; + } else { + return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0); + } +`; +var Cre = "return 1.0 / (1.0 + exp(-1.0 * a));"; +var Sre = "return sign(a);"; +var wre = "return sin(a);"; +var Ire = ` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `; -var Jte = "return sqrt(a);"; -var ere = "return a * a;"; -var tre = "return tan(a);"; -var rre = ` +var vre = ` + let epsilon = 1.1920928955078125e-7; + let threshold = log(epsilon) + 2.0; + + let too_large = a > -threshold; + let too_small = a < threshold; + let exp_a = exp(a); + + if (too_large) { + return a; + } else if (too_small) { + return exp_a; + } else { + return log(exp_a + 1.0); + } +`; +var kre = "return sqrt(a);"; +var Nre = "return a * a;"; +var Tre = ` + if (isnan(a)) { + return a; + } + + return select(uniforms.stepAlpha, 1.0, a > 0.0); +`; +var _re = "return tan(a);"; +var $re = ` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `; -var ore = "return f32(i32((a)));"; -function Ha(r, e) { +var Ere = "return f32(i32((a)));"; +function ja(r, e) { switch (r) { - case Q.ABS: - return yte; - case Q.ACOS: - return bte; - case Q.ACOSH: - return Cte; - case Q.ASIN: - return Ste; - case Q.ASINH: - return wte; - case Q.ATAN: - return Ite; - case Q.ATANH: - return vte; - case Q.COS: - return Nte; - case Q.COSH: - return Tte; - case Q.CEIL: - return kte; - case Q.ELU: - return e ? $te : Ete; - case Q.ERF: - return Ate; - case Q.EXP: - return Rte; - case Q.EXPM1: - return _te; - case Q.FLOOR: - return Fte; - case Q.IS_FINITE: - return Dte; - case Q.IS_INF: - return Ote; - case Q.IS_NAN: - return Pte; - case Q.LINEAR: - return Mte; - case Q.LOG: - return Lte; - case Q.LOG1P: - return Bte; - case Q.LOGICAL_NOT: + case X.ABS: return Vte; - case Q.NEG: + case X.ACOS: return zte; - case Q.LEAKYRELU: - return e ? Ute : Wte; - case Q.RECIPROCAL: + case X.ACOSH: + return Wte; + case X.ASIN: + return Ute; + case X.ASINH: return Gte; - case Q.RELU: - return e ? jte : Hte; - case Q.RELU6: - return e ? Kte : qte; - case Q.RSQRT: + case X.ATAN: + return Hte; + case X.ATANH: + return Kte; + case X.COS: + return jte; + case X.COSH: return Xte; - case Q.SIGMOID: - return Yte; - case Q.SIN: - return Qte; - case Q.SINH: - return Zte; - case Q.SQRT: + case X.CEIL: + return qte; + case X.ELU: + return e ? Zte : Qte; + case X.ERF: return Jte; - case Q.SQUARE: + case X.EXP: return ere; - case Q.TAN: + case X.EXPM1: + return Yte; + case X.FLOOR: return tre; - case Q.TANH: + case X.IS_FINITE: return rre; - case Q.TO_INT: + case X.IS_INF: return ore; + case X.IS_NAN: + return nre; + case X.LINEAR: + return sre; + case X.LOG: + return are; + case X.LOG1P: + return ire; + case X.LOGICAL_NOT: + return ure; + case X.NEG: + return pre; + case X.LEAKYRELU: + return e ? lre : cre; + case X.RECIPROCAL: + return mre; + case X.RELU: + return e ? gre : dre; + case X.RELU6: + return e ? hre : fre; + case X.ROUND: + return xre; + case X.RSQRT: + return yre; + case X.SELU: + return bre; + case X.SIGMOID: + return Cre; + case X.SIGN: + return Sre; + case X.SIN: + return wre; + case X.SINH: + return Ire; + case X.SOFTPLUS: + return vre; + case X.SQRT: + return kre; + case X.SQUARE: + return Nre; + case X.STEP: + return Tre; + case X.TAN: + return _re; + case X.TANH: + return $re; + case X.TO_INT: + return Ere; default: throw new Error(`BinaryType ${r} is not implemented!`); } @@ -26988,27 +27034,27 @@ var kt = (r) => { throw new Error(`${r}-component is not supported.`); } }; -function ur(r, e = false, t6 = false, o = 3) { +function pr(r, e = false, t10 = false, o = 3) { if (r === null) return ""; let n = ""; if (r === "linear") - n = Ha(Q.LINEAR); + n = ja(X.LINEAR); else if (r === "relu") - n = Ha(Q.RELU, t6); + n = ja(X.RELU, t10); else if (r === "elu") - n = Ha(Q.ELU, t6); + n = ja(X.ELU, t10); else if (r === "relu6") - n = Ha(Q.RELU6, t6); + n = ja(X.RELU6, t10); else if (r === "prelu") - n = Ic(ye.PRELU, t6); + n = kc(ge.PRELU, t10); else if (r === "sigmoid") - n = Ha(Q.SIGMOID, t6); + n = ja(X.SIGMOID, t10); else if (r === "leakyrelu") - n = Ha(Q.LEAKYRELU, t6); + n = ja(X.LEAKYRELU, t10); else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`); - let a = kt(t6 ? 4 : 1), i = ""; + let a = kt(t10 ? 4 : 1), i = ""; return e ? i = ` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); @@ -27018,195 +27064,190 @@ function ur(r, e = false, t6 = false, o = 3) { ${n} }`, i; } -function Hr(r, e) { +function Gr(r, e) { return ` ${r ? "value = value + getBiasByOutputCoords(coords);" : ""} ${e ? "value = activation(value, coords);" : ""} `; } -function iI(r, e, t6, o, n = false, s = false, a = false, i = 1) { - y.assert(t6 && i === 1 || !t6, () => `transposeA ${t6} is not compatible with component size ${i}`); - let p = ` - let batch = ${r ? "0" : "batchIn"}; - ${t6 ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"} +function xI(r, e, t10 = false, o = false, n = false, s = 1) { + y.assert(r && s === 1 || !r, () => `transposeA ${r} is not compatible with component size ${s}`); + let a = ` + ${r ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"} - `, u = o ? "value = getB(batch, col, row);" : "value = getB(batch, row, col);"; + `, i = e ? "value = getB(batch, col, row);" : "value = getB(batch, row, col);"; return ` - fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { - var value = ${kt(i)}(0.0); - let col = colIn * ${i}; - ${n && a ? p : ` - ${t6 ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} + fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} { + var value = ${kt(s)}(0.0); + let col = colIn * ${s}; + ${t10 && n ? a : ` + ${r ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { - ${p} + ${a} } `} return value; } - fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { - let col = colIn * ${i}; - let batch = ${e ? "0" : "batchIn"}; - var value = ${kt(i)}(0.0); - ${u} + fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} { + let col = colIn * ${s}; + var value = ${kt(s)}(0.0); + ${i} return value; } `; } -function Vl(r, e, t6, o, n, s, a = false, i = false, p = false, u = 1) { +function Wl(r, e, t10, o, n = false, s = false, a = false, i = 1) { return ` - ${iI(t6, o, n, s, a, i, p, u)} - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) { - let col = colIn * ${u}; - ${a && i ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} + ${xI(t10, o, n, s, a, i)} + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) { + let col = colIn * ${i}; + ${n && s ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${Hr(r, e)} + ${Gr(r, e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `; } -var nre = (r) => r ? ` - mm_Asub[inputRow][inputCol] = mm_readA(batch, +var Are = (r, e) => r ? ` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, - globalRowStart / innerElementSize + inputCol); + globalRowStart / ${e} + inputCol); ` : ` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, - kStart / innerElementSize + inputCol); + kStart / ${e} + inputCol); `; -var sre = (r, e) => r ? ` - let ACached0 = mm_Asub[k * innerElementSize][localRow]; - let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; - let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; - ${e === 3 ? "" : "let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} - for (var i = 0; i < rowPerThread; i = i + 1) { +var Rre = (r, e, t10) => r ? ` + let ACached0 = mm_Asub[k * ${e}][localRow]; + let ACached1 = mm_Asub[k * ${e} + 1][localRow]; + let ACached2 = mm_Asub[k * ${e} + 2][localRow]; + ${e === 3 ? "" : `let ACached3 = mm_Asub[k * ${e} + 3][localRow];`} + for (var i = 0; i < ${t10}; i++) { acc[i] = BCached0 * ACached0[i] + acc[i]; acc[i] = BCached1 * ACached1[i] + acc[i]; acc[i] = BCached2 * ACached2[i] + acc[i]; ${e === 3 ? "" : "acc[i] = BCached3 * ACached3[i] + acc[i];"} }` : ` - for (var i = 0; i < rowPerThread; i = i + 1) { + for (var i = 0; i < ${t10}; i++) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached0 * ACached.x + acc[i]; acc[i] = BCached1 * ACached.y + acc[i]; acc[i] = BCached2 * ACached.z + acc[i]; ${e === 3 ? "" : "acc[i] = BCached3 * ACached.w + acc[i];"} }`; -function Wu(r, e, t6 = false, o = 32, n = false, s = 32, a = false) { - let i = e[1] * r[1], p = e[0] * r[0], u = t6 ? i : o, c = t6 ? o : i, l = u / e[0], m = o / e[1]; - return y.assert((t6 && l === 4 && r[1] === 4 || !t6 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t6} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. - Otherwise, innerElementSize ${l} must be 3 or 4. - tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`), ` - var mm_Asub : array, ${u / l}>, ${c}>; - var mm_Bsub : array, ${p / r[0]}>, ${o}>; +function qu(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) { + let p = e[1] * r[1], u = e[0] * r[0], c = t10 ? p : o, l = t10 ? o : p, m = c / e[0], d = o / e[1], f = r[1]; + return y.assert((t10 && m === 4 && r[1] === 4 || !t10 && (m === 3 || m === 4)) && c % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t10} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4. + Otherwise, innerElementSize ${m} must be 3 or 4. + tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`), ` + var mm_Asub : array, ${c / m}>, ${l}>; + var mm_Bsub : array, ${u / r[0]}>, ${o}>; - const rowPerThread = ${r[1]}; - const colPerThread = ${r[0]}; - const innerElementSize = ${l}; - const tileInner = ${o}; - - ${se()} { + ${Q()} { let localRow = i32(localId.y); - let tileRow = ${a ? "0" : "localRow * rowPerThread"}; + let tileRow = ${a ? "0" : `localRow * ${f}`}; let tileCol = i32(localId.x); - let globalRow = ${a ? "0" : "i32(globalId.y) * rowPerThread"}; + let globalRow = ${a ? "0" : `i32(globalId.y) * ${f}`}; let globalCol = i32(globalId.x); let batch = ${n ? "0" : "i32(globalId.z)"}; - let globalRowStart = i32(workgroupId.y) * ${i}; + let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"}; + let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"}; + let globalRowStart = i32(workgroupId.y) * ${p}; - let numTiles = ${n ? `${Math.ceil(s / o)}` : "(uniforms.dimInner - 1) / tileInner + 1"}; + let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"}; - var acc: array, rowPerThread>; + var acc: array, ${f}>; // Loop over shared dimension. - let tileRowB = localRow * ${m}; - for (var t = 0; t < numTiles; t = t + 1) { + let tileRowB = localRow * ${d}; + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${nre(t6)} + ${Are(t10, m)} } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${d}; innerRow++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol); + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol); } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { - let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; - let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; - let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; - ${l === 3 ? "" : "let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + for (var k = 0; k < ${o / m}; k++) { + let BCached0 = mm_Bsub[k * ${m}][tileCol]; + let BCached1 = mm_Bsub[k * ${m} + 1][tileCol]; + let BCached2 = mm_Bsub[k * ${m} + 2][tileCol]; + ${m === 3 ? "" : `let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`} - ${sre(t6, l)} + ${Rre(t10, m, f)} } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } }`; } -var P3 = (r) => r ? ` - mm_Asub[inputRow][inputCol] = mm_readA(batch, +var z3 = (r) => r ? ` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); ` : ` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); `; -var are = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; -function Uu(r, e, t6 = false, o = 32, n = false, s = 32, a = false) { - let i = r[1] * e[1], p = r[0] * e[0], u = t6 ? i : o, c = t6 ? o : i; - y.assert(c % e[1] === 0 && u % e[0] === 0 && o % e[1] === 0, () => `tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`); - let l = c / e[1], m = u / e[0], d = o / e[1], f = a ? ` +var Dre = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; +function ju(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) { + let p = r[1] * e[1], u = r[0] * e[0], c = t10 ? p : o, l = t10 ? o : p; + y.assert(l % e[1] === 0 && c % e[0] === 0 && o % e[1] === 0, () => `tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`); + let m = l / e[1], d = c / e[0], f = o / e[1], h = r[1], g = r[0], x = a ? ` let localRow = i32(localId.y); let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${i}; - let globalColStart = i32(workgroupId.x) * ${p}; + let globalRowStart = i32(workgroupId.y) * ${p}; + let globalColStart = i32(workgroupId.x) * ${u}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { - ${P3(t6)} + for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { + ${z3(t10)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalColStart + inputCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${o}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - let ACached = ${t6 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + let ACached = ${t10 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`} + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } @@ -27214,58 +27255,58 @@ function Uu(r, e, t6 = false, o = 32, n = false, s = 32, a = false) { } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { let gRow = globalRowStart + localRow + innerRow * ${e[1]}; - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${e[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } ` : ` - let tileRow = i32(localId.y) * rowPerThread; - let tileCol = i32(localId.x) * colPerThread; + let tileRow = i32(localId.y) * ${h}; + let tileCol = i32(localId.x) * ${g}; - let globalRow = i32(globalId.y) * rowPerThread; - let globalCol = i32(globalId.x) * colPerThread; - let globalRowStart = i32(workgroupId.y) * ${i}; + let globalRow = i32(globalId.y) * ${h}; + let globalCol = i32(globalId.x) * ${g}; + let globalRowStart = i32(workgroupId.y) * ${p}; - let tileRowA = i32(localId.y) * ${l}; - let tileColA = i32(localId.x) * ${m}; - let tileRowB = i32(localId.y) * ${d}; + let tileRowA = i32(localId.y) * ${m}; + let tileColA = i32(localId.x) * ${d}; + let tileRowB = i32(localId.y) * ${f}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${P3(t6)} + ${z3(t10)} } } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol + innerCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${o}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - ${are(t6)} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + ${Dre(t10)} + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } @@ -27274,77 +27315,79 @@ function Uu(r, e, t6 = false, o = 32, n = false, s = 32, a = false) { workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `; return ` - var mm_Asub : array, ${c}>; - var mm_Bsub : array, ${o}>; - const rowPerThread = ${r[1]}; - const colPerThread = ${r[0]}; - const tileInner = ${o}; + var mm_Asub : array, ${l}>; + var mm_Bsub : array, ${o}>; - ${se()} { + ${Q()} { let batch = ${n ? "0" : "i32(globalId.z)"}; - let numTiles = ${n ? `${Math.ceil(s / o)}` : "(uniforms.dimInner - 1) / tileInner + 1"}; + let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"}; + let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"}; + let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"}; - var acc : array, rowPerThread>; + var acc : array, ${h}>; // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = 0.0; } } - ${f} + ${x} } `; } -var ire = (r) => r ? ` - mm_readA(batch, colA, globalRow), - mm_readA(batch, colA + 1, globalRow), - mm_readA(batch, colA + 2, globalRow), - mm_readA(batch, colA + 3, globalRow) +var Fre = (r) => r ? ` + mm_readA(batchA, colA, globalRow), + mm_readA(batchA, colA + 1, globalRow), + mm_readA(batchA, colA + 2, globalRow), + mm_readA(batchA, colA + 3, globalRow) ` : ` - mm_readA(batch, globalRow, colA), - mm_readA(batch, globalRow, colA + 1), - mm_readA(batch, globalRow, colA + 2), - mm_readA(batch, globalRow, colA + 3) + mm_readA(batchA, globalRow, colA), + mm_readA(batchA, globalRow, colA + 1), + mm_readA(batchA, globalRow, colA + 2), + mm_readA(batchA, globalRow, colA + 3) `; -function ure(r, e = false) { - return y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`), ` - const tileSize = ${r[0] * 4}; +function Ore(r, e = false) { + y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`); + let t10 = r[0] * 4; + return ` var mm_Asub : array, ${r[0]}>; - ${se()} { + ${Q()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); - let numTiles = (uniforms.dimInner - 1) / tileSize + 1; + let numTiles = (uniforms.dimInner - 1) / ${t10} + 1; let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - let colA = t * tileSize + tileCol * 4; - mm_Asub[tileCol] = vec4(${ire(e)}); + let colA = t * ${t10} + tileCol * 4; + mm_Asub[tileCol] = vec4(${Fre(e)}); workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileSize / 4; k = k + 1) { - let rowB = t * tileSize + k * 4; - let BCached = vec4(mm_readB(batch, rowB, globalCol), - mm_readB(batch, rowB + 1, globalCol), - mm_readB(batch, rowB + 2, globalCol), - mm_readB(batch, rowB + 3, globalCol)); + for (var k = 0; k < ${t10 / 4}; k++) { + let rowB = t * ${t10} + k * 4; + let BCached = vec4(mm_readB(batchB, rowB, globalCol), + mm_readB(batchB, rowB + 1, globalCol), + mm_readB(batchB, rowB + 2, globalCol), + mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); @@ -27357,53 +27400,55 @@ function ure(r, e = false) { } `; } -var _g = class { - constructor(e, t6, o, n, s = false, a = false, i = null, p = null, u = null, c = false) { - this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t6, this.dispatchLayout = { x: [2], y: [1], z: [0] }; - let l = s ? e[1] : e[2]; - if (this.isVec4 = (l % 4 === 0 && !s || t6[1] % 4 === 0 && s) && t6[2] % 4 === 0 && !a, this.isVectorA = t6[1] === 1 && !s, !this.isVec4 && this.isVectorA) +var Eg = class { + constructor(e, t10, o = false, n = false, s = null, a = null, i = null, p = false) { + this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t10, this.dispatchLayout = { x: [2], y: [1], z: [0] }; + let u = o ? e[1] : e[2]; + if (this.isVec4 = (u % 4 === 0 && !o || t10[1] % 4 === 0 && o) && t10[2] % 4 === 0 && !n, this.isVectorA = t10[1] === 1 && !o, !this.isVec4 && this.isVectorA) this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1]; else { - let f = tI(t6[1], l, t6[2], s); - this.workgroupSize = f.workgroupSize, this.elementsPerThread = f.elementsPerThread; + let m = dI(t10[1], u, t10[2], o); + this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread; } - this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread); - let m = i != null, d = u != null; - m && this.variableNames.push("bias"), d && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = c, this.transposeA = s, this.transposeB = a, this.addBias = m, this.activation = p, this.hasPreluActivationWeights = d, this.batchAEqualOne = o, this.batchBEqualOne = n, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t6[1], t6[2], l), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`; + this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread); + let c = s != null, l = i != null; + c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t10[1], t10[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`; } - getShapeFit(e, t6, o) { + getShapeFit(e, t10, o) { let n = this.workgroupSize[1] * this.elementsPerThread[1], s = this.workgroupSize[0] * this.elementsPerThread[0]; !this.isVec4 && this.isVectorA ? this.tileInner = this.workgroupSize[0] * 4 : this.tileInner = s; - let a = e % n === 0, i = t6 % s === 0, p = o % this.tileInner === 0; + let a = e % n === 0, i = t10 % s === 0, p = o % this.tileInner === 0; return [a, i, p]; } getUserCode() { return ` - ${ur(this.activation, this.hasPreluActivationWeights, this.isVec4)} - ${Vl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} - ${this.isVec4 ? Wu(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA) : this.isVectorA ? ure(this.workgroupSize, this.transposeA) : Uu(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads)} + ${pr(this.activation, this.hasPreluActivationWeights, this.isVec4)} + ${Wl(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} + ${this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA, true) : this.isVectorA ? Ore(this.workgroupSize, this.transposeA) : ju(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)} `; } }; -function pre() { +function Pre(r) { return ` - var sumValues : array; - ${se()} { + var sumValues : array; + ${Q()} { let coords = getOutputCoords(); let batch = coords[0]; + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; - for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) { - let dataA = mm_readA(batch, row, k); - let dataB = mm_readB(batch, k, col); + for (var k = i32(localId.x); k < Length; k = k + ${r}) { + let dataA = mm_readA(batchA, row, k); + let dataB = mm_readB(batchB, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); - for(var currentSize = workgroupSizeX / 2u; currentSize > 1u; + for(var currentSize = ${r / 2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { @@ -27419,25 +27464,25 @@ function pre() { } `; } -var Eg = class { - constructor(e, t6, o, n = false, s = false, a = null, i = null, p = null) { - this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize); - let u = a != null, c = p != null; - u && this.variableNames.push("bias"), c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.batchAEqualOne = t6, this.batchBEqualOne = o, this.shaderKey = `matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`; +var Ag = class { + constructor(e, t10 = false, o = false, n = null, s = null, a = null) { + this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize); + let i = n != null, p = a != null; + i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t10, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t10}_${o}`; } getUserCode() { return ` - ${ur(this.activation, this.hasPreluActivationWeights)} - ${Vl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)} - ${pre()} + ${pr(this.activation, this.hasPreluActivationWeights)} + ${Wl(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${Pre(this.workgroupSize[0])} `; } }; -function cre(r) { - let e = r[1], t6 = r[0], o = e > t6 ? e : t6; +function Mre(r) { + let e = r[1], t10 = r[0], o = e > t10 ? e : t10; return ` var mm_Asub : array, ${e}>; - var mm_Bsub : array, ${o}>; + var mm_Bsub : array, ${o}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. @@ -27445,12 +27490,14 @@ function cre(r) { // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${se()} { + ${Q()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${o} + 1; @@ -27458,9 +27505,9 @@ function cre(r) { var globalColA = tileCol; var globalRowB = 0; - var regA = mm_readA(batch, globalRow, globalColA); - var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + var regA = mm_readA(batchA, globalRow, globalColA); + var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; @@ -27471,9 +27518,9 @@ function cre(r) { workgroupBarrier(); - regA = mm_readA(batch, globalRow, globalColA); - regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + regA = mm_readA(batchA, globalRow, globalColA); + regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; @@ -27487,82 +27534,71 @@ function cre(r) { } `; } -var $g = class { - constructor(e, t6, o, n = false, s = false, a = null, i = null, p = null) { +var Rg = class { + constructor(e, t10, o, n = false, s = false, a = null, i = null, p = null) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]]; let u = a != null; u && this.variableNames.push("bias"); let c = p != null; - c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.batchAEqualOne = e[0] === 1, this.batchBEqualOne = t6[0] === 1, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`; + c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}`; } getUserCode() { return ` - ${ur(this.activation, this.hasPreluActivationWeights)} - ${Vl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)} - ${cre(this.workgroupSize)} + ${pr(this.activation, this.hasPreluActivationWeights)} + ${Wl(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${Mre(this.workgroupSize)} `; } }; -var Ag = class { - constructor(e, t6, o, n, s = false, a = false) { - this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.isVec4 = false, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }, this.isVec4 = (s && this.outputShape[1] % 4 === 0 || !s && t6 % 4 === 0) && this.outputShape[2] % 4 === 0, this.elementsPerThread = [4, 4, this.splitedDimInner], this.isVec4 || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = re(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t6], this.workgroupSize, this.elementsPerThread), this.transposeA = s, this.transposeB = a, this.batchAEqualOne = o, this.batchBEqualOne = n, this.shaderKey = `matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`; +var Dg = class { + constructor(e, t10, o = false, n = false) { + this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.isVec4 = false, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }, this.isVec4 = (o && this.outputShape[1] % 4 === 0 || !o && t10 % 4 === 0) && this.outputShape[2] % 4 === 0, this.elementsPerThread = [4, 4, this.splitedDimInner], this.isVec4 || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = Y(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t10], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`; } getUserCode() { - let e = (n) => ` - for (var i = 0; i < ${n}; i = i + 1) - { - var oldValue = atomicLoad(&(result[flatIndex + i])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + ${n > 1 ? "value[i]" : "value"}; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } - } - `, t6 = this.isVec4 ? 4 : 1; + let e = this.isVec4 ? 4 : 1; return ` - ${iI(this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, false, false, false, t6)} - fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t6)}) { - let col = colIn * ${t6}; + ${xI(false, this.transposeB, false, false, false, e)} + fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) { + let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. - ${e(t6)} + for (var i = 0; i < ${e}; i = i + 1) { + ${Ic("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")} + } } } - ${this.isVec4 ? Wu(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : Uu(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} + ${this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : ju(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} `; } }; -var Rg = class { - constructor(e, t6 = null, o = null, n = null) { - this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t6 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`; +var Fg = class { + constructor(e, t10 = null, o = null, n = null) { + this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`; } getUserCode() { return ` - ${ur(this.activation, this.hasPreluActivationWeights)} - ${se("index")} { + ${pr(this.activation, this.hasPreluActivationWeights)} + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${Hr(this.addBias, this.activation)} + ${Gr(this.addBias, this.activation)} setOutputAtIndex(index, value); } } `; } }; -var Fg = class { +var Og = class { constructor(e) { - this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill"; + this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } @@ -27570,83 +27606,83 @@ var Fg = class { `; } }; -function dr(r) { - let { backend: e, attrs: t6 } = r, { shape: o, value: n } = t6, { dtype: s } = t6; +function fr(r) { + let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), e.makeTensorInfo(o, s, a); } else { - let a = new Fg(o), i = [{ type: "float32", data: [n] }]; + let a = new Og(o), i = [{ type: "float32", data: [n] }]; return e.runWebGPUProgram(a, [], s, i); } } -var M3 = { kernelName: Cs, backendName: "webgpu", kernelFunc: dr }; -function de(r) { - let { inputs: e, attrs: t6 } = r, { x: o } = e, { shape: n } = t6, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); +var W3 = { kernelName: ws, backendName: "webgpu", kernelFunc: fr }; +function me(r) { + let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var L3 = { kernelName: Ns, backendName: "webgpu", kernelFunc: de }; -function Gu({ a: r, b: e, transposeA: t6, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = e.shape.length, l = t6 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t6 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = br.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t6} and transposeB=${o} must match.`); - let k = t6 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], $ = de({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = de({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [$, A], D = Math.max(x, b), P = x === 1, M = b === 1, L = [$, A], W = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], V, U, q = [D, d, f], H = O().get("WEBGPU_MATMUL_PROGRAM_TYPE"); - if (H < 0) { - let X = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), Z = X > 0 ? X : n.thresholdToIncreaseWorkgroups, ee = D * Math.ceil(d / 32) * Math.ceil(f / 32); - ee <= Z || d <= 8 && ee <= Z * 2 ? D * d * f <= 128 ? H = Ao.MatMulReduceProgram : D === 1 && m >= 2e3 ? H = Ao.MatMulSplitKProgram : H = Ao.MatMulSmallOutputSizeProgram : H = Ao.MatMulPackedProgram; +var U3 = { kernelName: _s, backendName: "webgpu", kernelFunc: me }; +function Xu({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = yr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`); + let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = me({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = me({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [E, A], D = Math.max(x, b), P = [E, A], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, V, z = [D, d, f], U = O().get("WEBGPU_MATMUL_PROGRAM_TYPE"); + if (U < 0) { + let H = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), q = H > 0 ? H : n.thresholdToIncreaseWorkgroups, Z = D * Math.ceil(d / 32) * Math.ceil(f / 32); + Z <= q || d <= 8 && Z <= q * 2 ? D * d * f <= 128 ? U = Ro.MatMulReduceProgram : D === 1 && m >= 2e3 ? U = Ro.MatMulSplitKProgram : U = Ro.MatMulSmallOutputSizeProgram : U = Ro.MatMulPackedProgram; } - switch (H) { - case Ao.MatMulReduceProgram: - V = new Eg(q, P, M, t6, o, s, p, a); + switch (U) { + case Ro.MatMulReduceProgram: + L = new Ag(z, t10, o, s, p, a); break; - case Ao.MatMulSplitKProgram: { - if (U = dr({ backend: n, attrs: { shape: q, value: 0, dtype: r.dtype } }), V = new Ag(q, m, P, M, t6, o), s || p) { - U = n.runWebGPUProgram(V, L, r.dtype, W, U); - let Z = new Rg(U.shape, s, p, a), ee = null, Y = [U]; - s && Y.push(s), a && Y.push(a), p === "leakyrelu" && (ee = [{ type: "float32", data: [i] }], Z.uniforms += " alpha : f32,"); - let J = n.runWebGPUProgram(Z, Y, U.dtype, ee); - R.push(U); - let ie = de({ inputs: { x: J }, backend: n, attrs: { shape: w } }); - R.push(J); - for (let pe of R) - n.disposeData(pe.dataId); - return ie; + case Ro.MatMulSplitKProgram: { + if (V = fr({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Dg(z, m, t10, o), s || p) { + V = n.runWebGPUProgram(L, P, r.dtype, M, V); + let q = new Fg(V.shape, s, p, a), Z = null, ee = [V]; + s && ee.push(s), a && ee.push(a), p === "leakyrelu" && (Z = [{ type: "float32", data: [i] }], q.uniforms += " alpha : f32,"); + let oe = n.runWebGPUProgram(q, ee, V.dtype, Z); + R.push(V); + let J = me({ inputs: { x: oe }, backend: n, attrs: { shape: w } }); + R.push(oe); + for (let te of R) + n.disposeData(te.dataId); + return J; } break; } - case Ao.MatMulSmallOutputSizeProgram: - V = new $g(k, _, q, t6, o, s, p, a); + case Ro.MatMulSmallOutputSizeProgram: + L = new Rg(k, _, z, t10, o, s, p, a); break; - case Ao.MatMulPackedProgram: - let X = n.adapterInfo.isIntel(); - V = new _g(k, q, P, M, t6, o, s, p, a, X); + case Ro.MatMulPackedProgram: + let H = n.adapterInfo.isIntel(); + L = new Eg(k, z, t10, o, s, p, a, H); break; default: - throw new Error(`Unsupported MatMulProgramType ${H}.`); + throw new Error(`Unsupported MatMulProgramType ${U}.`); } - s && L.push(s), a && L.push(a), p === "leakyrelu" && (W.push({ type: "float32", data: [i] }), V.uniforms += " alpha : f32,"), U = n.runWebGPUProgram(V, L, r.dtype, W, U); - let j = de({ inputs: { x: U }, backend: n, attrs: { shape: w } }); - R.push(U); - for (let X of R) - n.disposeData(X.dataId); - return j; + s && P.push(s), a && P.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), V = n.runWebGPUProgram(L, P, r.dtype, M, V); + let K = me({ inputs: { x: V }, backend: n, attrs: { shape: w } }); + R.push(V); + for (let H of R) + n.disposeData(H.dataId); + return K; } -function lre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return Gu({ a: n, b: s, transposeA: p, transposeB: u, backend: t6, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function Lre(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return Xu({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var B3 = { kernelName: fo, backendName: "webgpu", kernelFunc: lre }; -var zl = class { - constructor(e, t6, o) { - this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t6, o), this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e; +var G3 = { kernelName: ho, backendName: "webgpu", kernelFunc: Lre }; +var Ul = class { + constructor(e, t10, o) { + this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e; } getUserCode() { return ` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Ic(this.op, false)} + ${kc(this.op, false)} } - ${se("index")} { + ${Q("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -27658,14 +27694,17 @@ var zl = class { `; } }; -var Hu = class { - constructor(e, t6, o) { - this.size = true, this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t6, o), this.dispatchLayout = ue(this.outputShape), this.op = e, this.useSharedMemoryWithA = t6.length <= 1 && o.length > 1 && t6[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t6.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB ? (this.isVec4 = false, this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t6[0], this.shaderKey = `binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`, this.type = "shared", this.workgroupSize = [256, 1, 1], this.workPerThread = 1) : (y.arraysEqual(t6, o) && y.sizeFromShape(t6) % 4 === 0 ? (this.isVec4 = true, this.type = "vec4", this.workPerThread = 4) : (this.isVec4 = false, this.type = "plain", this.workPerThread = 1), this.shaderKey = `binary_${this.type}_${e}`, this.workgroupSize = [128, 1, 1]), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); +var Yu = class { + constructor(e, t10, o) { + this.size = true, this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = ae(this.outputShape), this.op = e, this.useSharedMemoryWithA = t10.length <= 1 && o.length > 1 && t10[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t10.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB ? (this.isVec4 = false, this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t10[0], this.shaderKey = `binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`, this.type = "shared", this.workgroupSize = [256, 1, 1], this.workPerThread = 1) : (y.arraysEqual(t10, o) && y.sizeFromShape(t10) % 4 === 0 ? (this.isVec4 = true, this.type = "vec4", this.workPerThread = 4) : (this.isVec4 = false, this.type = "plain", this.workPerThread = 1), this.shaderKey = `binary_${this.type}_${e}`, this.workgroupSize = [128, 1, 1]), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); } getUserCode() { - let e, t6 = this.isVec4 ? "vec4" : "f32", o = ` - fn binaryOperation(a : ${t6}, b : ${t6}) -> ${t6} { - ${Ic(this.op, this.isVec4)} + let e, t10 = this.isVec4 ? "vec4" : "f32", o = ` + fn binaryOperation(a : ${t10}, b : ${t10}) -> ${t10} { + let isNaN = false; + { + ${kc(this.op, this.isVec4)} + } }; `; if (this.type === "shared") { @@ -27675,7 +27714,7 @@ var Hu = class { e = ` ${o} var sharedBuf : array; - ${se("index")} { + ${Q("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -27693,7 +27732,7 @@ var Hu = class { } else e = ` ${o} - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); let b = getBByOutputIndex(index); @@ -27704,28 +27743,28 @@ var Hu = class { return e; } }; -function Ft(r) { - let { inputs: e } = r, { x: t6 } = e; - return r.backend.incRef(t6.dataId), { dataId: t6.dataId, shape: t6.shape, dtype: t6.dtype }; +function Dt(r) { + let { inputs: e } = r, { x: t10 } = e; + return r.backend.incRef(t10.dataId), { dataId: t10.dataId, shape: t10.shape, dtype: t10.dtype }; } -var V3 = { kernelName: mo, backendName: "webgpu", kernelFunc: Ft }; +var H3 = { kernelName: mo, backendName: "webgpu", kernelFunc: Dt }; function po(r) { - let { inputs: e, backend: t6 } = r, { real: o, imag: n } = e, s = t6.makeTensorInfo(o.shape, "complex64"), a = t6.tensorMap.get(s.dataId), i = Ft({ inputs: { x: o }, backend: t6 }), p = Ft({ inputs: { x: n }, backend: t6 }); + let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.tensorMap.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t10 }), p = Dt({ inputs: { x: n }, backend: t10 }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var z3 = { kernelName: ei, backendName: "webgpu", kernelFunc: po }; -var Ro = class { - constructor(e, t6) { +var K3 = { kernelName: ri, backendName: "webgpu", kernelFunc: po }; +var Hr = class { + constructor(e, t10, o = "") { this.variableNames = ["A"], this.size = true; - let o = 128; - this.workgroupSize = [o, 1, 1], this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t6, this.shaderKey = `unary_${t6}`; + let n = 128; + this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t10, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t10}`; } getUserCode() { return ` fn unaryOperation(a : f32) -> f32 { - ${Ha(this.op, false)} + ${ja(this.op, false)} } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); @@ -27734,29 +27773,29 @@ var Ro = class { `; } }; -function Se({ opType: r, cpuKernelImpl: e, dtype: t6 }) { +function xe({ opType: r, cpuKernelImpl: e, dtype: t10 }) { return ({ inputs: o, backend: n }) => { - let { x: s } = o, a = n, i = t6 || s.dtype; + let { x: s } = o, a = n, i = t10 || s.dtype; if (a.shouldExecuteOnCPU([s]) && e != null) { let u = a.tensorMap.get(s.dataId), c = e(u.values, i); return a.makeTensorInfo(s.shape, i, c); } - let p = new Ro(s.shape, r); + let p = new Hr(s.shape, r); return a.runWebGPUProgram(p, [s], i); }; } -function ot({ opType: r, cpuKernelImpl: e, supportsComplex: t6 = false, dtype: o }) { +function Ze({ opType: r, cpuKernelImpl: e, supportsComplex: t10 = false, dtype: o }) { return ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; - if (t6 && a.dtype === "complex64") { + if (t10 && a.dtype === "complex64") { let l = p.tensorMap.get(a.dataId), m = p.tensorMap.get(i.dataId), d, f; - if (r !== ye.MUL) + if (r !== ge.MUL) [d, f] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => { - let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, w = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Hu(r, a.shape, i.shape); + let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, w = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Yu(r, a.shape, i.shape); return p.runWebGPUProgram(k, [C, w], dt(x.dtype, b.dtype)); }); else { - let g = new zl(ye.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new zl(ye.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; + let g = new Ul(ge.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new Ul(ge.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; d = p.runWebGPUProgram(g, b, "float32"), f = p.runWebGPUProgram(x, b, "float32"); } let h = po({ inputs: { real: d, imag: f }, backend: p }); @@ -27767,67 +27806,68 @@ function ot({ opType: r, cpuKernelImpl: e, supportsComplex: t6 = false, dtype: o let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? S.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? S.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u); return p.makeTensorInfo(g, u, h); } - let c = new Hu(r, a.shape, i.shape); + let c = new Yu(r, a.shape, i.shape); return p.runWebGPUProgram(c, [a, i], u); }; } -var { addImpl: W3, castImpl: U3, ceilImpl: G3, concatImpl: H3, equalImpl: q3, expImpl: K3, expm1Impl: j3, floorImpl: X3, gatherNdImpl: Y3, gatherV2Impl: Q3, greaterEqualImpl: Z3, greaterImpl: J3, lessEqualImpl: eM, lessImpl: tM, logImpl: rM, maxImpl: oM, maximumImpl: nM, minimumImpl: sM, multiplyImpl: aM, negImpl: iM, notEqualImpl: uM, prodImpl: pM, rangeImpl: cM, rsqrtImpl: lM, scatterImpl: mM, simpleAbsImpl: dM, sliceImpl: fM, stridedSliceImpl: hM, stringNGramsImpl: gM, subImpl: xM, tileImpl: yM, topKImpl: bM, transposeImpl: CM, uniqueImpl: kNt } = Qp; -var mre = Se({ opType: Q.ABS, cpuKernelImpl: dM }); -var SM = { kernelName: gs, backendName: "webgpu", kernelFunc: mre }; -var dre = Se({ opType: Q.ACOS }); -var wM = { kernelName: sa, backendName: "webgpu", kernelFunc: dre }; -var fre = Se({ opType: Q.ACOSH }); -var IM = { kernelName: aa, backendName: "webgpu", kernelFunc: fre }; -var hre = ot({ opType: ye.ADD, cpuKernelImpl: W3, supportsComplex: true }); -var vM = { kernelName: eo, backendName: "webgpu", kernelFunc: hre }; -var Dg = class { +var { addImpl: q3, castImpl: j3, ceilImpl: X3, concatImpl: Y3, equalImpl: Q3, expImpl: Z3, expm1Impl: J3, floorImpl: eM, gatherNdImpl: tM, gatherV2Impl: rM, greaterEqualImpl: oM, greaterImpl: nM, lessEqualImpl: sM, lessImpl: aM, logImpl: iM, maxImpl: uM, maximumImpl: pM, minimumImpl: cM, multiplyImpl: lM, negImpl: mM, notEqualImpl: dM, prodImpl: fM, rangeImpl: hM, rsqrtImpl: gM, scatterImpl: xM, simpleAbsImpl: yM, sliceImpl: bM, stridedSliceImpl: CM, stringNGramsImpl: SM, subImpl: wM, tileImpl: IM, topKImpl: vM, transposeImpl: kM, uniqueImpl: iTt } = Zp; +var Bre = xe({ opType: X.ABS, cpuKernelImpl: yM }); +var NM = { kernelName: ys, backendName: "webgpu", kernelFunc: Bre }; +var Vre = xe({ opType: X.ACOS }); +var TM = { kernelName: aa, backendName: "webgpu", kernelFunc: Vre }; +var zre = xe({ opType: X.ACOSH }); +var _M = { kernelName: ia, backendName: "webgpu", kernelFunc: zre }; +var Wre = Ze({ opType: ge.ADD, cpuKernelImpl: q3, supportsComplex: true }); +var $M = { kernelName: eo, backendName: "webgpu", kernelFunc: Wre }; +var Pg = class { constructor(e) { - this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t6, o) => `T${o}`), this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; + this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; } getUserCode() { let e = []; this.variableNames.forEach((n) => { e.push(`let v${n} = get${n}ByOutputCoords(coords);`); }); - let t6 = this.variableNames.map((n) => `v${n}`).join(" + "); + let t10 = this.variableNames.map((n) => `v${n}`).join(" + "); return ` - ${se("index")} { + ${Q("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); ${e.join(` `)} - setOutputAtIndex(flatIndex, ${t6}); + setOutputAtIndex(flatIndex, ${t10}); } } } `; } }; -function gre(r) { - let { inputs: e, backend: t6 } = r, o = e; +function Ure(r) { + let { inputs: e, backend: t10 } = r, o = e; if (o.length === 1) - return Ft({ inputs: { x: o[0] }, backend: t6 }); - let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Dg(s); - return t6.runWebGPUProgram(a, o, n); + return Dt({ inputs: { x: o[0] }, backend: t10 }); + let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Pg(s); + return t10.runWebGPUProgram(a, o, n); } -var kM = { kernelName: Mo, backendName: "webgpu", kernelFunc: gre }; -var Og = class { - constructor(e, t6) { +var EM = { kernelName: Po, backendName: "webgpu", kernelFunc: Ure }; +var Mg = class { + constructor(e, t10) { this.variableNames = ["A"], this.workgroupSize = [16, 16, 1]; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[t6[n]]; - this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared"; + o[n] = e[t10[n]]; + this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared"; } getUserCode() { - return y.assert(this.workgroupSize[0] === this.workgroupSize[1], () => `Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`), ` - const tileSize = ${this.workgroupSize[0]}; + y.assert(this.workgroupSize[0] === this.workgroupSize[1], () => `Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`); + let e = this.workgroupSize[0]; + return ` var tile : array, ${this.workgroupSize[0]}>; - ${se()} { - var x = i32(workgroupId.x) * tileSize + i32(localId.x); - var y = i32(workgroupId.y) * tileSize + i32(localId.y); + ${Q()} { + var x = i32(workgroupId.x) * ${e} + i32(localId.x); + var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { @@ -27835,8 +27875,8 @@ var Og = class { } workgroupBarrier(); - x = i32(workgroupId.y) * tileSize + i32(localId.x); - y = i32(workgroupId.x) * tileSize + i32(localId.y); + x = i32(workgroupId.y) * ${e} + i32(localId.x); + y = i32(workgroupId.x) * ${e} + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); @@ -27845,97 +27885,97 @@ var Og = class { `; } }; -var Pg = class { - constructor(e, t6) { +var Lg = class { + constructor(e, t10) { this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[t6[n]]; - this.outputShape = o, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t6, this.shaderKey = `transpose_${t6}`; + o[n] = e[t10[n]]; + this.outputShape = o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t10, this.shaderKey = `transpose_${t10}`; } getUserCode() { - let e = Rt(this.outputShape.length), t6 = xre(this.newDim); + let e = Rt(this.outputShape.length), t10 = Gre(this.newDim); return ` - ${se("index")} { + ${Q("index")} { for(var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { let resRC = getCoordsFromIndex(flatIndex); setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D( - ${e}(${t6}), uniforms.aShape)]); + ${e}(${t10}), uniforms.aShape)]); } } } `; } }; -function xre(r) { +function Gre(r) { let e = r.length; if (e > 6) throw Error(`Transpose for rank ${e} is not yet supported`); - let t6 = new Array(e); + let t10 = new Array(e); for (let o = 0; o < r.length; o++) - t6[r[o]] = `resRC.${$o(o)}`; - return t6.join(); + t10[r[o]] = `resRC.${Ao(o)}`; + return t10.join(); } function Nr(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t6, i = n.shape.length, p = new Array(i); + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; - if (t6.shouldExecuteOnCPU([n])) { - let l = a.tensorMap.get(n.dataId).values, m = CM(l, n.shape, n.dtype, s, p); - return t6.makeTensorInfo(p, n.dtype, m); + if (t10.shouldExecuteOnCPU([n])) { + let l = a.tensorMap.get(n.dataId).values, m = kM(l, n.shape, n.dtype, s, p); + return t10.makeTensorInfo(p, n.dtype, m); } if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) { - let c = new Og(n.shape, s); + let c = new Mg(n.shape, s); return a.runWebGPUProgram(c, [n], n.dtype); } - let u = new Pg(n.shape, s); + let u = new Lg(n.shape, s); return a.runWebGPUProgram(u, [n], n.dtype); } -var NM = { kernelName: ro, backendName: "webgpu", kernelFunc: Nr }; -var Mg = class { - constructor(e, t6) { +var AM = { kernelName: ro, backendName: "webgpu", kernelFunc: Nr }; +var Bg = class { + constructor(e, t10) { this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize]; let [o] = S.computeOutAndReduceShapes(this.inputShape, [1]); - this.outputShape = o.length === 0 ? [1] : o, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t6, this.shaderKey = `reduce_${t6}`; + this.outputShape = o.length === 0 ? [1] : o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t10, this.shaderKey = `reduce_${t10}`; } getUserCode() { - let e = "", t6 = "0.0"; + let e = "", t10 = "0.0", o = this.workgroupSize[0]; this.reduceType === "min" || this.reduceType === "max" ? (e = ` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue) - { bestValue = candidate; }`, t6 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t6 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t6 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t6 = "0.0"); - let o = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);"; + { bestValue = candidate; }`, t10 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t10 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t10 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t10 = "0.0"); + let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);"; return ` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` - var xBestValues : array; + var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${se("index")} { - let outputIndex = index / i32(workgroupSizeX); + ${Q("index")} { + let outputIndex = index / ${o}; let offset = getOffset(outputIndex); - var bestValue = ${t6}; + var bestValue = ${t10}; let Length = uniforms.reduceSize; - let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX); + let WorkPerThread = DIV_CEIL(u32(Length), ${o}u); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { + k = k + ${o}) { let candidate = f32(x[offset + k]); ${e} } xBestValues[localId.x] = bestValue; workgroupBarrier(); - var reduceSize = min(u32(Length), workgroupSizeX); + var reduceSize = min(u32(Length), ${o}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -27949,65 +27989,65 @@ var Mg = class { } if (localId.x == 0u && outputIndex < uniforms.size) { - ${o} + ${n} } } `; } }; -function qr(r, e, t6, o, n) { +function Kr(r, e, t10, o, n) { let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = S.getAxesPermutation(p, s), c = r; u != null && (c = Nr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = S.getInnerMostAxes(p.length, s), a.push(c)), S.assertAxesAreInnerMostDims(o, p, s); let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = l; - t6 && (d = S.expandShapeToKeepDim(l, i)); + t10 && (d = S.expandShapeToKeepDim(l, i)); let f; if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) { let h = n.tensorMap.get(c.dataId).values; switch (o) { case "max": - let g = oM(h, y.sizeFromShape(m), d, r.dtype); + let g = uM(h, y.sizeFromShape(m), d, r.dtype); f = n.makeTensorInfo(d, r.dtype, g); break; case "prod": - let { outVals: x, outShape: b, outDtype: C } = pM(c.shape, c.dtype, h, p); + let { outVals: x, outShape: b, outDtype: C } = fM(c.shape, c.dtype, h, p); f = n.makeTensorInfo(b, C, x); break; default: throw new Error(`${o} CPU implementation is not yet supported.`); } } else { - let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = o === "mean" ? "float32" : ka(r.dtype), w = [{ type: "int32", data: [h] }], k = new Mg(b, o), _ = n.runWebGPUProgram(k, [c], C, w); - a.push(_), f = de({ inputs: { x: _ }, attrs: { shape: d }, backend: n }); + let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = o === "mean" ? "float32" : Ta(r.dtype), w = [{ type: "int32", data: [h] }], k = new Bg(b, o), _ = n.runWebGPUProgram(k, [c], C, w); + a.push(_), f = me({ inputs: { x: _ }, attrs: { shape: d }, backend: n }); } return a.forEach((h) => n.disposeData(h.dataId)), f; } -function yre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return qr(n, a, s, "all", t6); +function Hre(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Kr(n, a, s, "all", t10); } -var TM = { kernelName: Lo, backendName: "webgpu", kernelFunc: yre }; -function bre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return qr(n, a, s, "any", t6); +var RM = { kernelName: Mo, backendName: "webgpu", kernelFunc: Hre }; +function Kre(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Kr(n, a, s, "any", t10); } -var _M = { kernelName: Bo, backendName: "webgpu", kernelFunc: bre }; -var vc = class { - constructor(e, t6, o) { +var DM = { kernelName: Lo, backendName: "webgpu", kernelFunc: Kre }; +var Nc = class { + constructor(e, t10, o) { this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true; - let n = [t6]; + let n = [t10]; this.op = o === "min" ? "<" : ">"; let [s, a] = S.computeOutAndReduceShapes(e, n); - this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = ue(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = re(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`; + this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = ae(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = Y(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`; } getUserCode() { - let e = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${$o(this.inputShape.length - 1)}`, t6 = () => { - let o = ""; + let e = this.workgroupSize[0], t10 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Ao(this.inputShape.length - 1)}`, o = () => { + let n = ""; if (this.outputShape.length === 1) - this.inputShape.length !== 1 && (o += "outputCoords,"); + this.inputShape.length !== 1 && (n += "outputCoords,"); else - for (let n = 0; n < this.outputShape.length; n++) - o += `outputCoords.${$o(n)},`; - return o; + for (let s = 0; s < this.outputShape.length; s++) + n += `outputCoords.${Ao(s)},`; + return n; }; return this.type === "shared" ? ` fn DIV_CEIL(a : u32, b : u32) -> u32 { @@ -28015,20 +28055,20 @@ var vc = class { } ${` - var xBestIndices : array; - var xBestValues : array; + var xBestIndices : array; + var xBestValues : array; `} - ${se("index")} { - let outputIndex = index / i32(workgroupSizeX); - let reduceLength = ${e()}; + ${Q("index")} { + let outputIndex = index / ${e}; + let reduceLength = ${t10()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; let outputCoords = getCoordsFromIndex(outputIndex); for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { - let candidate = getX(${t6()} k); + k = k + ${e}) { + let candidate = getX(${o()} k); if (!isnan(candidate) && candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = k; @@ -28038,7 +28078,7 @@ var vc = class { xBestIndices[localId.x] = bestIndex; workgroupBarrier(); - var reduceSize = min(u32(reduceLength), workgroupSizeX); + var reduceSize = min(u32(reduceLength), ${e}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -28059,14 +28099,14 @@ var vc = class { } } ` : ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; - var bestValue = getX(${t6()} 0); - let reduceLength = ${e()}; + var bestValue = getX(${o()} 0); + let reduceLength = ${t10()}; for (var i = 1; i < reduceLength; i++) { - let candidate = getX(${t6()} i); + let candidate = getX(${o()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; @@ -28078,40 +28118,40 @@ var vc = class { `; } }; -function Cre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Nr({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = new vc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t6.runWebGPUProgram(c, [p], "int32", l); - return u.forEach((d) => t6.disposeData(d.dataId)), m; +function qre(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = new Nc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l); + return u.forEach((d) => t10.disposeData(d.dataId)), m; } -var EM = { kernelName: Vo, backendName: "webgpu", kernelFunc: Cre }; -function Sre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Nr({ inputs: { x: n }, backend: t6, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = new vc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t6.runWebGPUProgram(c, [p], "int32", l); - return u.forEach((d) => t6.disposeData(d.dataId)), m; +var FM = { kernelName: Bo, backendName: "webgpu", kernelFunc: qre }; +function jre(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = new Nc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l); + return u.forEach((d) => t10.disposeData(d.dataId)), m; } -var $M = { kernelName: Za, backendName: "webgpu", kernelFunc: Sre }; -var wre = Se({ opType: Q.ASIN }); -var AM = { kernelName: ia, backendName: "webgpu", kernelFunc: wre }; -var Ire = Se({ opType: Q.ASINH }); -var RM = { kernelName: ua, backendName: "webgpu", kernelFunc: Ire }; -var vre = Se({ opType: Q.ATAN }); -var FM = { kernelName: pa, backendName: "webgpu", kernelFunc: vre }; -var kre = ot({ opType: ye.ATAN2 }); -var DM = { kernelName: la, backendName: "webgpu", kernelFunc: kre }; -var Nre = Se({ opType: Q.ATANH }); -var OM = { kernelName: ca, backendName: "webgpu", kernelFunc: Nre }; -var Wl = class { - constructor(e, t6) { - this.variableNames = ["x"], this.uniforms = "stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,", this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `pool2D_${t6}`, this.poolType = t6; +var OM = { kernelName: ei, backendName: "webgpu", kernelFunc: jre }; +var Xre = xe({ opType: X.ASIN }); +var PM = { kernelName: ua, backendName: "webgpu", kernelFunc: Xre }; +var Yre = xe({ opType: X.ASINH }); +var MM = { kernelName: pa, backendName: "webgpu", kernelFunc: Yre }; +var Qre = xe({ opType: X.ATAN }); +var LM = { kernelName: ca, backendName: "webgpu", kernelFunc: Qre }; +var Zre = Ze({ opType: ge.ATAN2 }); +var BM = { kernelName: ma, backendName: "webgpu", kernelFunc: Zre }; +var Jre = xe({ opType: X.ATANH }); +var VM = { kernelName: la, backendName: "webgpu", kernelFunc: Jre }; +var Gl = class { + constructor(e, t10) { + this.variableNames = ["x"], this.uniforms = "stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,", this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `pool2D_${t10}`, this.poolType = t10; } getUserCode() { let e = "resultValue = max(value, resultValue);"; this.poolType === "avg" && (e = "resultValue = resultValue + value; count = count + 1.0;"); - let t6 = "resultValue"; - return this.poolType === "avg" && (t6 = "resultValue / count"), ` - ${se("index")} { + let t10 = "resultValue"; + return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), ` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -28140,19 +28180,19 @@ var Wl = class { } } - setOutputAtIndex(index, ${t6}); + setOutputAtIndex(index, ${t10}); } } `; } }; -var Lg = class { +var Vg = class { constructor(e) { - this.variableNames = ["x"], this.uniforms = "stride : vec2,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne"; + this.variableNames = ["x"], this.uniforms = "stride : vec2,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -28169,122 +28209,167 @@ var Lg = class { `; } }; -function Ul(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o; - return qr(n, s, a, "max", t6); +function Hl(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o; + return Kr(n, s, a, "max", t10); } -var PM = { kernelName: yn, backendName: "webgpu", kernelFunc: Ul }; -function uI(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; - return qr(n, a, s, "mean", t6); +var zM = { kernelName: yn, backendName: "webgpu", kernelFunc: Hl }; +function yI(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o; + return Kr(n, a, s, "mean", t10); } -var MM = { kernelName: Sn, backendName: "webgpu", kernelFunc: uI }; -function Bg(r, e, t6, o) { +var WM = { kernelName: Sn, backendName: "webgpu", kernelFunc: yI }; +function zg(r, e, t10, o) { if (e.filterWidth === 1 && e.filterHeight === 1 && y.arraysEqual(e.inShape, e.outShape)) - return Ft({ inputs: { x: r }, backend: o }); + return Dt({ inputs: { x: r }, backend: o }); if (e.filterWidth === e.inWidth && e.filterHeight === e.inHeight && e.batchSize === 1 && e.padInfo.type === "VALID") { - let a = r.shape.length, i = de({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p; - t6 === "avg" ? p = uI({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t6 === "max", () => `Invalid pool type ${t6}`), p = Ul({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); - let u = de({ inputs: { x: p }, backend: o, attrs: { shape: e.outShape } }); + let a = r.shape.length, i = me({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p; + t10 === "avg" ? p = yI({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), p = Hl({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); + let u = me({ inputs: { x: p }, backend: o, attrs: { shape: e.outShape } }); return o.disposeData(i.dataId), o.disposeData(p.dataId), u; } let n, s = [{ type: "int32", data: [e.strideHeight, e.strideWidth] }]; - return e.filterHeight === 1 && e.filterWidth === 1 ? n = new Lg(e) : (t6 === "avg" ? n = new Wl(e, "avg") : (y.assert(t6 === "max", () => `Invalid pool type ${t6}`), n = new Wl(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s); + return e.filterHeight === 1 && e.filterWidth === 1 ? n = new Vg(e) : (t10 === "avg" ? n = new Gl(e, "avg") : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), n = new Gl(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s); } -function Tre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p); - return Bg(n, c, "avg", t6); +function eoe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p); + return zg(n, c, "avg", t10); } -var LM = { kernelName: zo, backendName: "webgpu", kernelFunc: Tre }; -function _re(r) { - let { inputs: e, backend: t6, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; - return Gu({ a: n, b: s, transposeA: a, transposeB: i, backend: t6 }); -} -var BM = { kernelName: Wo, backendName: "webgpu", kernelFunc: _re }; -var Vg = class { - constructor(e, t6) { - this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6, this.rank = t6.length, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Rt(e.length)}, `, this.shaderKey = "slice"; +var UM = { kernelName: Vo, backendName: "webgpu", kernelFunc: eoe }; +var Wg = class { + constructor(e) { + this.variableNames = ["dy"], this.uniforms = `stride : vec2, pads : vec2, dilation : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avg_pool2d_backprop"; } getUserCode() { - let e = Rt(this.rank), t6 = Ere(this.rank), o; - return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`), ` - ${se("index")} { + return ` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let batch = coords[0]; + let d = coords[3]; + + let dyRCCorner = vec2(coords.yz) - uniforms.pads; + let dyRCorner = dyRCCorner.x; + let dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) { + let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]); + + if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { + continue; + } + let idyR = i32(dyR); + + for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) { + let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]); + + if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { + continue; + } + let idyC = i32(dyC); + + let dyValue = getDy(batch, idyR, idyC, d); + + dotProd = dotProd + dyValue * uniforms.avgMultiplier; + } + } + setOutputAtIndex(index, dotProd); + } + } + `; + } +}; +function toe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s; + fI([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = new Wg(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }]; + return t10.runWebGPUProgram(l, [n], a.dtype, d); +} +var GM = { kernelName: mp, backendName: "webgpu", kernelFunc: toe }; +function roe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o; + return Xu({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 }); +} +var HM = { kernelName: zo, backendName: "webgpu", kernelFunc: roe }; +var Ug = class { + constructor(e, t10) { + this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.rank = t10.length, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Rt(e.length)}, `, this.shaderKey = "slice"; + } + getUserCode() { + let e = Rt(this.rank), t10 = ooe(this.rank), o; + return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`), ` + ${Q("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); ${o.join(` `)} - setOutputAtIndex(index, getSource(${t6})); + setOutputAtIndex(index, getSource(${t10})); } } `; } }; -var pI = ["x", "y", "z", "w", "u", "v"]; -function Ere(r) { +var bI = ["x", "y", "z", "w", "u", "v"]; +function ooe(r) { if (r === 1) return "sourceLoc"; if (r <= 6) - return pI.slice(0, r).map((e) => `sourceLoc.${e}`).join(","); + return bI.slice(0, r).map((e) => `sourceLoc.${e}`).join(","); throw Error(`Slicing for rank ${r} is not yet supported`); } -function ds(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a); - if (ut.assertParamsValid(n, i, p), t6.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = t6.tensorMap.get(n.dataId), m = fM(l.values, i, p, n.shape, n.dtype); - return t6.makeTensorInfo(p, n.dtype, m); +function fs(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a); + if (ut.assertParamsValid(n, i, p), t10.shouldExecuteOnCPU([n]) || n.dtype === "string") { + let l = t10.tensorMap.get(n.dataId), m = bM(l.values, i, p, n.shape, n.dtype); + return t10.makeTensorInfo(p, n.dtype, m); } if (y.sizeFromShape(p) === 0) - return t6.makeTensorInfo(p, n.dtype, []); - let u = new Vg(i, p), c = [{ type: "int32", data: i }]; - return t6.runWebGPUProgram(u, [n], n.dtype, c); + return t10.makeTensorInfo(p, n.dtype, []); + let u = new Ug(i, p), c = [{ type: "int32", data: i }]; + return t10.runWebGPUProgram(u, [n], n.dtype, c); } -var VM = { kernelName: _s, backendName: "webgpu", kernelFunc: ds }; -var $re = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; +var KM = { kernelName: Es, backendName: "webgpu", kernelFunc: fs }; +var noe = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet"); - let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = de({ inputs: { x: n }, backend: t6, attrs: { shape: p } }), h = Nr({ inputs: { x: f }, backend: t6, attrs: { perm: u } }), g = de({ inputs: { x: h }, backend: t6, attrs: { shape: c } }), x = ds({ inputs: { x: g }, backend: t6, attrs: { begin: l, size: m } }); - return d.push(f), d.push(h), d.push(g), d.forEach((b) => t6.disposeData(b.dataId)), x; + let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = me({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = Nr({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = me({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = fs({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } }); + return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeData(b.dataId)), x; }; -var zM = { kernelName: xs, backendName: "webgpu", kernelFunc: $re }; -var Are = ` +var qM = { kernelName: bs, backendName: "webgpu", kernelFunc: noe }; +var soe = ` fn bincount_write(index: i32, value: f32) { - var oldValue = atomicLoad(& (result[index])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + value; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak( - &(result[index]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } + ${Ic("&result[index]", "value", "float32")} } `; -var Rre = ` +var aoe = ` fn bincount_write(index: i32, value: f32) { - result[index] = value; + atomicStore(&result[index], bitcast(value)); } `; -var kc = class { - constructor(e, t6, o = false) { - this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t6, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`; +var Tc = class { + constructor(e, t10, o = false) { + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t10, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`; } getUserCode() { return ` - ${this.binaryOutput ? Rre : Are} - ${se("index")} { + ${this.binaryOutput ? aoe : soe} + ${Q("index")} { ${this.rank === 1 ? `if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput ? 1 : this.hasWeights ? "f32(getW(index))" : "1."}; + let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(index)" : "1."}; bincount_write(indexVal, value); } }` : `let coord = getCoordsFromIndex(index); if (coordsInBounds2D(coord, uniforms.xShape)) { let indexVal = i32(getX(coord[0], coord[1])); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput ? 1 : this.hasWeights ? "f32(getW(coord[0], coord[1]))" : "1."}; + let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(coord[0], coord[1])" : "1."}; bincount_write(coord.x * uniforms.binCountSize + indexVal, value); } }`} @@ -28292,84 +28377,78 @@ var kc = class { `; } }; -function Fre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = dr({ backend: t6, attrs: { shape: c, value: 0, dtype: l } }), d = new kc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; - return t6.runWebGPUProgram(d, h, l, f, m); +function ioe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = fr({ backend: t10, attrs: { shape: c, value: 0, dtype: l } }), d = new Tc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; + return t10.runWebGPUProgram(d, h, l, f, m); } -var WM = { kernelName: Ja, backendName: "webgpu", kernelFunc: Fre }; -var cI = ot({ opType: ye.NOT_EQUAL, dtype: "bool", cpuKernelImpl: uM }); -var UM = { kernelName: Nn, backendName: "webgpu", kernelFunc: cI }; -function qa(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.tensorMap.get(o.dataId); - return Ft({ inputs: { x: n.complexTensorInfos.real }, backend: t6 }); +var jM = { kernelName: ti, backendName: "webgpu", kernelFunc: ioe }; +var CI = Ze({ opType: ge.NOT_EQUAL, dtype: "bool", cpuKernelImpl: dM }); +var XM = { kernelName: Nn, backendName: "webgpu", kernelFunc: CI }; +function Xa(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 }); } -var GM = { kernelName: ai, backendName: "webgpu", kernelFunc: qa }; -function HM(r, e) { - let t6 = new Ro(r.shape, Q.TO_INT), o = e.runWebGPUProgram(t6, [r], "int32"); +var YM = { kernelName: di, backendName: "webgpu", kernelFunc: Xa }; +function QM(r, e) { + let t10 = new Hr(r.shape, X.TO_INT), o = e.runWebGPUProgram(t10, [r], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function lI(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dtype: s } = o; +function SI(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return Ft({ inputs: { x: n }, backend: t6 }); - let a = Vr(n.shape), i = lI({ inputs: { x: n }, backend: t6, attrs: { dtype: "float32" } }), p = po({ inputs: { real: i, imag: a }, backend: t6 }); - return a.dispose(), t6.disposeData(i.dataId), p; + return Dt({ inputs: { x: n }, backend: t10 }); + let a = Br(n.shape), i = SI({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = po({ inputs: { real: i, imag: a }, backend: t10 }); + return a.dispose(), t10.disposeData(i.dataId), p; } if (n.dtype === "complex64") { - let a = qa({ inputs: { input: n }, backend: t6 }), i = lI({ inputs: { x: a }, backend: t6, attrs: { dtype: s } }); - return t6.disposeData(a.dataId), i; + let a = Xa({ inputs: { input: n }, backend: t10 }), i = SI({ inputs: { x: a }, backend: t10, attrs: { dtype: s } }); + return t10.disposeData(a.dataId), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = Ft({ inputs: { x: n }, backend: t6 }); + let a = Dt({ inputs: { x: n }, backend: t10 }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } - if (t6.shouldExecuteOnCPU([n])) { - let a = t6.tensorMap.get(n.dataId).values, [i, p, u] = U3(a, n.shape, n.dtype, s); - return t6.makeTensorInfo(i, p, u); + if (t10.shouldExecuteOnCPU([n])) { + let a = t10.tensorMap.get(n.dataId).values, [i, p, u] = j3(a, n.shape, n.dtype, s); + return t10.makeTensorInfo(i, p, u); } if (s === "int32") - return HM(n, t6); + return QM(n, t10); if (s === "bool") { - let a = t6.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = cI({ inputs: { a: n, b: a }, backend: t6 }); - return t6.disposeData(a.dataId), p; + let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = CI({ inputs: { a: n, b: a }, backend: t10 }); + return t10.disposeData(a.dataId), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var qM = { kernelName: co, backendName: "webgpu", kernelFunc: lI }; -var Dre = Se({ opType: Q.CEIL, cpuKernelImpl: G3 }); -var KM = { kernelName: Uo, backendName: "webgpu", kernelFunc: Dre }; -var zg = class { +var ZM = { kernelName: co, backendName: "webgpu", kernelFunc: SI }; +var uoe = xe({ opType: X.CEIL, cpuKernelImpl: X3 }); +var JM = { kernelName: Wo, backendName: "webgpu", kernelFunc: uoe }; +var Gg = class { constructor(e) { - this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4"; + this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); - var clampedValue : vec4; - for (var i = 0; i < 4; i = i + 1) { - if (isnan(value[i])) { - clampedValue[i] = value[i]; - } else { - clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); - } - } - + var clampedValue = clamp( + value, vec4(uniforms.minVal), vec4(uniforms.maxVal)); + clampedValue = select(clampedValue, value, isnanVec4(value)); setOutputAtIndex(index, clampedValue); } } `; } }; -var Wg = class { +var Hg = class { constructor(e) { - this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip"; + this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -28382,16 +28461,16 @@ var Wg = class { `; } }; -function Ore(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; - return y.sizeFromShape(n.shape) % 4 === 0 ? i = new zg(n.shape) : i = new Wg(n.shape), t6.runWebGPUProgram(i, [n], n.dtype, p); +function poe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; + return y.sizeFromShape(n.shape) % 4 === 0 ? i = new Gg(n.shape) : i = new Hg(n.shape), t10.runWebGPUProgram(i, [n], n.dtype, p); } -var jM = { kernelName: lo, backendName: "webgpu", kernelFunc: Ore }; -var Ug = class { +var eL = { kernelName: lo, backendName: "webgpu", kernelFunc: poe }; +var Kg = class { constructor(e) { - this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t6, o) => `T${o}`), this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1; - for (let t6 = 0; t6 < this.offsetLength; t6++) - this.uniforms += `offset${t6} : i32,`; + this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1; + for (let t10 = 0; t10 < this.offsetLength; t10++) + this.uniforms += `offset${t10} : i32,`; this.shaderKey = "concat"; } getUserCode() { @@ -28405,7 +28484,7 @@ var Ug = class { } else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));"); return ` - ${se("index")} { + ${Q("index")} { for(var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { @@ -28421,63 +28500,63 @@ var Ug = class { `; } }; -function qu(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e, n = t6.tensorMap.get(o.dataId); - return Ft({ inputs: { x: n.complexTensorInfos.imag }, backend: t6 }); +function Qu(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 }); } -var XM = { kernelName: si, backendName: "webgpu", kernelFunc: qu }; -function Nc(r, e, t6) { +var tL = { kernelName: ci, backendName: "webgpu", kernelFunc: Qu }; +function _c(r, e, t10) { let o = r[0].dtype; if (o === "complex64") { - let f = r.map((C) => qa({ inputs: { input: C }, backend: t6 })), h = r.map((C) => qu({ inputs: { input: C }, backend: t6 })), g = Nc(f, e, t6), x = Nc(h, e, t6), b = po({ inputs: { real: g, imag: x }, backend: t6 }); - return f.forEach((C) => t6.disposeData(C.dataId)), h.forEach((C) => t6.disposeData(C.dataId)), t6.disposeData(g.dataId), t6.disposeData(x.dataId), b; + let f = r.map((C) => Xa({ inputs: { input: C }, backend: t10 })), h = r.map((C) => Qu({ inputs: { input: C }, backend: t10 })), g = _c(f, e, t10), x = _c(h, e, t10), b = po({ inputs: { real: g, imag: x }, backend: t10 }); + return f.forEach((C) => t10.disposeData(C.dataId)), h.forEach((C) => t10.disposeData(C.dataId)), t10.disposeData(g.dataId), t10.disposeData(x.dataId), b; } - let n = t6.shouldExecuteOnCPU(r); + let n = t10.shouldExecuteOnCPU(r); if (o === "string" && (n = true), n) { let f = r.map((k) => { - let $ = [-1, y.sizeFromShape(k.shape.slice(e))]; - return de({ inputs: { x: k }, backend: t6, attrs: { shape: $ } }); - }), h = f.map((k) => ({ vals: t6.readSync(k.dataId), shape: k.shape })), g = S.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = H3(h, g, o, x), C = S.computeOutShape(r.map((k) => k.shape), e), w = t6.makeTensorInfo(C, o, b); - return f.forEach((k) => t6.disposeData(k.dataId)), w; + let E = [-1, y.sizeFromShape(k.shape.slice(e))]; + return me({ inputs: { x: k }, backend: t10, attrs: { shape: E } }); + }), h = f.map((k) => ({ vals: t10.readSync(k.dataId), shape: k.shape })), g = S.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = Y3(h, g, o, x), C = S.computeOutShape(r.map((k) => k.shape), e), w = t10.makeTensorInfo(C, o, b); + return f.forEach((k) => t10.disposeData(k.dataId)), w; } - let s = t6.device.limits.maxStorageBuffersPerShaderStage - 1; + let s = t10.device.limits.maxStorageBuffersPerShaderStage - 1; if (r.length > s) { let f = []; for (let g = 0; g < r.length; g += s) { let x = r.slice(g, g + s); - f.push(Nc(x, e, t6)); + f.push(_c(x, e, t10)); } - let h = Nc(f, e, t6); + let h = _c(f, e, t10); for (let g of f) - t6.disposeData(g.dataId); + t10.disposeData(g.dataId); return h; } - let { tensors2D: a, outShape: i } = Pre(r, e, t6), p = a.map((f) => f.shape), u = new Ug(p), c = [], l = new Array(p.length - 1); + let { tensors2D: a, outShape: i } = coe(r, e, t10), p = a.map((f) => f.shape), u = new Kg(p), c = [], l = new Array(p.length - 1); if (l.length > 0) { l[0] = p[0][1], c.push({ type: "int32", data: [l[0]] }); for (let f = 1; f < l.length; f++) l[f] = l[f - 1] + p[f][1], c.push({ type: "int32", data: [l[f]] }); } - let m = t6.runWebGPUProgram(u, a, a[0].dtype, c); - a.forEach((f) => t6.disposeData(f.dataId)); - let d = de({ inputs: { x: m }, backend: t6, attrs: { shape: i } }); - return t6.disposeData(m.dataId), d; + let m = t10.runWebGPUProgram(u, a, a[0].dtype, c); + a.forEach((f) => t10.disposeData(f.dataId)); + let d = me({ inputs: { x: m }, backend: t10, attrs: { shape: i } }); + return t10.disposeData(m.dataId), d; } -function Pre(r, e, t6) { +function coe(r, e, t10) { let o = S.computeOutShape(r.map((s) => s.shape), e); - return { tensors2D: r.map((s) => de({ inputs: { x: s }, backend: t6, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o }; + return { tensors2D: r.map((s) => me({ inputs: { x: s }, backend: t10, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o }; } -function mI(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); +function wI(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape); S.assertParamsConsistent(a, s); let i = S.computeOutShape(e.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) - return t6.makeTensorInfo(i, e[0].dtype, []); + return t10.makeTensorInfo(i, e[0].dtype, []); let p = e.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? Ft({ inputs: { x: p[0] }, backend: t6 }) : Nc(p, s, t6); + return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: t10 }) : _c(p, s, t10); } -var YM = { kernelName: ys, backendName: "webgpu", kernelFunc: mI }; -function Mre(r, e, t6, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { +var rL = { kernelName: Cs, backendName: "webgpu", kernelFunc: wI }; +function loe(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { let c = (R) => { switch (R) { case 1: @@ -28540,21 +28619,21 @@ function Mre(r, e, t6, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} } - return ${kt(i)}(0.0);` : o && t6 ? ` + return ${kt(i)}(0.0);` : o && t10 ? ` let col = colIn * ${i}; ${b}` : ` let col = colIn * ${i}; if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } - return ${kt(i)}(0.0);`, w = `${l(p)}`, k = kt(u), _ = r ? kt(i) : kt(p), $ = r ? kt(p) : kt(i); + return ${kt(i)}(0.0);`, w = `${l(p)}`, k = kt(u), _ = r ? kt(i) : kt(p), E = r ? kt(p) : kt(i); return ` - ${ur(s, a, u === 4, 4)} + ${pr(s, a, u === 4, 4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} { ${r ? C : w} } - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} { + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} { ${r ? w : C} } @@ -28565,30 +28644,30 @@ function Mre(r, e, t6, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { var value = valueIn; let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; ${d} - ${Hr(n, s)} + ${Gr(n, s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`; } -var Gg = class { - constructor(e, t6, o, n, s = false, a = null, i = false, p = false) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = Ml(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Ll(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableTypes = ["f32", "vec4"]) : (this.innerElementSize = 4, this.variableTypes = ["vec4", "vec4"]), s && (this.variableNames.push("bias"), this.variableTypes.push("vec4")), i && (this.variableNames.push("preluActivationWeights"), this.variableTypes.push("vec4"))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t6 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; +var qg = class { + constructor(e, t10, o, n, s = false, a = null, i = false, p = false) { + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = Bl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Vl(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableTypes = ["f32", "vec4"]) : (this.innerElementSize = 4, this.variableTypes = ["vec4", "vec4"]), s && (this.variableNames.push("bias"), this.variableTypes.push("vec4")), i && (this.variableNames.push("preluActivationWeights"), this.variableTypes.push("vec4"))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t10 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; } getUserCode() { - let e = this.isVec4 ? Wu(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : Uu(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t6 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; + let e = this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : ju(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t10 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; return ` - ${Mre(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t6[0], t6[1], t6[2])} + ${loe(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t10[0], t10[1], t10[2])} ${e} `; } }; -var Hg = class { - constructor(e, t6 = false, o = null, n = false) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t6, this.activation = o, this.hasPreluActivationWeights = n, t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`; +var jg = class { + constructor(e, t10 = false, o = null, n = false) { + this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10, this.activation = o, this.hasPreluActivationWeights = n, t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`; } getUserCode() { return ` - ${ur(this.activation, this.hasPreluActivationWeights, false, 4)} + ${pr(this.activation, this.hasPreluActivationWeights, false, 4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -28609,11 +28688,11 @@ var Hg = class { let coords = ${this.isChannelsLast ? "vec4(batch, row, col, chan);" : "vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${Hr(this.addBias, this.activation)} + ${Gr(this.addBias, this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${se("index")} { + ${Q("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast ? "coords[3];" : "coords[1];"} @@ -28636,15 +28715,15 @@ var Hg = class { `; } }; -var qg = class { - constructor(e, t6) { +var Xg = class { + constructor(e, t10) { this.variableNames = ["x"], this.uniforms = `pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t6, this.shaderKey = `im2col_${this.isChannelsLast}`; + inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t10, this.shaderKey = `im2col_${this.isChannelsLast}`; } getUserCode() { - let e = this.isChannelsLast ? 1 : 2, t6 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)"; + let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)"; return ` - ${se("index")} { + ${Q("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -28659,7 +28738,7 @@ var qg = class { let xCol = offsetX + uniforms.dilation[1] * ((col % uniforms.itemsPerBlockRow) / uniforms.inChannels); let ch = col % uniforms.inChannels; - if(xCol < uniforms.xShape[${t6}] && xCol >= 0) { + if(xCol < uniforms.xShape[${t10}] && xCol >= 0) { value = ${s}; } } @@ -28669,78 +28748,190 @@ var qg = class { `; } }; -function Kg(r, e) { - let t6 = r.length; - return t6 >= 3 ? e ? [...r.slice(0, -3), r[t6 - 3] * r[t6 - 2], r[t6 - 1]] : [...r.slice(0, -3), r[t6 - 3], r[t6 - 2] * r[t6 - 1]] : !e && t6 === 1 && r[0] > 1 ? [r[0], 1] : null; +function Yg(r, e) { + let t10 = r.length; + return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null; } -function Lre({ x: r, filter: e, convInfo: t6, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = t6.dataFormat === "channelsLast", u = !p, c = false, l = p && t6.filterHeight === t6.inHeight && t6.filterWidth === t6.inWidth && t6.padInfo.type === "VALID", m = [], d, f; +function moe({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = t10.dataFormat === "channelsLast", u = !p, c = false, l = p && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = [], d, f; if (l) { - let x = t6.inHeight * t6.inWidth * t6.inChannels; - d = de({ inputs: { x: r }, backend: o, attrs: { shape: [1, t6.batchSize, x] } }), f = de({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t6.outChannels] } }); + let x = t10.inHeight * t10.inWidth * t10.inChannels; + d = me({ inputs: { x: r }, backend: o, attrs: { shape: [1, t10.batchSize, x] } }), f = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t10.outChannels] } }); } else - d = de({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t6.batchSize, t6.inHeight * t6.inWidth, t6.inChannels] : [t6.batchSize, t6.inChannels, t6.inHeight * t6.inWidth] } }), f = de({ inputs: { x: e }, backend: o, attrs: { shape: [1, t6.inChannels, t6.outChannels] } }); + d = me({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t10.batchSize, t10.inHeight * t10.inWidth, t10.inChannels] : [t10.batchSize, t10.inChannels, t10.inHeight * t10.inWidth] } }), f = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }); if (m.push(d), m.push(f), s != null) { - let x = Kg(s.shape, p); - x != null && (s = de({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s)); + let x = Yg(s.shape, p); + x != null && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s)); } if (n != null) { - let x = Kg(n.shape, p); - x != null && (n = de({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n)); + let x = Yg(n.shape, p); + x != null && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n)); } - let h = Gu({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = de({ inputs: { x: h }, backend: o, attrs: { shape: t6.outShape } }); + let h = Xu({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = me({ inputs: { x: h }, backend: o, attrs: { shape: t10.outShape } }); m.push(h); for (let x of m) o.disposeData(x.dataId); return g; } -function Bre({ x: r, filter: e, convInfo: t6, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t6, C = b === "channelsLast", w = p * u * c, k = h * f, _ = C ? [t6.batchSize, k, w] : [t6.batchSize, w, k], $ = new qg(_, C), A = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], R = o.runWebGPUProgram($, [r], r.dtype, A), D = []; +function doe({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t10, C = b === "channelsLast", w = p * u * c, k = h * f, _ = C ? [t10.batchSize, k, w] : [t10.batchSize, w, k], E = new Xg(_, C), A = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], R = o.runWebGPUProgram(E, [r], r.dtype, A), D = []; D.push(R); - let P = de({ inputs: { x: e }, backend: o, attrs: { shape: [1, w, -1] } }); + let P = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, w, -1] } }); if (D.push(P), s != null) { - let U = Kg(s.shape, C); - U != null && (s = de({ inputs: { x: s }, backend: o, attrs: { shape: U } }), D.push(s)); + let U = Yg(s.shape, C); + U != null && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: U } }), D.push(s)); } if (n != null) { - let U = Kg(n.shape, C); - U != null && (n = de({ inputs: { x: n }, backend: o, attrs: { shape: U } }), D.push(n)); + let U = Yg(n.shape, C); + U != null && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: U } }), D.push(n)); } - let W = Gu({ a: C ? R : P, b: C ? P : R, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), V = de({ inputs: { x: W }, backend: o, attrs: { shape: t6.outShape } }); - D.push(W); + let V = Xu({ a: C ? R : P, b: C ? P : R, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = me({ inputs: { x: V }, backend: o, attrs: { shape: t10.outShape } }); + D.push(V); for (let U of D) o.disposeData(U.dataId); - return V; + return z; } -function jg({ x: r, filter: e, convInfo: t6, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = n != null, u = s != null, c = t6.dataFormat === "channelsLast", l = c && t6.filterHeight === t6.inHeight && t6.filterWidth === t6.inWidth && t6.padInfo.type === "VALID", m = O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG"); - if (!m && (l || t6.filterHeight === 1 && t6.filterWidth === 1 && t6.dilationHeight === 1 && t6.dilationWidth === 1 && t6.strideHeight === 1 && t6.strideWidth === 1 && (t6.padInfo.type === "SAME" || t6.padInfo.type === "VALID"))) - return Lre({ x: r, filter: e, convInfo: t6, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); - let d = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t6.batchSize * Math.ceil(t6.outHeight * t6.outWidth / 32) * Math.ceil(t6.outChannels / 32); +function Qg({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = n != null, u = s != null, c = t10.dataFormat === "channelsLast", l = c && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG"); + if (!m && (l || t10.filterHeight === 1 && t10.filterWidth === 1 && t10.dilationHeight === 1 && t10.dilationWidth === 1 && t10.strideHeight === 1 && t10.strideWidth === 1 && (t10.padInfo.type === "SAME" || t10.padInfo.type === "VALID"))) + return moe({ x: r, filter: e, convInfo: t10, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + let d = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t10.batchSize * Math.ceil(t10.outHeight * t10.outWidth / 32) * Math.ceil(t10.outChannels / 32); if (O().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f) - return Bre({ x: r, filter: e, convInfo: t6, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); - let g, x = [t6.padInfo.top, t6.padInfo.left], b = [{ type: "int32", data: [t6.filterHeight, t6.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t6.strideHeight, t6.strideWidth] }, { type: "int32", data: [t6.dilationHeight, t6.dilationWidth] }]; + return doe({ x: r, filter: e, convInfo: t10, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); + let g, x = [t10.padInfo.top, t10.padInfo.left], b = [{ type: "int32", data: [t10.filterHeight, t10.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t10.strideHeight, t10.strideWidth] }, { type: "int32", data: [t10.dilationHeight, t10.dilationWidth] }]; if (m) - g = new Hg(t6, p, i, u); + g = new jg(t10, p, i, u); else { - let _ = c ? t6.outHeight * t6.outWidth : t6.outChannels, $ = c ? t6.outChannels : t6.outHeight * t6.outWidth, A = t6.filterHeight * t6.filterWidth * t6.inChannels; - b.push({ type: "int32", data: [_] }, { type: "int32", data: [$] }, { type: "int32", data: [A] }); + let _ = c ? t10.outHeight * t10.outWidth : t10.outChannels, E = c ? t10.outChannels : t10.outHeight * t10.outWidth, A = t10.filterHeight * t10.filterWidth * t10.inChannels; + b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [A] }); let R = o.adapterInfo.isIntel(); - g = new Gg(t6, _, $, A, p, i, u, R); + g = new qg(t10, _, E, A, p, i, u, R); } let C = [], w = [r, e]; - p && (!c && n.shape.length === 1 && (n = de({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.push(n)), w.push(n)), u && (!c && s.shape.length === 1 && (s = de({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), C.push(s)), w.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,"); + p && (!c && n.shape.length === 1 && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.push(n)), w.push(n)), u && (!c && s.shape.length === 1 && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), C.push(s)), w.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,"); let k = o.runWebGPUProgram(g, w, r.dtype, b); for (let _ of C) o.disposeData(_.dataId); return k; } -function Vre(r) { - let { inputs: e, attrs: t6, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t6, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); - return jg({ x: n, filter: s, convInfo: m, backend: o }); +function foe(r) { + let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t10, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); + return Qg({ x: n, filter: s, convInfo: m, backend: o }); } -var QM = { kernelName: Go, backendName: "webgpu", kernelFunc: Vre }; -function zre(r = 4) { +var oL = { kernelName: Uo, backendName: "webgpu", kernelFunc: foe }; +var Zg = class { + constructor(e) { + this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`; + } + getUserCode() { + let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1; + return ` + ${Q("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let batch = coords[0]; + let d1 = coords[${o}]; + + let dyCorner = vec2(coords[${e}], coords[${t10}]) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { + let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); + let wRPerm = uniforms.filterDims.x - 1 - wR; + if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR = i32(dyR); + + for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { + let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); + let wCPerm = uniforms.filterDims.y - 1 - wC; + if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC = i32(dyC); + + for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { + if (${this.isChannelsLast}) { + let xValue = getDy(batch, idyR, idyC, d2); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } else { + let xValue = getDy(batch, d2, idyR, idyC); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } + + } + } + } + setOutputAtIndex(index, dotProd); + } + } + `; + } +}; +var Jg = class { + constructor(e) { + this.variableNames = ["x", "dy"], this.uniforms = "pad : vec2, stride : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`; + } + getUserCode() { + return ` + ${Q("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let wR = coords[0]; + let wC = coords[1]; + let d1 = coords[2]; + let d2 = coords[3]; + + // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var b = 0; b < uniforms.batchSize; b = b + 1) { + for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) { + let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0]; + if (xR < 0 || xR >= uniforms.inHeight) { + continue; + } + + for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) { + let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1]; + + if (xC < 0 || xC >= uniforms.inWidth) { + continue; + } + + if (${this.isChannelsLast}) { + let dyValue = getDy(b, yR, yC, d2); + let xValue = getX(b, xR, xC, d1); + dotProd = dotProd + xValue * dyValue; + } else { + let dyValue = getDy(b, d2, yR, yC); + let xValue = getX(b, d1, xR, xC); + dotProd = dotProd + xValue * dyValue; + } + } + } + } + setOutputAtIndex(index, dotProd); + } + } + `; + } +}; +function hoe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Jg(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }]; + return t10.runWebGPUProgram(d, [n, s], n.dtype, f); +} +var nL = { kernelName: oi, backendName: "webgpu", kernelFunc: hoe }; +function goe(r = 4) { let e = (s) => { switch (s) { case 1: @@ -28816,102 +29007,44 @@ function zre(r = 4) { } }`; } -var Xg = class { +var ex = class { constructor(e) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = Ml(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Ll(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.variableTypes = ["vec4", "f32"]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = Bl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Vl(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.variableTypes = ["vec4", "f32"]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; } getUserCode() { - let e = this.isVec4 ? Wu(this.elementsPerThread, this.workgroupSize) : Uu(this.elementsPerThread, this.workgroupSize); + let e = this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize) : ju(this.elementsPerThread, this.workgroupSize); return ` - ${zre(this.isVec4 ? 4 : 1)} + ${goe(this.isVec4 ? 4 : 1)} ${e} `; } }; -var Yg = class { - constructor(e) { - this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`; - } - getUserCode() { - let e = this.isChannelsLast ? 1 : 2, t6 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1; - return ` - ${se("index")} { - if(index < uniforms.size) { - let coords = getCoordsFromIndex(index); - let batch = coords[0]; - let d1 = coords[${o}]; - - let dyCorner = vec2(coords[${e}], coords[${t6}]) - uniforms.pads; - let dyRCorner = dyCorner.x; - let dyCCorner = dyCorner.y; - - // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). - // ? = to be determined. : = across all values in that axis. - var dotProd = 0.0; - for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { - let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); - let wRPerm = uniforms.filterDims.x - 1 - wR; - if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || - wRPerm < 0) { - continue; - } - let idyR = i32(dyR); - - for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { - let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); - let wCPerm = uniforms.filterDims.y - 1 - wC; - if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || - fract(dyC) > 0.0 || wCPerm < 0) { - continue; - } - let idyC = i32(dyC); - - for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { - if (${this.isChannelsLast}) { - let xValue = getDy(batch, idyR, idyC, d2); - let wValue = getW(wRPerm, wCPerm, d1, d2); - dotProd = dotProd + xValue * wValue; - } else { - let xValue = getDy(batch, d2, idyR, idyC); - let wValue = getW(wRPerm, wCPerm, d1, d2); - dotProd = dotProd + xValue * wValue; - } - - } - } - } - setOutputAtIndex(index, dotProd); - } - } - `; - } -}; -function Wre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; +function xoe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; if (O().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.filterHeight <= 2 && m.filterWidth <= 2 && m.outChannels <= 16 && m.inChannels === 1) - f = new Yg(m); + f = new Zg(m); else { - f = new Xg(m); + f = new ex(m); let h = m.inHeight * m.inWidth, g = m.inChannels, x = m.filterHeight * m.filterWidth * m.outChannels; d.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [x] }); } - return t6.runWebGPUProgram(f, [n, s], "float32", d); + return t10.runWebGPUProgram(f, [n, s], "float32", d); } -var ZM = { kernelName: Ho, backendName: "webgpu", kernelFunc: Wre }; -var Ure = Se({ opType: Q.COS }); -var JM = { kernelName: qo, backendName: "webgpu", kernelFunc: Ure }; -var Gre = Se({ opType: Q.COSH }); -var eL = { kernelName: Ko, backendName: "webgpu", kernelFunc: Gre }; -var Qg = class { - constructor(e, t6, o, n) { +var sL = { kernelName: Go, backendName: "webgpu", kernelFunc: xoe }; +var yoe = xe({ opType: X.COS }); +var aL = { kernelName: Ho, backendName: "webgpu", kernelFunc: yoe }; +var boe = xe({ opType: X.COSH }); +var iL = { kernelName: Ko, backendName: "webgpu", kernelFunc: boe }; +var tx = class { + constructor(e, t10, o, n) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true; - let [s] = t6; - this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`; + let [s] = t10; + this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`; } getUserCode() { - let [e, t6] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t6} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t6} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t6}`]; + let [e, t10] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t10} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t10} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t10}`]; return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); @@ -28938,7 +29071,7 @@ var Qg = class { return; } let in_x = ${p}; - if( in_x < 0.0 || in_x > ${t6} ) { + if( in_x < 0.0 || in_x > ${t10} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } @@ -28969,33 +29102,33 @@ var Qg = class { `; } }; -var Hre = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Qg(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; - return t6.runWebGPUProgram(c, [n, s, a], "float32", l); +var Coe = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new tx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; + return t10.runWebGPUProgram(c, [n, s, a], "float32", l); }; -var tL = { kernelName: Yo, backendName: "webgpu", kernelFunc: Hre }; -var Ku; +var uL = { kernelName: Xo, backendName: "webgpu", kernelFunc: Coe }; +var Zu; (function(r) { r.Prod = "*", r.Sum = "+"; -})(Ku || (Ku = {})); -var Gl = class { - constructor(e, t6, o, n) { - this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t6, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`; +})(Zu || (Zu = {})); +var Kl = class { + constructor(e, t10, o, n) { + this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`; } getUserCode() { - let e = this.outputShape.length, t6 = this.op === Ku.Prod ? "1.0" : "0.0", o = this.exclusive ? t6 : `getX(${rL(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; + let e = this.outputShape.length, t10 = this.op === Zu.Prod ? "1.0" : "0.0", o = this.exclusive ? t10 : `getX(${pL(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; return this.exclusive ? (s = this.reverse ? `end != ${n - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (s = this.reverse ? `end + pow2 < ${n}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${oL(e, "coords", this.op)}; + let end = ${cL(e, "coords", this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${oL(e, "coords", this.op)} = idx; - val ${this.op}= getX(${rL(e, "coords", this.op)}); + ${cL(e, "coords", this.op)} = idx; + val ${this.op}= getX(${pL(e, "coords", this.op)}); } setOutputAtIndex(index, val); } @@ -29003,7 +29136,7 @@ var Gl = class { `; } }; -function rL(r, e, t6) { +function pL(r, e, t10) { if (r === 1) return `${e}`; if (r === 2) @@ -29012,9 +29145,9 @@ function rL(r, e, t6) { return `${e}.x, ${e}.y, ${e}.z`; if (r === 4) return `${e}.x, ${e}.y, ${e}.z, ${e}.w`; - throw Error(`Cumulative ${t6} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`); } -function oL(r, e, t6) { +function cL(r, e, t10) { if (r === 1) return `${e}`; if (r === 2) @@ -29023,51 +29156,51 @@ function oL(r, e, t6) { return `${e}.z`; if (r === 4) return `${e}.w`; - throw Error(`Cumulative ${t6} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`); } -function Zg(r, e, t6, o, n, s) { +function rx(r, e, t10, o, n, s) { let a = e.shape.length, i = S.getAxesPermutation([o], a), p = e; - i != null && (p = Nr({ inputs: { x: e }, backend: t6, attrs: { perm: i } })); + i != null && (p = Nr({ inputs: { x: e }, backend: t10, attrs: { perm: i } })); let u = S.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = Ft({ inputs: { x: p }, backend: t6 }); + let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t10 }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { - let d = new Gl(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; - l = t6.runWebGPUProgram(d, [l], l.dtype, h), t6.disposeData(f.dataId); + let d = new Kl(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; + l = t10.runWebGPUProgram(d, [l], l.dtype, h), t10.disposeData(f.dataId); } if (n) { - let m = new Gl(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; - l = t6.runWebGPUProgram(m, [l], l.dtype, f), t6.disposeData(d.dataId); + let m = new Kl(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; + l = t10.runWebGPUProgram(m, [l], l.dtype, f), t10.disposeData(d.dataId); } if (i != null) { - let m = S.getUndoAxesPermutation(i), d = Nr({ inputs: { x: l }, backend: t6, attrs: { perm: m } }); - return t6.disposeData(l.dataId), t6.disposeData(p.dataId), d; + let m = S.getUndoAxesPermutation(i), d = Nr({ inputs: { x: l }, backend: t10, attrs: { perm: m } }); + return t10.disposeData(l.dataId), t10.disposeData(p.dataId), d; } return l; } -function qre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return Zg(Ku.Prod, n, t6, s, a, i); +function Soe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return rx(Zu.Prod, n, t10, s, a, i); } -var nL = { kernelName: jo, backendName: "webgpu", kernelFunc: qre }; -function Kre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; - return Zg(Ku.Sum, n, t6, s, a, i); +var lL = { kernelName: qo, backendName: "webgpu", kernelFunc: Soe }; +function woe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o; + return rx(Zu.Sum, n, t10, s, a, i); } -var sL = { kernelName: Xo, backendName: "webgpu", kernelFunc: Kre }; -function jre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = dr({ backend: t6, attrs: { shape: d, value: 0, dtype: l } }), h = new kc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; - return t6.runWebGPUProgram(h, x, l, g, f); +var mL = { kernelName: jo, backendName: "webgpu", kernelFunc: woe }; +function Ioe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = fr({ backend: t10, attrs: { shape: d, value: 0, dtype: l } }), h = new Tc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; + return t10.runWebGPUProgram(h, x, l, g, f); } -var aL = { kernelName: ti, backendName: "webgpu", kernelFunc: jre }; -var Jg = class { - constructor(e, t6) { - this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t6}`, this.dataFormat = t6; +var dL = { kernelName: ni, backendName: "webgpu", kernelFunc: Ioe }; +var ox = class { + constructor(e, t10) { + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t10}`, this.dataFormat = t10; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -29104,19 +29237,19 @@ var Jg = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function Xre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new Jg(f, a); - return t6.runWebGPUProgram(g, [n], n.dtype, h); +function voe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new ox(f, a); + return t10.runWebGPUProgram(g, [n], n.dtype, h); } -var iL = { kernelName: Qo, backendName: "webgpu", kernelFunc: Xre }; -var ex = class { - constructor(e, t6, o, n = false, s = null, a = false) { - this.variableNames = ["x", "W"], this.uniforms = "pad : vec2, inDims : vec2,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t6, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`; +var fL = { kernelName: Yo, backendName: "webgpu", kernelFunc: voe }; +var nx = class { + constructor(e, t10, o, n = false, s = null, a = false) { + this.variableNames = ["x", "W"], this.uniforms = "pad : vec2, inDims : vec2,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t10, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`; } getUserCode() { - let e = this.filterWidth * this.filterHeight, t6 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1; + let e = this.filterWidth * this.filterHeight, t10 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1; return ` - ${ur(this.activation, this.hasPreluActivation, false, 4)} + ${pr(this.activation, this.hasPreluActivation, false, 4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -29129,7 +29262,7 @@ var ex = class { return value; } - ${se()} { + ${Q()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pad; @@ -29154,7 +29287,7 @@ var ex = class { // Load one tile of W into local memory. var wIndex = i32(localIndex); - ${e < t6 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t6})`} + ${e < t10 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t10})`} { let wRow = wIndex / ${this.filterWidth}; @@ -29172,7 +29305,7 @@ var ex = class { value = fma(xVal, wVal, value); } } - ${Hr(this.addBias, this.activation)} + ${Gr(this.addBias, this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } @@ -29180,14 +29313,14 @@ var ex = class { `; } }; -var Tc = class { - constructor(e, t6 = false, o = null, n = false) { - this.variableNames = ["x", "W"], this.uniforms = "pad : vec2, inDims : vec2,", this.workgroupSize = [4, 4, 4], this.workPerThread = 4, this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t6, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`; +var $c = class { + constructor(e, t10 = false, o = null, n = false) { + this.variableNames = ["x", "W"], this.uniforms = "pad : vec2, inDims : vec2,", this.workgroupSize = [4, 4, 4], this.workPerThread = 4, this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`; } getUserCode() { - let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth; + let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, t10 = this.convInfo.strideHeight, o = this.convInfo.strideWidth; return ` - ${ur(this.activation, this.hasPreluActivation, true, 4)} + ${pr(this.activation, this.hasPreluActivation, true, 4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -29196,14 +29329,12 @@ var Tc = class { return value; } - const strideHeight = ${this.convInfo.strideHeight}; - const strideWidth = ${this.convInfo.strideWidth}; - ${se()} { + ${Q()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; - let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad; + let xRCCorner = vec2(r, c) * vec2(${t10}, ${o}) - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; @@ -29223,7 +29354,7 @@ var Tc = class { for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { - dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]); + dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]); } } } @@ -29233,7 +29364,7 @@ var Tc = class { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${Hr(this.addBias, this.activation)} + ${Gr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } @@ -29241,17 +29372,17 @@ var Tc = class { `; } }; -var _c = class { - constructor(e, t6 = false, o = null, n = false) { +var Ec = class { + constructor(e, t10 = false, o = null, n = false) { this.variableNames = ["x", "W"], this.uniforms = `pad : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, stride : vec2, dilation : vec2,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t6 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t6, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`; + filterWidth : i32, stride : vec2, dilation : vec2,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`; } getUserCode() { let e = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);"; return ` - ${ur(this.activation, this.hasPreluActivation, false, 4)} + ${pr(this.activation, this.hasPreluActivation, false, 4)} - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -29310,65 +29441,131 @@ var _c = class { } } } - ${Hr(this.addBias, this.activation)} + ${Gr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `; } }; -function Yre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = u; +function koe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = u; m == null && (m = [1, 1]); let d = S.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g; - return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new ex(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? g = new Tc(d) : (g = new _c(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t6.runWebGPUProgram(g, [n, s], n.dtype, f); + return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new nx(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? g = new $c(d) : (g = new Ec(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t10.runWebGPUProgram(g, [n, s], n.dtype, f); } -var uL = { kernelName: Zo, backendName: "webgpu", kernelFunc: Yre }; -var dI = ot({ opType: ye.MUL, cpuKernelImpl: aM, supportsComplex: true }); -var pL = { kernelName: kn, backendName: "webgpu", kernelFunc: dI }; -function Hl(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return qr(n, s, a, "sum", t6); +var hL = { kernelName: Qo, backendName: "webgpu", kernelFunc: koe }; +var sx = class { + constructor(e) { + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag"; + } + getUserCode() { + return ` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); + setOutputAtIndex(index, value); + } + } + `; + } +}; +function Noe(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = me({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new sx(s), p = t10.runWebGPUProgram(i, [a], a.dtype), u = me({ inputs: { x: p }, backend: t10, attrs: { shape: n } }); + return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u; } -var cL = { kernelName: Hn, backendName: "webgpu", kernelFunc: Hl }; -function Qre(r) { - let { inputs: e, backend: t6, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); +var gL = { kernelName: si, backendName: "webgpu", kernelFunc: Noe }; +var ax = class { + constructor(e) { + this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d"; + } + getUserCode() { + return ` + ${Q("index")} { + if (index < uniforms.size) { + let neg_infinity = -3.4e38; + let coords = getOutputCoords(); + let batch = coords.x; + let d1 = coords.w; + let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad; + let hBeg = outTopLeftCorner.x; + let wBeg = outTopLeftCorner.y; + + var curVal = neg_infinity; + for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) { + let hIn = hBeg + h * uniforms.dilation[0]; + + if (hIn >= 0 && hIn < uniforms.xShape[1]) { + for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) { + let wIn = wBeg + w * uniforms.dilation[1]; + + if (wIn >= 0 && wIn < uniforms.xShape[2]) { + let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1); + if (val > curVal) { + curVal = val; + } + } + } + } + } + + setOutputAtIndex(index, curVal); + } + } + `; + } +}; +function Toe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new ax(u); + return t10.runWebGPUProgram(m, [n, s], n.dtype, l); +} +var xL = { kernelName: ai, backendName: "webgpu", kernelFunc: Toe }; +var II = Ze({ opType: ge.MUL, cpuKernelImpl: lM, supportsComplex: true }); +var yL = { kernelName: kn, backendName: "webgpu", kernelFunc: II }; +function ql(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Kr(n, s, a, "sum", t10); +} +var bL = { kernelName: Kn, backendName: "webgpu", kernelFunc: ql }; +function _oe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length); S.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C; - S.isIdentityPermutation(x) ? C = s[g] : (C = Nr({ inputs: { x: s[g] }, backend: t6, attrs: { perm: x } }), f.push(C)); + S.isIdentityPermutation(x) ? C = s[g] : (C = Nr({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C)); let w = C.shape.slice(); for (let k = 0; k < b.length; ++k) w.splice(b[k], 0, 1); - y.arraysEqual(C.shape, w) || (C = de({ inputs: { x: C }, backend: t6, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = dI({ inputs: { a: C, b: m }, backend: t6 }), f.push(m)); + y.arraysEqual(C.shape, w) || (C = me({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = II({ inputs: { a: C, b: m }, backend: t10 }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = Hl({ inputs: { x: m }, backend: t6, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = ql({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) - h !== m && t6.disposeData(h.dataId); + h !== m && t10.disposeData(h.dataId); return m; } -var lL = { kernelName: ri, backendName: "webgpu", kernelFunc: Qre }; -var Zre = Se({ opType: Q.ELU }); -var mL = { kernelName: en, backendName: "webgpu", kernelFunc: Zre }; -var Jre = ot({ opType: ye.EQUAL, dtype: "bool", cpuKernelImpl: q3 }); -var dL = { kernelName: tn, backendName: "webgpu", kernelFunc: Jre }; -var eoe = Se({ opType: Q.ERF }); -var fL = { kernelName: ma, backendName: "webgpu", kernelFunc: eoe }; -var fI = Se({ opType: Q.EXP, cpuKernelImpl: K3, dtype: "float32" }); -var hL = { kernelName: rn, backendName: "webgpu", kernelFunc: fI }; -function tx(r) { - let { inputs: e, attrs: t6, backend: o } = r, { dim: n } = t6, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; - return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), de({ inputs: { x: s }, backend: o, attrs: { shape: i } }); +var CL = { kernelName: ii, backendName: "webgpu", kernelFunc: _oe }; +var $oe = xe({ opType: X.ELU }); +var SL = { kernelName: Jo, backendName: "webgpu", kernelFunc: $oe }; +var Eoe = Ze({ opType: ge.EQUAL, dtype: "bool", cpuKernelImpl: Q3 }); +var wL = { kernelName: en, backendName: "webgpu", kernelFunc: Eoe }; +var Aoe = xe({ opType: X.ERF }); +var IL = { kernelName: da, backendName: "webgpu", kernelFunc: Aoe }; +var vI = xe({ opType: X.EXP, cpuKernelImpl: Z3, dtype: "float32" }); +var vL = { kernelName: tn, backendName: "webgpu", kernelFunc: vI }; +function ix(r) { + let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; + return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), me({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var gL = { kernelName: bs, backendName: "webgpu", kernelFunc: tx }; -var toe = Se({ opType: Q.EXPM1, cpuKernelImpl: j3 }); -var xL = { kernelName: da, backendName: "webgpu", kernelFunc: toe }; -var ql = class { - constructor(e, t6) { - this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`; +var kL = { kernelName: Ss, backendName: "webgpu", kernelFunc: ix }; +var Roe = xe({ opType: X.EXPM1, cpuKernelImpl: J3 }); +var NL = { kernelName: fa, backendName: "webgpu", kernelFunc: Roe }; +var jl = class { + constructor(e, t10) { + this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`; } getUserCode() { return ` @@ -29398,7 +29595,7 @@ var ql = class { return result; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); @@ -29407,30 +29604,30 @@ var ql = class { `; } }; -function rx(r, e, t6) { - let o = t6.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = de({ inputs: { x: r }, backend: t6, attrs: { shape: [a, s] } }); +function ux(r, e, t10) { + let o = t10.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = me({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }); i.push(p); - let u = p.shape, c = new ql("real", u), l = new ql("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t6.runWebGPUProgram(c, m, "float32", h); + let u = p.shape, c = new jl("real", u), l = new jl("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t10.runWebGPUProgram(c, m, "float32", h); i.push(g); - let x = t6.runWebGPUProgram(l, m, "float32", h); + let x = t10.runWebGPUProgram(l, m, "float32", h); i.push(x); - let b = po({ inputs: { real: g, imag: x }, backend: t6 }); + let b = po({ inputs: { real: g, imag: x }, backend: t10 }); i.push(b); - let C = de({ inputs: { x: b }, backend: t6, attrs: { shape: r.shape } }); - return i.forEach((w) => t6.disposeData(w.dataId)), C; + let C = me({ inputs: { x: b }, backend: t10, attrs: { shape: r.shape } }); + return i.forEach((w) => t10.disposeData(w.dataId)), C; } -function roe(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e; - return rx(o, false, t6); +function Doe(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e; + return ux(o, false, t10); } -var yL = { kernelName: oi, backendName: "webgpu", kernelFunc: roe }; -var ox = class { +var TL = { kernelName: ui, backendName: "webgpu", kernelFunc: Doe }; +var px = class { constructor(e) { - this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; + this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -29441,23 +29638,23 @@ var ox = class { `; } }; -var bL = { kernelName: on, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { image: t6 } = r, o = e, n = new ox(t6.shape); - return o.runWebGPUProgram(n, [t6], t6.dtype); +var _L = { kernelName: rn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { image: t10 } = r, o = e, n = new px(t10.shape); + return o.runWebGPUProgram(n, [t10], t10.dtype); } }; -var ooe = Se({ opType: Q.FLOOR, cpuKernelImpl: X3 }); -var CL = { kernelName: nn, backendName: "webgpu", kernelFunc: ooe }; -var noe = ot({ opType: ye.INT_DIV, dtype: "int32" }); -var SL = { kernelName: sn, backendName: "webgpu", kernelFunc: noe }; -var nx = class { - constructor(e, t6, o = false) { - this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [t6, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; +var Foe = xe({ opType: X.FLOOR, cpuKernelImpl: eM }); +var $L = { kernelName: on, backendName: "webgpu", kernelFunc: Foe }; +var Ooe = Ze({ opType: ge.INT_DIV, dtype: "int32" }); +var EL = { kernelName: nn, backendName: "webgpu", kernelFunc: Ooe }; +var cx = class { + constructor(e, t10, o = false) { + this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [t10, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; } getUserCode() { let e = this.importVideo ? "textureLoad(src, vec2(coords.yx));" : "textureLoad(src, vec2(coords.yx), 0)"; return ` @binding(1) @group(0) var src: ${this.importVideo ? "texture_external" : "texture_2d"}; - ${se("index")} { + ${Q("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -29470,12 +29667,12 @@ var nx = class { `; } }; -var wL = { kernelName: Zi, backendName: "webgpu", kernelFunc: soe }; -var Ec; -var hI = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -var sx = /* @__PURE__ */ new Map(); -function soe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o; +var AL = { kernelName: Zi, backendName: "webgpu", kernelFunc: Poe }; +var Ac; +var kI = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +var lx = /* @__PURE__ */ new Map(); +function Poe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o; if (n == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = false, f = a || i; @@ -29483,23 +29680,23 @@ function soe(r) { let b; if (d) { let D = n; - if (!sx.has(D) || sx.get(D).expired) { + if (!lx.has(D) || lx.get(D).expired) { let P = { source: D }; - sx.set(D, t6.device.importExternalTexture(P)); + lx.set(D, t10.device.importExternalTexture(P)); } - b = { width: c, height: l, format: null, usage: null, texture: sx.get(D) }; + b = { width: c, height: l, format: null, usage: null, texture: lx.get(D) }; } else { if (f) { let L = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (Ec == null || L !== hI) && (hI = L, Ec = document.createElement("canvas").getContext("2d", { willReadFrequently: hI })), Ec.canvas.width = c, Ec.canvas.height = l, Ec.drawImage(n, 0, 0, c, l), n = Ec.canvas; + (Ac == null || L !== kI) && (kI = L, Ac = document.createElement("canvas").getContext("2d", { willReadFrequently: kI })), Ac.canvas.width = c, Ac.canvas.height = l, Ac.drawImage(n, 0, 0, c, l), n = Ac.canvas; } - let D = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, P = "rgba8unorm", M = t6.textureManager.acquireTexture(m[1], m[0], P, D); - t6.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: P, usage: D, texture: M }; + let D = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, P = "rgba8unorm", M = t10.textureManager.acquireTexture(m[1], m[0], P, D); + t10.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: P, usage: D, texture: M }; } - let C = y.sizeFromShape(m), w = y.computeStrides(m), k = new nx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...w] }], $ = t6.makeTensorInfo([l, c], "int32"), A = t6.tensorMap.get($.dataId); + let C = y.sizeFromShape(m), w = y.computeStrides(m), k = new cx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...w] }], E = t10.makeTensorInfo([l, c], "int32"), A = t10.tensorMap.get(E.dataId); A.resourceInfo = b; - let R = t6.runWebGPUProgram(k, [$], "int32", _); - return t6.disposeData($.dataId), R; + let R = t10.runWebGPUProgram(k, [E], "int32", _); + return t10.disposeData(E.dataId), R; } let h = n.data, g = h; if (s != null && s !== 4) { @@ -29508,26 +29705,26 @@ function soe(r) { for (let w = 0; w < b; w++) w % 4 < s && (g[C++] = h[w]); } - let x = t6.makeTensorInfo(m, "int32", new Int32Array(g)); - return t6.uploadToGPU(x.dataId), x; + let x = t10.makeTensorInfo(m, "int32", new Int32Array(g)); + return t10.uploadToGPU(x.dataId), x; } -var ax = class { - constructor(e, t6, o, n, s) { - this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t6), S.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm"; +var mx = class { + constructor(e, t10, o, n, s) { + this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm"; } getUserCode() { let e = "0.0"; this.offsetShape != null && (e = "getOffsetByOutputIndex(index)"); - let t6 = "1.0"; - return this.scaleShape != null && (t6 = "getScaleByOutputIndex(index)"), ` - ${se("index")} { + let t10 = "1.0"; + return this.scaleShape != null && (t10 = "getScaleByOutputIndex(index)"), ` + ${Q("index")} { if (index < uniforms.size) { let xValue = getXByOutputIndex(index); let meanValue = getMeanByOutputIndex(index); let varianValue = getVarianceByOutputIndex(index); let offsetValue = ${e}; - let scaleValue = ${t6}; + let scaleValue = ${t10}; let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon)); setOutputAtIndex(index,dot(vec3(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } @@ -29535,36 +29732,36 @@ var ax = class { `; } }; -var IL = { kernelName: an, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t6, c = [o, a, i], l = null; +var RL = { kernelName: sn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t10, c = [o, a, i], l = null; s != null && (l = s.shape, c.push(s)); let m = null; n != null && (m = n.shape, c.push(n)); - let d = new ax(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; + let d = new mx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; return u.runWebGPUProgram(d, c, o.dtype, f); } }; -function aoe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); - return jg({ x: n, filter: s, convInfo: g, backend: t6, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); +function Moe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); + return Qg({ x: n, filter: s, convInfo: g, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); } -var vL = { kernelName: ho, backendName: "webgpu", kernelFunc: aoe }; -function ioe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; +var DL = { kernelName: go, backendName: "webgpu", kernelFunc: Moe }; +function Loe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; f == null && (f = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`); let h = S.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null; x && g.push(a), b && g.push(i); let C = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], w; - return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? w = new Tc(h, x, m, b) : (w = new _c(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), w.uniforms += " alpha : f32,"), t6.runWebGPUProgram(w, g, "float32", C); + return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? w = new $c(h, x, m, b) : (w = new Ec(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), w.uniforms += " alpha : f32,"), t10.runWebGPUProgram(w, g, "float32", C); } -var kL = { kernelName: go, backendName: "webgpu", kernelFunc: ioe }; -var ix = class { - constructor(e, t6) { - this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Rt(e)},`; +var FL = { kernelName: xo, backendName: "webgpu", kernelFunc: Loe }; +var dx = class { + constructor(e, t10) { + this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Rt(e)},`; } getUserCode() { let e; return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -29580,24 +29777,24 @@ var ix = class { `; } }; -function uoe(r) { - let { inputs: e, backend: t6 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = de({ inputs: { x: n }, backend: t6, attrs: { shape: [u, a] } }), d = de({ inputs: { x: o }, backend: t6, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); - if (t6.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let b = t6.readSync(n.dataId), C = t6.bufferSync(o), w = Y3(b, C, o.dtype, u, a, c, l, o.shape, i); - return t6.makeTensorInfo(p, o.dtype, w.values); +function Boe(r) { + let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = me({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); + if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { + let b = t10.readSync(n.dataId), C = t10.bufferSync(o), w = tM(b, C, o.dtype, u, a, c, l, o.shape, i); + return t10.makeTensorInfo(p, o.dtype, w.values); } - let f = new ix(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t6.runWebGPUProgram(f, [d, m], d.dtype, h), x = de({ inputs: { x: g }, backend: t6, attrs: { shape: p } }); - return t6.disposeData(m.dataId), t6.disposeData(d.dataId), t6.disposeData(g.dataId), x; + let f = new dx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t10.runWebGPUProgram(f, [d, m], d.dtype, h), x = me({ inputs: { x: g }, backend: t10, attrs: { shape: p } }); + return t10.disposeData(m.dataId), t10.disposeData(d.dataId), t10.disposeData(g.dataId), x; } -var NL = { kernelName: un, backendName: "webgpu", kernelFunc: uoe }; -var ux = class { - constructor(e, t6) { - this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t6, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather"; +var OL = { kernelName: an, backendName: "webgpu", kernelFunc: Boe }; +var fx = class { + constructor(e, t10) { + this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather"; } getUserCode() { - let e = poe(this.aShape); + let e = Voe(this.aShape); return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -29608,84 +29805,194 @@ var ux = class { `; } }; -function poe(r) { - let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t6 = []; +function Voe(r) { + let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t10 = []; for (let o = 0; o < r.length; o++) - o === 2 ? t6.push("indexZ") : t6.push(`${e[o]}`); - return t6.join(); + o === 2 ? t10.push("indexZ") : t10.push(`${e[o]}`); + return t10.join(); } -function gI(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = de({ inputs: { x: n }, backend: t6, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = de({ inputs: { x: s }, backend: t6, attrs: { shape: [u.batchSize, c / u.batchSize] } }); +function NI(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = me({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } }); l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; - if (t6.shouldExecuteOnCPU([n, s])) { - let C = t6.tensorMap.get(d.dataId).values, w = le(d.shape, d.dtype, C), _ = t6.tensorMap.get(m.dataId).values, $ = le(m.shape, m.dtype, _), A = Q3($, w, f); - return l.forEach((R) => t6.disposeData(R.dataId)), t6.makeTensorInfo(u.outputShape, A.dtype, A.values); + if (t10.shouldExecuteOnCPU([n, s])) { + let C = t10.tensorMap.get(d.dataId).values, w = le(d.shape, d.dtype, C), _ = t10.tensorMap.get(m.dataId).values, E = le(m.shape, m.dtype, _), A = rM(E, w, f); + return l.forEach((R) => t10.disposeData(R.dataId)), t10.makeTensorInfo(u.outputShape, A.dtype, A.values); } - let h = new ux(m.shape, f), g = t6.runWebGPUProgram(h, [m, d], m.dtype); + let h = new fx(m.shape, f), g = t10.runWebGPUProgram(h, [m, d], m.dtype); l.push(g); - let x = de({ inputs: { x: g }, backend: t6, attrs: { shape: u.outputShape } }); - return l.forEach((b) => t6.disposeData(b.dataId)), x; + let x = me({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } }); + return l.forEach((b) => t10.disposeData(b.dataId)), x; } -var TL = { kernelName: Ss, backendName: "webgpu", kernelFunc: gI }; -var coe = ot({ opType: ye.GREATER, cpuKernelImpl: J3, dtype: "bool" }); -var _L = { kernelName: pn, backendName: "webgpu", kernelFunc: coe }; -var loe = ot({ opType: ye.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: Z3 }); -var EL = { kernelName: cn, backendName: "webgpu", kernelFunc: loe }; -function moe(r) { - let { inputs: e, backend: t6 } = r, { input: o } = e; - return rx(o, true, t6); +var PL = { kernelName: Is, backendName: "webgpu", kernelFunc: NI }; +var zoe = Ze({ opType: ge.GREATER, cpuKernelImpl: nM, dtype: "bool" }); +var ML = { kernelName: un, backendName: "webgpu", kernelFunc: zoe }; +var Woe = Ze({ opType: ge.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: oM }); +var LL = { kernelName: pn, backendName: "webgpu", kernelFunc: Woe }; +function Uoe(r) { + let { inputs: e, backend: t10 } = r, { input: o } = e; + return ux(o, true, t10); } -var $L = { kernelName: ni, backendName: "webgpu", kernelFunc: moe }; -var doe = Se({ opType: Q.IS_FINITE, dtype: "bool" }); -var AL = { kernelName: fa, backendName: "webgpu", kernelFunc: doe }; -var foe = Se({ opType: Q.IS_INF, dtype: "bool" }); -var RL = { kernelName: ha, backendName: "webgpu", kernelFunc: foe }; -var hoe = Se({ opType: Q.IS_NAN, dtype: "bool" }); -var FL = { kernelName: ln, backendName: "webgpu", kernelFunc: hoe }; -function goe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Ro(n.shape, Q.LEAKYRELU); - return i.uniforms = "alpha : f32,", t6.runWebGPUProgram(i, [n], "float32", a); +var BL = { kernelName: pi, backendName: "webgpu", kernelFunc: Uoe }; +var Goe = xe({ opType: X.IS_FINITE, dtype: "bool" }); +var VL = { kernelName: ha, backendName: "webgpu", kernelFunc: Goe }; +var Hoe = xe({ opType: X.IS_INF, dtype: "bool" }); +var zL = { kernelName: ga, backendName: "webgpu", kernelFunc: Hoe }; +var Koe = xe({ opType: X.IS_NAN, dtype: "bool" }); +var WL = { kernelName: cn, backendName: "webgpu", kernelFunc: Koe }; +function qoe(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Hr(n.shape, X.LEAKYRELU, "alpha : f32,"); + return t10.runWebGPUProgram(i, [n], "float32", a); } -var DL = { kernelName: mn, backendName: "webgpu", kernelFunc: goe }; -var xoe = ot({ opType: ye.LESS, dtype: "bool", cpuKernelImpl: tM }); -var OL = { kernelName: dn, backendName: "webgpu", kernelFunc: xoe }; -var yoe = ot({ opType: ye.LESS_EQUAL, dtype: "bool", cpuKernelImpl: eM }); -var PL = { kernelName: fn, backendName: "webgpu", kernelFunc: yoe }; -var boe = Se({ opType: Q.LOG, cpuKernelImpl: rM }); -var ML = { kernelName: hn, backendName: "webgpu", kernelFunc: boe }; -var Coe = Se({ opType: Q.LOG1P }); -var LL = { kernelName: ga, backendName: "webgpu", kernelFunc: Coe }; -var Soe = ot({ opType: ye.LOGICAL_AND, dtype: "bool" }); -var BL = { kernelName: gn, backendName: "webgpu", kernelFunc: Soe }; -var woe = Se({ opType: Q.LOGICAL_NOT }); -var VL = { kernelName: xn, backendName: "webgpu", kernelFunc: woe }; -var Ioe = ot({ opType: ye.MAX, cpuKernelImpl: nM }); -var zL = { kernelName: bn, backendName: "webgpu", kernelFunc: Ioe }; -function voe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p); - return Bg(n, c, "max", t6); +var UL = { kernelName: ln, backendName: "webgpu", kernelFunc: qoe }; +var joe = Ze({ opType: ge.LESS, dtype: "bool", cpuKernelImpl: aM }); +var GL = { kernelName: mn, backendName: "webgpu", kernelFunc: joe }; +var Xoe = Ze({ opType: ge.LESS_EQUAL, dtype: "bool", cpuKernelImpl: sM }); +var HL = { kernelName: dn, backendName: "webgpu", kernelFunc: Xoe }; +var hx = class { + constructor(e) { + this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace"; + } + getUserCode() { + return ` + ${Q("index")} { + if (index < uniforms.size) { + setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); + } + } + `; + } +}; +function Yoe(r) { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = (n - o) / (s - 1), i = new hx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }]; + return e.runWebGPUProgram(i, [], "float32", p); } -var WL = { kernelName: Cn, backendName: "webgpu", kernelFunc: voe }; -function koe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return qr(n, s, a, "min", t6); +var KL = { kernelName: li, backendName: "webgpu", kernelFunc: Yoe }; +var Qoe = xe({ opType: X.LOG, cpuKernelImpl: iM }); +var qL = { kernelName: fn, backendName: "webgpu", kernelFunc: Qoe }; +var Zoe = xe({ opType: X.LOG1P }); +var jL = { kernelName: xa, backendName: "webgpu", kernelFunc: Zoe }; +var Joe = Ze({ opType: ge.LOGICAL_AND, dtype: "bool" }); +var XL = { kernelName: hn, backendName: "webgpu", kernelFunc: Joe }; +var ene = xe({ opType: X.LOGICAL_NOT }); +var YL = { kernelName: gn, backendName: "webgpu", kernelFunc: ene }; +var tne = Ze({ opType: ge.LOGICAL_OR }); +var QL = { kernelName: xn, backendName: "webgpu", kernelFunc: tne }; +var ZL = ` + var powValue = 0.0; + let basis = uniforms.bias + uniforms.alpha * sum; + if (uniforms.beta == 0.5) { + powValue = inverseSqrt(basis); + } else if (uniforms.beta == 1.0) { + powValue = 1.0 / basis; + } else { + powValue = exp(log(basis) * (-uniforms.beta)); + } +`; +var gx = class { + constructor(e) { + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn"; + } + getUserCode() { + return ` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let b = coords[0]; + let r = coords[1]; + let c = coords[2]; + let d = coords[3]; + + let x = getX(b, r, c, d); + var sum = 0.0; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let idx = d + i; + if (idx >= 0 && idx < uniforms.xShape[3]) { + let z = getX(b, r, c, idx); + sum = sum + z * z; + } + } + ${ZL} + + setOutputAtIndex(index, x * powValue); + } + } + `; + } +}; +var xx = class { + constructor(e, t10) { + this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(t10 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t10}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared"; + } + getUserCode() { + return ` + var lrnSub: array; + const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; + const maxAllowRadius = ${this.maxAllowRadius}; + + ${Q()} { + let localDepth = i32(localId.x); + let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; + let xDepth = workgroupDepth + localDepth - maxAllowRadius; + let b = i32(globalId.z) / uniforms.xShape[1]; + let r = i32(globalId.z) - b * uniforms.xShape[1]; + let c = i32(globalId.y); + let d = workgroupDepth + localDepth; + + var x = 0.0; + if (xDepth >= 0 && xDepth < uniforms.xShape[3]) { + x = getX(b, r, c, xDepth); + } + lrnSub[localDepth] = x; + workgroupBarrier(); + + if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) { + var sum = 0.0; + let index = localDepth + maxAllowRadius; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let z = lrnSub[index + i]; + sum = sum + z * z; + } + ${ZL} + + setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); + } + } `; + } +}; +function rne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u; + s > 16 ? u = new gx(n.shape) : u = new xx(n.shape, s); + let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }]; + return t10.runWebGPUProgram(u, [n], n.dtype, c); } -var UL = { kernelName: wn, backendName: "webgpu", kernelFunc: koe }; -var Noe = ot({ opType: ye.MIN, cpuKernelImpl: sM }); -var GL = { kernelName: In, backendName: "webgpu", kernelFunc: Noe }; -var px = class { - constructor(e, t6, o) { - this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t6.map((n, s) => { +var JL = { kernelName: mi, backendName: "webgpu", kernelFunc: rne }; +var one = Ze({ opType: ge.MAX, cpuKernelImpl: pM }); +var eB = { kernelName: bn, backendName: "webgpu", kernelFunc: one }; +function nne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p); + return zg(n, c, "max", t10); +} +var tB = { kernelName: Cn, backendName: "webgpu", kernelFunc: nne }; +function sne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Kr(n, s, a, "min", t10); +} +var rB = { kernelName: wn, backendName: "webgpu", kernelFunc: sne }; +var ane = Ze({ opType: ge.MIN, cpuKernelImpl: cM }); +var oB = { kernelName: In, backendName: "webgpu", kernelFunc: ane }; +var yx = class { + constructor(e, t10, o) { + this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t10.map((n, s) => { this.uniforms += ` pad${s} : vec2,`; }), this.offset = o === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${o}`; } getUserCode() { - let e = this.xShape.length, t6 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Rt(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; + let e = this.xShape.length, t10 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Rt(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { - let start = ${i}(${t6}); + let start = ${i}(${t10}); let end = ${i}(${o}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${e}; i = i + 1) { @@ -29702,41 +30009,41 @@ var px = class { `; } }; -var HL = { kernelName: vn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { x: o } = r, { paddings: n, mode: s } = e, a = t6, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new px(o.shape, n, s); +var nB = { kernelName: vn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { x: o } = r, { paddings: n, mode: s } = e, a = t10, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new yx(o.shape, n, s); return a.runWebGPUProgram(p, [o], o.dtype, i); } }; -var Toe = ot({ opType: ye.MOD }); -var qL = { kernelName: ya, backendName: "webgpu", kernelFunc: Toe }; -function _oe(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; - if (t6.shouldExecuteOnCPU([o])) { - let s = t6.tensorMap.get(o.dataId), [a, i] = iM(s.values, o.shape, o.dtype); - return t6.makeTensorInfo(i, o.dtype, a); +var ine = Ze({ opType: ge.MOD }); +var sB = { kernelName: ya, backendName: "webgpu", kernelFunc: ine }; +function une(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; + if (t10.shouldExecuteOnCPU([o])) { + let s = t10.tensorMap.get(o.dataId), [a, i] = mM(s.values, o.shape, o.dtype); + return t10.makeTensorInfo(i, o.dtype, a); } - let n = new Ro(o.shape, Q.NEG); - return t6.runWebGPUProgram(n, [o], o.dtype); + let n = new Hr(o.shape, X.NEG); + return t10.runWebGPUProgram(n, [o], o.dtype); } -var KL = { kernelName: ws, backendName: "webgpu", kernelFunc: _oe }; -function Eoe(r) { +var aB = { kernelName: vs, backendName: "webgpu", kernelFunc: une }; +function pne(r) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t6.readSync(n.dataId), c = t6.readSync(s.dataId), { selectedIndices: l } = Lt.nonMaxSuppressionV3Impl(u, c, a, i, p); - return t6.makeTensorInfo([l.length], "int32", new Int32Array(l)); + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = Vt.nonMaxSuppressionV3Impl(u, c, a, i, p); + return t10.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var jL = { kernelName: Tn, backendName: "webgpu", kernelFunc: Eoe }; -function $oe(r) { +var iB = { kernelName: Tn, backendName: "webgpu", kernelFunc: pne }; +function cne(r) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: e, backend: t6, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t6.readSync(n.dataId), l = t6.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Lt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); - return [t6.makeTensorInfo([g.length], "int32", new Int32Array(g)), t6.makeTensorInfo([x.length], "float32", new Float32Array(x))]; + let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Vt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); + return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var XL = { kernelName: _n, backendName: "webgpu", kernelFunc: $oe }; -var cx = class { - constructor(e, t6) { - this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t6], this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot"; +var uB = { kernelName: _n, backendName: "webgpu", kernelFunc: cne }; +var bx = class { + constructor(e, t10) { + this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, @@ -29746,58 +30053,58 @@ var cx = class { `; } }; -function Aoe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new cx(u, a), l = de({ inputs: { x: n }, backend: t6, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t6.runWebGPUProgram(c, [l], s, m); - t6.disposeData(l.dataId); - let f = [...n.shape, a], h = de({ inputs: { x: d }, backend: t6, attrs: { shape: f } }); - return t6.disposeData(d.dataId), h; +function lne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new bx(u, a), l = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t10.runWebGPUProgram(c, [l], s, m); + t10.disposeData(l.dataId); + let f = [...n.shape, a], h = me({ inputs: { x: d }, backend: t10, attrs: { shape: f } }); + return t10.disposeData(d.dataId), h; } -var YL = { kernelName: En, backendName: "webgpu", kernelFunc: Aoe }; -function Kl(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var pB = { kernelName: $n, backendName: "webgpu", kernelFunc: lne }; +function Xl(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "complex64") { - let n = qa({ inputs: { input: o }, backend: t6 }), s = Kl({ inputs: { x: n }, backend: t6 }), a = qu({ inputs: { input: o }, backend: t6 }), i = Kl({ inputs: { x: a }, backend: t6 }), p = po({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeData(n.dataId), t6.disposeData(s.dataId), t6.disposeData(a.dataId), t6.disposeData(i.dataId), p; + let n = Xa({ inputs: { input: o }, backend: t10 }), s = Xl({ inputs: { x: n }, backend: t10 }), a = Qu({ inputs: { input: o }, backend: t10 }), i = Xl({ inputs: { x: a }, backend: t10 }), p = po({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p; } else - return dr({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t6 }); + return fr({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 }); } -var QL = { kernelName: Fs, backendName: "webgpu", kernelFunc: Kl }; -function ZL(r) { - let { inputs: e, backend: t6 } = r, { x: o } = e; +var cB = { kernelName: Os, backendName: "webgpu", kernelFunc: Xl }; +function lB(r) { + let { inputs: e, backend: t10 } = r, { x: o } = e; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = qa({ inputs: { input: o }, backend: t6 }), s = ZL({ inputs: { x: n }, backend: t6 }), a = qu({ inputs: { input: o }, backend: t6 }), i = Kl({ inputs: { x: a }, backend: t6 }), p = po({ inputs: { real: s, imag: i }, backend: t6 }); - return t6.disposeData(n.dataId), t6.disposeData(s.dataId), t6.disposeData(a.dataId), t6.disposeData(i.dataId), p; + let n = Xa({ inputs: { input: o }, backend: t10 }), s = lB({ inputs: { x: n }, backend: t10 }), a = Qu({ inputs: { input: o }, backend: t10 }), i = Xl({ inputs: { x: a }, backend: t10 }), p = po({ inputs: { real: s, imag: i }, backend: t10 }); + return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p; } else - return dr({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t6 }); + return fr({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 }); } -var JL = { kernelName: Is, backendName: "webgpu", kernelFunc: ZL }; -function Roe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { axis: n } = o; +var mB = { kernelName: ks, backendName: "webgpu", kernelFunc: lB }; +function mne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o; if (e.length === 1) - return tx({ inputs: { input: e[0] }, backend: t6, attrs: { dim: n } }); + return ix({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } }); let s = e[0].shape, a = e[0].dtype; e.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = e.map((c) => { - let l = tx({ inputs: { input: c }, backend: t6, attrs: { dim: n } }); + let l = ix({ inputs: { input: c }, backend: t10, attrs: { dim: n } }); return i.push(l), l; - }), u = mI({ inputs: p, backend: t6, attrs: { axis: n } }); - return i.forEach((c) => t6.disposeData(c.dataId)), u; + }), u = wI({ inputs: p, backend: t10, attrs: { axis: n } }); + return i.forEach((c) => t10.disposeData(c.dataId)), u; } -var eB = { kernelName: vs, backendName: "webgpu", kernelFunc: Roe }; -var lx = class { - constructor(e, t6) { - this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), t6.map((o, n) => { +var dB = { kernelName: Ns, backendName: "webgpu", kernelFunc: mne }; +var Cx = class { + constructor(e, t10) { + this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), t10.map((o, n) => { this.uniforms += ` pad${n} : vec2,`; }), this.xShape = e, this.shaderKey = "pad"; } getUserCode() { - let e = this.xShape.length, t6 = Rt(e), o = this.xShape.map((l, m) => `uniforms.pad${m}[0]`).join(","), n = this.xShape.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), s = e > 1 ? `${t6}(${o})` : `${o}`, a = e > 1 ? `${t6}(${n})` : `${n}`, i = e > 1 ? "any(outC < start)" : "outC < start", p = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; + let e = this.xShape.length, t10 = Rt(e), o = this.xShape.map((l, m) => `uniforms.pad${m}[0]`).join(","), n = this.xShape.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), s = e > 1 ? `${t10}(${o})` : `${o}`, a = e > 1 ? `${t10}(${n})` : `${n}`, i = e > 1 ? "any(outC < start)" : "outC < start", p = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let start = ${s}; let end = ${a}; @@ -29814,52 +30121,52 @@ var lx = class { `; } }; -var xI = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; +var TI = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o; if (s.every((u) => y.arraysEqual(u, [0, 0]))) - return Ft({ inputs: { x: n }, backend: t6 }); + return Dt({ inputs: { x: n }, backend: t10 }); if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return dr({ backend: t6, attrs: { shape: u, value: a, dtype: n.dtype } }); + return fr({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } }); } let i = [{ type: "float32", data: [a] }]; s.map((u) => i.push({ type: "int32", data: [u[0], u[1]] })); - let p = new lx(n.shape, s); - return t6.runWebGPUProgram(p, [n], n.dtype, i); + let p = new Cx(n.shape, s); + return t10.runWebGPUProgram(p, [n], n.dtype, i); }; -var tB = { kernelName: $n, backendName: "webgpu", kernelFunc: xI }; -var Foe = ot({ opType: ye.POW }); -var rB = { kernelName: An, backendName: "webgpu", kernelFunc: Foe }; -function Doe(r) { - let { inputs: e, backend: t6 } = r, { x: o, alpha: n } = e, s = new Hu(ye.PRELU, o.shape, n.shape); - return t6.runWebGPUProgram(s, [o, n], "float32"); +var fB = { kernelName: En, backendName: "webgpu", kernelFunc: TI }; +var dne = Ze({ opType: ge.POW }); +var hB = { kernelName: An, backendName: "webgpu", kernelFunc: dne }; +function fne(r) { + let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = new Yu(ge.PRELU, o.shape, n.shape); + return t10.runWebGPUProgram(s, [o, n], "float32"); } -var oB = { kernelName: Rn, backendName: "webgpu", kernelFunc: Doe }; -function Ooe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; - return qr(n, s, a, "prod", t6); +var gB = { kernelName: Rn, backendName: "webgpu", kernelFunc: fne }; +function hne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o; + return Kr(n, s, a, "prod", t10); } -var nB = { kernelName: Fn, backendName: "webgpu", kernelFunc: Ooe }; -var Poe = (r) => { - let { backend: e, attrs: t6 } = r, { start: o, stop: n, step: s, dtype: a } = t6, i = cM(o, n, s, a); +var xB = { kernelName: Dn, backendName: "webgpu", kernelFunc: hne }; +var gne = (r) => { + let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = hM(o, n, s, a); return e.makeTensorInfo([i.length], a, i); }; -var sB = { kernelName: ks, backendName: "webgpu", kernelFunc: Poe }; -var yI = ot({ opType: ye.DIV }); -var aB = { kernelName: Jo, backendName: "webgpu", kernelFunc: yI }; -var Moe = Se({ opType: Q.RECIPROCAL }); -var iB = { kernelName: Dn, backendName: "webgpu", kernelFunc: Moe }; -var Loe = Se({ opType: Q.RELU }); -var uB = { kernelName: On, backendName: "webgpu", kernelFunc: Loe }; -var Boe = Se({ opType: Q.RELU6 }); -var pB = { kernelName: Ln, backendName: "webgpu", kernelFunc: Boe }; -var mx = class { - constructor(e, t6, o) { - this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t6, o, e[3]], this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear"; +var yB = { kernelName: Ts, backendName: "webgpu", kernelFunc: gne }; +var _I = Ze({ opType: ge.DIV }); +var bB = { kernelName: Zo, backendName: "webgpu", kernelFunc: _I }; +var xne = xe({ opType: X.RECIPROCAL }); +var CB = { kernelName: Fn, backendName: "webgpu", kernelFunc: xne }; +var yne = xe({ opType: X.RELU }); +var SB = { kernelName: On, backendName: "webgpu", kernelFunc: yne }; +var bne = xe({ opType: X.RELU6 }); +var wB = { kernelName: Ln, backendName: "webgpu", kernelFunc: bne }; +var Sx = class { + constructor(e, t10, o) { + this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -29904,19 +30211,19 @@ var mx = class { `; } }; -function Voe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new mx(n.shape, p, u); - return t6.runWebGPUProgram(f, [n], "float32", d); +function Cne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Sx(n.shape, p, u); + return t10.runWebGPUProgram(f, [n], "float32", d); } -var cB = { kernelName: Mn, backendName: "webgpu", kernelFunc: Voe }; -var dx = class { - constructor(e, t6, o, n) { - this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t6, o, e[3]], this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`; +var IB = { kernelName: Mn, backendName: "webgpu", kernelFunc: Cne }; +var wx = class { + constructor(e, t10, o, n) { + this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`; } getUserCode() { let e; return this.halfPixelCenters ? e = "max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : e = "vec2(rc) * effectiveInputOverOutputRatioRC", ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -29949,14 +30256,14 @@ var dx = class { `; } }; -function zoe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new dx(n.shape, p, u, a); - return t6.runWebGPUProgram(f, [n], n.dtype, d); +function Sne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new wx(n.shape, p, u, a); + return t10.runWebGPUProgram(f, [n], n.dtype, d); } -var lB = { kernelName: Pn, backendName: "webgpu", kernelFunc: zoe }; -var fx = class { +var vB = { kernelName: Pn, backendName: "webgpu", kernelFunc: Sne }; +var Ix = class { constructor(e) { - this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4,", this.shaderKey = "reverse"; + this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4,", this.shaderKey = "reverse"; } getUserCode() { return ` @@ -29981,7 +30288,7 @@ var fx = class { return reverseCoords; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -29992,10 +30299,10 @@ var fx = class { `; } }; -function Woe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length; +function wne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length; if (a === 0) - return Ft({ inputs: { x: n }, backend: t6 }); + return Dt({ inputs: { x: n }, backend: t10 }); let i = n.shape, p = [1, 1, 1, 1]; i.forEach((g, x) => { let b = x + 4 - a; @@ -30006,20 +30313,20 @@ function Woe(r) { let x = g + 4 - a; c[x] = 1; }); - let l = [{ type: "int32", data: c }], m = de({ inputs: { x: n }, backend: t6, attrs: { shape: p } }), d = new fx(p), f = t6.runWebGPUProgram(d, [m], m.dtype, l); - t6.disposeData(m.dataId); - let h = de({ inputs: { x: f }, backend: t6, attrs: { shape: i } }); - return t6.disposeData(f.dataId), h; + let l = [{ type: "int32", data: c }], m = me({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = new Ix(p), f = t10.runWebGPUProgram(d, [m], m.dtype, l); + t10.disposeData(m.dataId); + let h = me({ inputs: { x: f }, backend: t10, attrs: { shape: i } }); + return t10.disposeData(f.dataId), h; } -var mB = { kernelName: Bn, backendName: "webgpu", kernelFunc: Woe }; -var hx = class { - constructor(e, t6) { - this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32, - cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t6 == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3"); +var kB = { kernelName: Bn, backendName: "webgpu", kernelFunc: wne }; +var vx = class { + constructor(e, t10) { + this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32, + cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t10 == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3"); } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -30041,22 +30348,24 @@ var hx = class { `; } }; -var dB = { kernelName: es, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t6 }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t6, p = new hx(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; +var NB = { kernelName: ts, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => { + let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new vx(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l); } }; -var Uoe = Se({ opType: Q.RSQRT, cpuKernelImpl: lM }); -var fB = { kernelName: Vn, backendName: "webgpu", kernelFunc: Uoe }; -var Gi = class { - constructor(e, t6, o, n, s, a, i, p = true) { - this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = ue(e), this.dispatch = re(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t6 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`; +var Ine = xe({ opType: X.ROUND }); +var TB = { kernelName: Vn, backendName: "webgpu", kernelFunc: Ine }; +var vne = xe({ opType: X.RSQRT, cpuKernelImpl: gM }); +var _B = { kernelName: zn, backendName: "webgpu", kernelFunc: vne }; +var Hi = class { + constructor(e, t10, o, n, s, a, i, p = true) { + this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = ae(e), this.dispatch = Y(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t10 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`; let u = Rt(s.length); this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o; } getUserCode() { let e = ""; this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j"); - let t6 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = ""; + let t10 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = ""; this.dispatchLayout.x.length === 1 ? (n = "flattenedIndex", s = ` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; @@ -30073,58 +30382,37 @@ var Gi = class { return vec2(d0, d1); } `); - let i = `getUpdates(${Array.from({ length: this.updatesRank }, (c, l) => `coords[${l}]`).join(", ")})`, p = (c, l) => { - let m = `atomicAdd(${c}, bitcast(${l}))`; - this.type === "float32" && (m = ` - { - var oldBits = 0; - var newBits = bitcast(${l}); - loop { - let info = atomicCompareExchangeWeak(${c}, oldBits, newBits); - if (info.exchanged) { - break; - } - oldBits = info.old_value; - let oldValue = bitcast(oldBits); - let newValue = oldValue + (${l}); - newBits = bitcast(newValue); - } - } - `); - let d = `atomicStore(${c}, bitcast(${l}));`; - return this.sumDupeIndices ? m : d; - }; + let i = `getUpdates(${Array.from({ length: this.updatesRank }, (u, c) => `coords[${c}]`).join(", ")})`; return ` ${s} - - ${se("index")} { + ${Q("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { - let indexInside = i32(round(${t6})); + let indexInside = i32(round(${t10})); flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${wc(this.type, false)}(${i}); + ${vc(this.type, false)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${p("&result[flatIndex]", "updateValue")}; + ${this.sumDupeIndices ? Ic("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`; } }; -function Goe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u]; +function kne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) - return t6.makeTensorInfo(a, n.dtype); - let d = de({ inputs: { x: n }, backend: t6, attrs: { shape: [p, i] } }), f = de({ inputs: { x: s }, backend: t6, attrs: { shape: [p, u] } }), h = f.dtype, g = dr({ backend: t6, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new Gi(f.shape, i, d.shape.length, f.shape.length, c, m, h), w = t6.runWebGPUProgram(C, [f, d], h, b, g), k = de({ inputs: { x: w }, backend: t6, attrs: { shape: a } }); - return t6.disposeData(d.dataId), t6.disposeData(f.dataId), t6.disposeData(w.dataId), k; + return t10.makeTensorInfo(a, n.dtype); + let d = me({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = me({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = f.dtype, g = fr({ backend: t10, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new Hi(f.shape, i, d.shape.length, f.shape.length, c, m, h), w = t10.runWebGPUProgram(C, [f, d], h, b, g), k = me({ inputs: { x: w }, backend: t10, attrs: { shape: a } }); + return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(w.dataId), k; } -var hB = { kernelName: zn, backendName: "webgpu", kernelFunc: Goe }; -var gx = class { - constructor(e, t6) { - this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t6, this.shaderKey = `search_sorted_${t6}`; +var $B = { kernelName: Wn, backendName: "webgpu", kernelFunc: kne }; +var kx = class { + constructor(e, t10) { + this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t10, this.shaderKey = `search_sorted_${t10}`; } getUserCode() { return ` @@ -30142,7 +30430,7 @@ var gx = class { return right; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); @@ -30152,83 +30440,89 @@ var gx = class { `; } }; -function Hoe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new gx([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; - return t6.runWebGPUProgram(i, [n, s], "int32", p); +function Nne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new kx([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; + return t10.runWebGPUProgram(i, [n, s], "int32", p); } -var gB = { kernelName: ii, backendName: "webgpu", kernelFunc: Hoe }; -var xx = class { - constructor(e, t6, o) { - this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t6, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select"; +var EB = { kernelName: fi, backendName: "webgpu", kernelFunc: Nne }; +var Nx = class { + constructor(e, t10, o) { + this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select"; } getUserCode() { - let e, t6; + let e, t10; if (this.rank > 4) throw Error(`Where for rank ${this.rank} is not yet supported`); if (this.rank === 1) - t6 = "resRC", e = "resRC"; + t10 = "resRC", e = "resRC"; else { let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [], a = []; for (let i = 0; i < this.outputShape.length; i++) a.push(`${n[i]}`), i < this.cRank && s.push(`${n[i]}`); - e = s.join(), t6 = a.join(); + e = s.join(), t10 = a.join(); } return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let cVal = getC(${e}); if (cVal >= 1.0) { - setOutputAtIndex(index, getA(${t6})); + setOutputAtIndex(index, getA(${t10})); } else { - setOutputAtIndex(index, getB(${t6})); + setOutputAtIndex(index, getB(${t10})); } } } `; } }; -function qoe(r) { - let { inputs: e, backend: t6 } = r, { condition: o, t: n, e: s } = e, a = new xx(o.shape.length, n.shape, n.shape.length); - return t6.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype)); +function Tne(r) { + let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new Nx(o.shape.length, n.shape, n.shape.length); + return t10.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var xB = { kernelName: Ts, backendName: "webgpu", kernelFunc: qoe }; -var Koe = Se({ opType: Q.SIGMOID }); -var yB = { kernelName: Un, backendName: "webgpu", kernelFunc: Koe }; -var joe = Se({ opType: Q.SIN }); -var bB = { kernelName: Wn, backendName: "webgpu", kernelFunc: joe }; -var Xoe = Se({ opType: Q.SINH }); -var CB = { kernelName: Sa, backendName: "webgpu", kernelFunc: Xoe }; -var bI = ot({ opType: ye.SUB, cpuKernelImpl: xM, supportsComplex: true }); -var SB = { kernelName: Xn, backendName: "webgpu", kernelFunc: bI }; -function Yoe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Ul({ inputs: { x: n }, backend: t6, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = de({ inputs: { x: i }, backend: t6, attrs: { shape: p } }), c = bI({ inputs: { a: n, b: u }, backend: t6 }), l = fI({ inputs: { x: c }, backend: t6 }), m = Hl({ inputs: { x: l }, backend: t6, attrs: { axis: a, keepDims: false } }), d = de({ inputs: { x: m }, backend: t6, attrs: { shape: p } }), f = yI({ inputs: { a: l, b: d }, backend: t6 }); - return t6.disposeData(i.dataId), t6.disposeData(u.dataId), t6.disposeData(c.dataId), t6.disposeData(l.dataId), t6.disposeData(m.dataId), t6.disposeData(d.dataId), f; +var AB = { kernelName: $s, backendName: "webgpu", kernelFunc: Tne }; +var _ne = xe({ opType: X.SELU }); +var RB = { kernelName: Ca, backendName: "webgpu", kernelFunc: _ne }; +var $ne = xe({ opType: X.SIGMOID }); +var DB = { kernelName: Gn, backendName: "webgpu", kernelFunc: $ne }; +var Ene = xe({ opType: X.SIGN }); +var FB = { kernelName: wa, backendName: "webgpu", kernelFunc: Ene }; +var Ane = xe({ opType: X.SIN }); +var OB = { kernelName: Un, backendName: "webgpu", kernelFunc: Ane }; +var Rne = xe({ opType: X.SINH }); +var PB = { kernelName: Sa, backendName: "webgpu", kernelFunc: Rne }; +var $I = Ze({ opType: ge.SUB, cpuKernelImpl: wM, supportsComplex: true }); +var MB = { kernelName: Yn, backendName: "webgpu", kernelFunc: $I }; +function Dne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Hl({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = me({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = $I({ inputs: { a: n, b: u }, backend: t10 }), l = vI({ inputs: { x: c }, backend: t10 }), m = ql({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = me({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = _I({ inputs: { a: l, b: d }, backend: t10 }); + return t10.disposeData(i.dataId), t10.disposeData(u.dataId), t10.disposeData(c.dataId), t10.disposeData(l.dataId), t10.disposeData(m.dataId), t10.disposeData(d.dataId), f; } -var wB = { kernelName: qn, backendName: "webgpu", kernelFunc: Yoe }; -var Qoe = (r) => { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; +var LB = { kernelName: qn, backendName: "webgpu", kernelFunc: Dne }; +var Fne = xe({ opType: X.SOFTPLUS }); +var BB = { kernelName: Ia, backendName: "webgpu", kernelFunc: Fne }; +var One = (r) => { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o; y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet"); let i = s.reduce((x, b) => x * b), p = [[0, 0]]; p.push(...a); for (let x = 1 + s.length; x < n.shape.length; ++x) p.push([0, 0]); - let u = [], c = xI({ inputs: { x: n }, backend: t6, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = de({ inputs: { x: c }, backend: t6, attrs: { shape: l } }), h = Nr({ inputs: { x: f }, backend: t6, attrs: { perm: m } }), g = de({ inputs: { x: h }, backend: t6, attrs: { shape: d } }); - return u.push(c), u.push(f), u.push(h), u.forEach((x) => t6.disposeData(x.dataId)), g; + let u = [], c = TI({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = me({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = Nr({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = me({ inputs: { x: h }, backend: t10, attrs: { shape: d } }); + return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeData(x.dataId)), g; }; -var IB = { kernelName: Es, backendName: "webgpu", kernelFunc: Qoe }; -var yx = class { - constructor(e, t6) { +var VB = { kernelName: As, backendName: "webgpu", kernelFunc: One }; +var Tx = class { + constructor(e, t10) { this.variableNames = ["A"], this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(e.length); for (let n = 0; n < o.length; n++) - o[n] = e[n] * t6[n]; - this.outputShape = o, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile"; + o[n] = e[n] * t10[n]; + this.outputShape = o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile"; } getUserCode() { - let e = Zoe(this.rank, "uniforms."); + let e = Pne(this.rank, "uniforms."); return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); setOutputAtIndex(index, getA(${e})); @@ -30237,133 +30531,138 @@ var yx = class { `; } }; -function Zoe(r, e = "") { +function Pne(r, e = "") { if (r >= 5) throw Error(`Tile for rank ${r} is not yet supported`); if (r === 1) return `(resRC % ${e}aShape)`; - let t6 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; + let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; for (let n = 0; n < r; n++) - o.push(`(${t6[n]} % ${e}aShape[${n}])`); + o.push(`(${t10[n]} % ${e}aShape[${n}])`); return o.join(); } -function CI(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { reps: s } = o; - if (t6.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) { - let p = t6.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = yM(c, s); - return t6.makeTensorInfo(l.shape, l.dtype, l.values); +function EI(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o; + if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) { + let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = IM(c, s); + return t10.makeTensorInfo(l.shape, l.dtype, l.values); } - let a = new yx(n.shape, s); - return t6.runWebGPUProgram(a, [n], n.dtype); + let a = new Tx(n.shape, s); + return t10.runWebGPUProgram(a, [n], n.dtype); } -var vB = { kernelName: to, backendName: "webgpu", kernelFunc: CI }; -function Joe(r) { - let { inputs: e, backend: t6, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false; +var zB = { kernelName: to, backendName: "webgpu", kernelFunc: EI }; +function Mne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let A = t6.bufferSync(n), R = t6.bufferSync(s), D = y.decodeString(t6.readSync(a.dataId)[0]), P = mM(A, R, i, m, c, u, p, l, D, d); - return t6.makeTensorInfo(i, P.dtype, P.values); + let A = t10.bufferSync(n), R = t10.bufferSync(s), D = y.decodeString(t10.readSync(a.dataId)[0]), P = xM(A, R, i, m, c, u, p, l, D, d); + return t10.makeTensorInfo(i, P.dtype, P.values); } - let f = [m / c, c], h = de({ inputs: { x: n }, backend: t6, attrs: { shape: [u, p] } }), g = s.shape.length ? de({ inputs: { x: s }, backend: t6, attrs: { shape: [u, c] } }) : Ft({ inputs: { x: s }, backend: t6 }), x = g.dtype, b = t6.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = de({ inputs: { x: a }, backend: t6, attrs: { shape: Array(f.length).fill(1) } }), w = CI({ inputs: { x: C }, backend: t6, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; + let f = [m / c, c], h = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u, p] } }), g = s.shape.length ? me({ inputs: { x: s }, backend: t10, attrs: { shape: [u, c] } }) : Dt({ inputs: { x: s }, backend: t10 }), x = g.dtype, b = t10.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = me({ inputs: { x: a }, backend: t10, attrs: { shape: Array(f.length).fill(1) } }), w = EI({ inputs: { x: C }, backend: t10, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; switch (u) { case 0: break; case 1: { - let A = new Gi([u, c], p, h.shape.length, g.shape.length, l, f, x, d); - t6.runWebGPUProgram(A, [g, h], x, _, w); + let A = new Hi([u, c], p, h.shape.length, g.shape.length, l, f, x, d); + t10.runWebGPUProgram(A, [g, h], x, _, w); } break; default: { - let A = new Gi([u, c], p, h.shape.length, b.shape.length, l, f, x, d); - t6.runWebGPUProgram(A, [b, h], x, _, w); + let A = new Hi([u, c], p, h.shape.length, b.shape.length, l, f, x, d); + t10.runWebGPUProgram(A, [b, h], x, _, w); } { - let A = new Gi([u, c], p, h.shape.length, g.shape.length, l, f, x); - t6.runWebGPUProgram(A, [g, h], x, _, w); + let A = new Hi([u, c], p, h.shape.length, g.shape.length, l, f, x); + t10.runWebGPUProgram(A, [g, h], x, _, w); } } - let $ = de({ inputs: { x: w }, backend: t6, attrs: { shape: i } }); - return t6.disposeData(h.dataId), t6.disposeData(g.dataId), t6.disposeData(C.dataId), t6.disposeData(b.dataId), t6.disposeData(w.dataId), $; + let E = me({ inputs: { x: w }, backend: t10, attrs: { shape: i } }); + return t10.disposeData(h.dataId), t10.disposeData(g.dataId), t10.disposeData(C.dataId), t10.disposeData(b.dataId), t10.disposeData(w.dataId), E; } -var kB = { kernelName: li, backendName: "webgpu", kernelFunc: Joe }; -function ene(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var WB = { kernelName: yi, backendName: "webgpu", kernelFunc: Mne }; +function Lne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; - let f = ds({ inputs: { x: n }, backend: t6, attrs: { begin: c, size: d } }); + let f = fs({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } }); return c[i] += m, f; }); } -var NB = { kernelName: $s, backendName: "webgpu", kernelFunc: ene }; -var tne = Se({ opType: Q.SQRT }); -var TB = { kernelName: Gn, backendName: "webgpu", kernelFunc: tne }; -var _B = { kernelName: mi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { - let { x: t6 } = r, o = e, n = new Ro(t6.shape, Q.SQUARE); - return o.runWebGPUProgram(n, [t6], t6.dtype); +var UB = { kernelName: Rs, backendName: "webgpu", kernelFunc: Lne }; +var Bne = xe({ opType: X.SQRT }); +var GB = { kernelName: Hn, backendName: "webgpu", kernelFunc: Bne }; +var HB = { kernelName: bi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => { + let { x: t10 } = r, o = e, n = new Hr(t10.shape, X.SQUARE); + return o.runWebGPUProgram(n, [t10], t10.dtype); } }; -var rne = ot({ opType: ye.SQUARED_DIFFERENCE }); -var EB = { kernelName: Kn, backendName: "webgpu", kernelFunc: rne }; -var bx = class { +var Vne = Ze({ opType: ge.SQUARED_DIFFERENCE }); +var KB = { kernelName: jn, backendName: "webgpu", kernelFunc: Vne }; +function zne({ inputs: r, attrs: e, backend: t10 }) { + let { x: o } = r, n = new Hr(o.shape, X.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }]; + return t10.runWebGPUProgram(n, [o], o.dtype, s); +} +var qB = { kernelName: fo, backendName: "webgpu", kernelFunc: zne }; +var _x = class { constructor(e) { - this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); - let t6 = Rt(this.outputShape.length); - this.uniforms = `begin : ${t6}, strides : ${t6}, `, this.shaderKey = "stridedSlice"; + this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); + let t10 = Rt(this.outputShape.length); + this.uniforms = `begin : ${t10}, strides : ${t10}, `, this.shaderKey = "stridedSlice"; } getUserCode() { - let e = this.outputShape.length, t6 = ""; + let e = this.outputShape.length, t10 = ""; if (e === 1) - t6 = "coords * uniforms.strides + uniforms.begin"; + t10 = "coords * uniforms.strides + uniforms.begin"; else { let n = 0; - t6 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(","); + t10 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(","); } return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); - setOutputAtIndex(index, getX(${t6})); + setOutputAtIndex(index, getX(${t10})); } } `; } }; -function one(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function Wne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = de({ inputs: { x: n }, backend: t6, attrs: { shape: f } }); + k = me({ inputs: { x: n }, backend: t10, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ut.computeOutShape(b, C, w), $ = ds({ inputs: { x: n }, backend: t6, attrs: { begin: b, size: _ } }); - k = de({ inputs: { x: $ }, backend: t6, attrs: { shape: f } }), t6.disposeData($.dataId); - } else if (t6.shouldExecuteOnCPU([n])) { - let $ = t6.readSync(n.dataId), A = le(n.shape, n.dtype, $), R = hM(d, A, w, b); - k = t6.makeTensorInfo(f, n.dtype, R.values); + let _ = ut.computeOutShape(b, C, w), E = fs({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } }); + k = me({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeData(E.dataId); + } else if (t10.shouldExecuteOnCPU([n])) { + let E = t10.readSync(n.dataId), A = le(n.shape, n.dtype, E), R = CM(d, A, w, b); + k = t10.makeTensorInfo(f, n.dtype, R.values); } else { - let $ = new bx(d), A = [{ type: "int32", data: b }, { type: "int32", data: w }], R = t6.runWebGPUProgram($, [n], n.dtype, A); - k = de({ inputs: { x: R }, backend: t6, attrs: { shape: f } }), t6.disposeData(R.dataId); + let E = new _x(d), A = [{ type: "int32", data: b }, { type: "int32", data: w }], R = t10.runWebGPUProgram(E, [n], n.dtype, A); + k = me({ inputs: { x: R }, backend: t10, attrs: { shape: f } }), t10.disposeData(R.dataId); } return k; } -var $B = { kernelName: jn, backendName: "webgpu", kernelFunc: one }; -function nne(r) { - let { inputs: e, backend: t6, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t6.readSync(c.dataId), d = t6.readSync(l.dataId), [f, h] = gM(m, d, n, s, a, i, p, u); - return [t6.makeTensorInfo([f.length], "string", f), t6.makeTensorInfo(l.shape, "int32", h)]; +var jB = { kernelName: Xn, backendName: "webgpu", kernelFunc: Wne }; +function Une(r) { + let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = SM(m, d, n, s, a, i, p, u); + return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)]; } -var AB = { kernelName: As, backendName: "webgpu", kernelFunc: nne }; -var sne = Se({ opType: Q.TAN }); -var RB = { kernelName: Yn, backendName: "webgpu", kernelFunc: sne }; -var ane = Se({ opType: Q.TANH }); -var FB = { kernelName: Qn, backendName: "webgpu", kernelFunc: ane }; -var Cx = class { +var XB = { kernelName: Ds, backendName: "webgpu", kernelFunc: Une }; +var Gne = xe({ opType: X.TAN }); +var YB = { kernelName: Qn, backendName: "webgpu", kernelFunc: Gne }; +var Hne = xe({ opType: X.TANH }); +var QB = { kernelName: Zn, backendName: "webgpu", kernelFunc: Hne }; +var $x = class { constructor(e) { - this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32, + this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`, this.shaderKey = "swap"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -30432,13 +30731,13 @@ var Cx = class { `; } }; -var Sx = class { +var Ex = class { constructor(e) { - this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge"; + this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge"; } getUserCode() { return ` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -30499,54 +30798,54 @@ var Sx = class { `; } }; -function $c(r, e) { +function Rc(r, e) { e !== null && r.disposeData(e.dataId); } -function DB(r) { +function ZB(r) { let e = 1; for (; e < r; ) e *= 2; return e; } -function ine(r) { - let { inputs: e, backend: t6, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; - if (t6.shouldExecuteOnCPU([n])) { - let k = t6.readSync(n.dataId), [_, $] = bM(k, i, n.dtype, s, a); - return [t6.makeTensorInfo(_.shape, _.dtype, _.values), t6.makeTensorInfo($.shape, $.dtype, $.values)]; +function Kne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; + if (t10.shouldExecuteOnCPU([n])) { + let k = t10.readSync(n.dataId), [_, E] = vM(k, i, n.dtype, s, a); + return [t10.makeTensorInfo(_.shape, _.dtype, _.values), t10.makeTensorInfo(E.shape, E.dtype, E.values)]; } if (s === 0) - return i[i.length - 1] = 0, [t6.makeTensorInfo(i, n.dtype, []), t6.makeTensorInfo(i, "int32", [])]; + return i[i.length - 1] = 0, [t10.makeTensorInfo(i, n.dtype, []), t10.makeTensorInfo(i, "int32", [])]; if (p === 1) - return [n, dr({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t6 })]; - let c = y.sizeFromShape(i) / p, l = de({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t6 }), m = DB(s), d = DB(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, $) => { - let A = h(), R = new Cx($), P = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; - f = t6.runWebGPUProgram(R, A, "int32", P), $c(t6, M); + return [n, fr({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t10 })]; + let c = y.sizeFromShape(i) / p, l = me({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t10 }), m = ZB(s), d = ZB(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => { + let A = h(), R = new $x(E), P = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; + f = t10.runWebGPUProgram(R, A, "int32", P), Rc(t10, M); }; for (let k = 1; k < m; k *= 2) { let _ = k * 2; - for (let $ = k; $ >= 1; $ /= 2) - g(_, $, [c, d]); + for (let E = k; E >= 1; E /= 2) + g(_, E, [c, d]); } for (let k = d; k > m; k /= 2) { - let _ = h(), $ = new Sx([c, k / 2]), R = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], D = f; - f = t6.runWebGPUProgram($, _, "int32", R), $c(t6, D); + let _ = h(), E = new Ex([c, k / 2]), R = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], D = f; + f = t10.runWebGPUProgram(E, _, "int32", R), Rc(t10, D); let P = m / 2, M = P * 2; for (let L = P; L >= 1; L /= 2) g(M, L, f.shape); } let x = f; - f = ds({ inputs: { x: f }, backend: t6, attrs: { begin: 0, size: [c, s] } }), $c(t6, x); - let b = gI({ inputs: { x: l, indices: f }, backend: t6, attrs: { axis: 1, batchDims: 1 } }); - $c(t6, l); + f = fs({ inputs: { x: f }, backend: t10, attrs: { begin: 0, size: [c, s] } }), Rc(t10, x); + let b = NI({ inputs: { x: l, indices: f }, backend: t10, attrs: { axis: 1, batchDims: 1 } }); + Rc(t10, l); let C = i.slice(0, -1); - C.push(s), x = f, f = de({ inputs: { x: f }, attrs: { shape: C }, backend: t6 }), $c(t6, x); + C.push(s), x = f, f = me({ inputs: { x: f }, attrs: { shape: C }, backend: t10 }), Rc(t10, x); let w = b; - return b = de({ inputs: { x: b }, attrs: { shape: C }, backend: t6 }), $c(t6, w), [b, f]; + return b = me({ inputs: { x: b }, attrs: { shape: C }, backend: t10 }), Rc(t10, w), [b, f]; } -var OB = { kernelName: Zn, backendName: "webgpu", kernelFunc: ine }; -var wx = class { +var JB = { kernelName: Jn, backendName: "webgpu", kernelFunc: Kne }; +var Ax = class { constructor(e) { - this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ue(this.outputShape), this.dispatch = re(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform"; + this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform"; } getUserCode() { return ` @@ -30613,7 +30912,7 @@ var wx = class { return outputValue; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; @@ -30668,8 +30967,8 @@ var wx = class { `; } }; -function une(r) { - let { inputs: e, backend: t6, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new wx(g), b = a === "nearest" ? 1 : 2, C; +function qne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Ax(g), b = a === "nearest" ? 1 : 2, C; switch (i) { case "constant": C = 1; @@ -30688,11 +30987,11 @@ function une(r) { break; } let w = [{ type: "int32", data: [b] }, { type: "int32", data: [C] }, { type: "float32", data: [p] }]; - return t6.runWebGPUProgram(x, [n, s], "float32", w); + return t10.runWebGPUProgram(x, [n, s], "float32", w); } -var PB = { kernelName: Jn, backendName: "webgpu", kernelFunc: une }; -function pne(r) { - let { inputs: e, backend: t6, attrs: o } = r, { value: n } = e, { axis: s } = o; +var eV = { kernelName: es, backendName: "webgpu", kernelFunc: qne }; +function jne(r) { + let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -30702,29 +31001,29 @@ function pne(r) { let f = new Array(p); for (let h = 0; h < f.length; h++) { m[s] = h; - let g = ds({ inputs: { x: a }, backend: t6, attrs: { begin: m, size: d } }), x = de({ inputs: { x: g }, backend: t6, attrs: { shape: u } }); + let g = fs({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = me({ inputs: { x: g }, backend: t10, attrs: { shape: u } }); f[h] = x, l.push(g); } - return l.forEach((h) => t6.disposeData(h.dataId)), f; + return l.forEach((h) => t10.disposeData(h.dataId)), f; } -var MB = { kernelName: Rs, backendName: "webgpu", kernelFunc: pne }; -var cne = [B3, SM, wM, IM, vM, kM, TM, _M, EM, $M, AM, RM, FM, DM, OM, LM, BM, zM, WM, qM, KM, jM, z3, YM, QM, ZM, JM, eL, tL, nL, sL, aL, iL, uL, lL, mL, dL, fL, hL, gL, xL, yL, M3, bL, wL, CL, SL, IL, vL, kL, NL, TL, _L, EL, V3, $L, XM, AL, RL, FL, DL, OL, PL, LL, ML, BL, VL, PM, zL, WL, MM, UL, GL, HL, qL, pL, KL, jL, XL, UM, YL, JL, eB, tB, rB, oB, nB, sB, GM, aB, iB, uB, pB, L3, cB, lB, mB, dB, fB, hB, gB, xB, yB, bB, CB, VM, $B, AB, wB, IB, kB, NB, TB, _B, EB, SB, cL, RB, FB, vB, OB, PB, NM, MB, QL]; -for (let r of cne) - Ia(r); -var LB = "4.1.0"; -var lne = "4.1.0"; -var mne = "4.1.0"; -var dne = "4.1.0"; -var fne = "4.1.0"; -var hne = "0.0.1-alpha.16"; -var gne = { tfjs: LB, "tfjs-core": LB, "tfjs-converter": lne, "tfjs-backend-cpu": mne, "tfjs-backend-webgl": dne, "tfjs-backend-wasm": fne, "tfjs-backend-webgpu": hne }; +var tV = { kernelName: Fs, backendName: "webgpu", kernelFunc: jne }; +var Xne = [G3, NM, TM, _M, $M, EM, RM, DM, FM, OM, PM, MM, LM, BM, VM, UM, GM, HM, qM, jM, ZM, JM, eL, K3, rL, oL, nL, sL, aL, iL, uL, lL, mL, dL, fL, hL, gL, xL, CL, SL, wL, IL, vL, kL, NL, TL, W3, _L, AL, $L, EL, RL, DL, FL, OL, PL, ML, LL, H3, BL, tL, VL, zL, WL, UL, GL, HL, KL, jL, qL, XL, YL, QL, JL, zM, eB, tB, WM, rB, oB, nB, sB, yL, aB, iB, uB, XM, pB, mB, dB, fB, hB, gB, xB, yB, YM, bB, CB, SB, wB, U3, IB, vB, kB, NB, TB, _B, $B, EB, AB, RB, DB, FB, OB, PB, KM, qB, jB, XB, LB, BB, VB, WB, UB, GB, HB, KB, MB, bL, YB, QB, zB, JB, eV, AM, tV, cB]; +for (let r of Xne) + ka(r); +var rV = "4.2.0"; +var Yne = "4.2.0"; +var Qne = "4.2.0"; +var Zne = "4.2.0"; +var Jne = "4.2.0"; +var ese = "0.0.1-alpha.17"; +var tse = { tfjs: rV, "tfjs-core": rV, "tfjs-converter": Yne, "tfjs-backend-cpu": Qne, "tfjs-backend-webgl": Zne, "tfjs-backend-wasm": Jne, "tfjs-backend-webgpu": ese }; // src/util/util.ts function log(...msg) { const dt2 = new Date(); - const ts = `${dt2.getHours().toString().padStart(2, "0")}:${dt2.getMinutes().toString().padStart(2, "0")}:${dt2.getSeconds().toString().padStart(2, "0")}.${dt2.getMilliseconds().toString().padStart(3, "0")}`; + const ts2 = `${dt2.getHours().toString().padStart(2, "0")}:${dt2.getMinutes().toString().padStart(2, "0")}:${dt2.getSeconds().toString().padStart(2, "0")}.${dt2.getMilliseconds().toString().padStart(3, "0")}`; if (msg) - console.log(ts, "Human:", ...msg); + console.log(ts2, "Human:", ...msg); } function join(folder, file) { const separator = folder.endsWith("/") ? "" : "/"; @@ -31201,20 +31500,20 @@ function GLImageFilter() { }, saturation: (amount) => { const x = (amount || 0) * 2 / 3 + 1; - const y10 = (x - 1) * -0.5; + const y6 = (x - 1) * -0.5; filter.colorMatrix([ x, - y10, - y10, + y6, + y6, 0, 0, - y10, + y6, x, - y10, + y6, 0, 0, - y10, - y10, + y6, + y6, x, 0, 0, @@ -31617,24 +31916,24 @@ function GLImageFilter() { async function histogramEqualization(inputImage) { const squeeze = inputImage.shape.length === 4 ? Up(inputImage) : inputImage; const rgb2 = Oa(squeeze, 3, 2); - const min = [sl(rgb2[0]), sl(rgb2[1]), sl(rgb2[2])]; - const max = [Us(rgb2[0]), Us(rgb2[1]), Us(rgb2[2])]; + const min = [rl(rgb2[0]), rl(rgb2[1]), rl(rgb2[2])]; + const max = [Gs(rgb2[0]), Gs(rgb2[1]), Gs(rgb2[2])]; const absMax = await Promise.all(max.map((channel) => channel.data())); const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); const maxRange = maxValue > 1 ? 255 : 1; const factor = maxRange / maxValue; let final; if (factor > 1) { - const sub = [Ne(rgb2[0], min[0]), Ne(rgb2[1], min[1]), Ne(rgb2[2], min[2])]; - const range = [Ne(max[0], min[0]), Ne(max[1], min[1]), Ne(max[2], min[2])]; - const enh = [ae(sub[0], factor), ae(sub[1], factor), ae(sub[2], factor)]; + const sub = [ke(rgb2[0], min[0]), ke(rgb2[1], min[1]), ke(rgb2[2], min[2])]; + const range = [ke(max[0], min[0]), ke(max[1], min[1]), ke(max[2], min[2])]; + const enh = [ne(sub[0], factor), ne(sub[1], factor), ne(sub[2], factor)]; const stack = Sr([enh[0], enh[1], enh[2]], 2); - final = z(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]); - Dt([...sub, ...range, ...enh]); + final = W(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]); + Ot([...sub, ...range, ...enh]); } else { - final = Fa(squeeze, 0); + final = Ra(squeeze, 0); } - Dt([...rgb2, ...min, ...max, rgb2, squeeze, inputImage]); + Ot([...rgb2, ...min, ...max, rgb2, squeeze, inputImage]); return final; } @@ -31702,24 +32001,24 @@ async function process2(input, config3, getTensor = true) { throw new Error("input error: attempted to use tensor without a shape"); if (input.shape.length === 3) { if (input.shape[2] === 3) { - tensor2 = Fa(input, 0); + tensor2 = Ra(input, 0); } else if (input.shape[2] === 4) { - const rgb2 = g1(input, [0, 0, 0], [-1, -1, 3]); - tensor2 = Fa(rgb2, 0); - Dt(rgb2); + const rgb2 = Qk(input, [0, 0, 0], [-1, -1, 3]); + tensor2 = Ra(rgb2, 0); + Ot(rgb2); } } else if (input.shape.length === 4) { if (input.shape[3] === 3) { - tensor2 = Br(input); + tensor2 = Lr(input); } else if (input.shape[3] === 4) { - tensor2 = x1(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + tensor2 = Zk(input, [0, 0, 0, 0], [-1, -1, -1, 3]); } } if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); if (tensor2.dtype === "int32") { - const cast = Ke(tensor2, "float32"); - Dt(tensor2); + const cast = je(tensor2, "float32"); + Ot(tensor2); tensor2 = cast; } return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null }; @@ -31831,8 +32130,8 @@ async function process2(input, config3, getTensor = true) { let pixels; let depth = 3; if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && Q0) { - pixels = Q0 ? Q0.fromPixels(input) : null; + if (env.browser && gN) { + pixels = gN ? gN.fromPixels(input) : null; } else { depth = input.data.length / input.height / input.width; const arr = new Uint8Array(input.data.buffer); @@ -31841,12 +32140,12 @@ async function process2(input, config3, getTensor = true) { } else { if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (Q0 && env.browser) { + if (gN && env.browser) { if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = Q0.fromPixels(outCanvas); + pixels = gN.fromPixels(outCanvas); } else { tmpCanvas = copy(outCanvas); - pixels = Q0.fromPixels(tmpCanvas); + pixels = gN.fromPixels(tmpCanvas); } } else { const tempCanvas = copy(outCanvas); @@ -31858,20 +32157,20 @@ async function process2(input, config3, getTensor = true) { } } if (depth === 4) { - const rgb2 = g1(pixels, [0, 0, 0], [-1, -1, 3]); - Dt(pixels); + const rgb2 = Qk(pixels, [0, 0, 0], [-1, -1, 3]); + Ot(pixels); pixels = rgb2; } if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = Ke(pixels, "float32"); - const tensor = config3.filter.equalization ? await histogramEqualization(casted) : Fa(casted, 0); - Dt([pixels, casted]); + const casted = je(pixels, "float32"); + const tensor = config3.filter.equalization ? await histogramEqualization(casted) : Ra(casted, 0); + Ot([pixels, casted]); if (config3.filter.autoBrightness) { - const max = Us(tensor); + const max = Gs(tensor); const maxVal = await max.data(); config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - Dt(max); + Ot(max); } return { tensor, canvas: config3.filter.return ? outCanvas : null }; } @@ -31880,25 +32179,25 @@ async function skip(config3, input) { if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; if (!last.inputTensor) { - last.inputTensor = Br(input); + last.inputTensor = Lr(input); } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - Dt(last.inputTensor); - last.inputTensor = Br(input); + Ot(last.inputTensor); + last.inputTensor = Lr(input); } else { - const t6 = {}; - t6.diff = Ne(input, last.inputTensor); - t6.squared = ae(t6.diff, t6.diff); - t6.sum = et(t6.squared); - const diffSum = await t6.sum.data(); + const t10 = {}; + t10.diff = ke(input, last.inputTensor); + t10.squared = ne(t10.diff, t10.diff); + t10.sum = et(t10.squared); + const diffSum = await t10.sum.data(); const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - Dt([last.inputTensor, t6.diff, t6.squared, t6.sum]); - last.inputTensor = Br(input); + Ot([last.inputTensor, t10.diff, t10.squared, t10.sum]); + last.inputTensor = Lr(input); skipFrame = diffRelative <= (config3.cacheSensitivity || 0); } return skipFrame; } async function compare(config3, input1, input2) { - const t6 = {}; + const t10 = {}; if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); @@ -31909,14 +32208,14 @@ async function compare(config3, input1, input2) { log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); return 0; } - t6.input1 = Br(input1); - t6.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? uq.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Br(input2); - t6.diff = Ne(t6.input1, t6.input2); - t6.squared = ae(t6.diff, t6.diff); - t6.sum = et(t6.squared); - const diffSum = await t6.sum.data(); + t10.input1 = Lr(input1); + t10.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? eK.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Lr(input2); + t10.diff = ke(t10.input1, t10.input2); + t10.squared = ne(t10.diff, t10.diff); + t10.sum = et(t10.squared); + const diffSum = await t10.sum.data(); const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - Dt([t6.input1, t6.input2, t6.diff, t6.squared, t6.sum]); + Ot([t10.input1, t10.input2, t10.diff, t10.squared, t10.sum]); return diffRelative; } @@ -31968,7 +32267,7 @@ var Env = class { __privateAdd(this, _imageData, void 0); this.browser = typeof navigator !== "undefined"; this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: gne["tfjs-core"] }; + this.tfjs = { version: tse["tfjs-core"] }; this.offscreen = typeof OffscreenCanvas !== "undefined"; this.initial = true; this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; @@ -32009,11 +32308,11 @@ var Env = class { globalThis.ImageData = val; } async updateBackend() { - this.backends = Object.keys(cr().registryFactory); + this.backends = Object.keys(sr().registryFactory); try { this.tensorflow = { - version: Oie()["binding"] ? Oie()["binding"].TF_Version : void 0, - gpu: Oie()["binding"] ? Oie()["binding"].isUsingGpuDevice() : void 0 + version: Pie()["binding"] ? Pie()["binding"].TF_Version : void 0, + gpu: Pie()["binding"] ? Pie()["binding"].isUsingGpuDevice() : void 0 }; } catch (e) { } @@ -32044,7 +32343,7 @@ var Env = class { this.webgpu.supported = false; } try { - this.kernels = Am(Aie()).map((kernel) => kernel.kernelName.toLowerCase()); + this.kernels = Rm(Rie()).map((kernel) => kernel.kernelName.toLowerCase()); } catch (e) { } } @@ -32454,14 +32753,14 @@ async function loadModel(modelPath) { options.cacheSupported = typeof indexedDB !== "undefined"; let cachedModels = {}; try { - cachedModels = options.cacheSupported && options.cacheModels ? await Ea.listModels() : {}; + cachedModels = options.cacheSupported && options.cacheModels ? await La.listModels() : {}; } catch (e) { options.cacheSupported = false; } modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new ll(modelStats[shortModelName].url, tfLoadOptions); + let model23 = new fl(modelStats[shortModelName].url, tfLoadOptions); let loaded = false; try { model23.findIOHandler(); @@ -32476,7 +32775,7 @@ async function loadModel(modelPath) { if (artifacts) model23.loadSync(artifacts); else - model23 = await l6(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + model23 = await A6(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); modelStats[shortModelName].sizeLoadedWeights = ((_d2 = (_c2 = model23.artifacts) == null ? void 0 : _c2.weightData) == null ? void 0 : _d2.byteLength) || 0; if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); @@ -32497,7 +32796,7 @@ async function loadModel(modelPath) { } // package.json -var version = "3.0.1"; +var version = "3.0.2"; // src/tfjs/humangl.ts var config2 = { @@ -32527,7 +32826,7 @@ function register(instance) { var _a2; if (instance.config.backend !== "humangl") return; - if (config2.name in cr().registry && !((_a2 = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a2.getParameter(config2.gl.VERSION))) { + if (config2.name in sr().registry && !((_a2 = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a2.getParameter(config2.gl.VERSION))) { log("humangl error: backend invalid context"); instance.models.reset(); } @@ -32569,23 +32868,23 @@ function register(instance) { return; } try { - RS(2, config2.gl); + US(2, config2.gl); } catch (err) { log("humangl error: cannot set webgl context:", err); return; } try { - const ctx = new Fu(config2.gl); - Ci(config2.name, () => new Bi(ctx), config2.priority); + const ctx = new Lu(config2.gl); + Ti(config2.name, () => new Vi(ctx), config2.priority); } catch (err) { log("humangl error: cannot register webgl backend:", err); return; } try { - const kernels = Am("webgl"); + const kernels = Rm("webgl"); kernels.forEach((kernelConfig) => { const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - Ia(newKernelConfig); + ka(newKernelConfig); }); } catch (err) { log("humangl error: cannot update webgl backend registration:", err); @@ -32599,7 +32898,7 @@ function register(instance) { return; } extensions(); - const backend = Oie(); + const backend = Pie(); const current = typeof backend["gpgpu"] !== "undefined" ? backend["getGPGPUContext"]().gl : null; if (current) { if (instance.config.debug) @@ -32620,12 +32919,12 @@ var constants = { rgb: [0.2989, 0.587, 0.114] }; function init() { - constants.tf255 = be(255, "float32"); - constants.tf1 = be(1, "float32"); - constants.tf2 = be(2, "float32"); - constants.tf05 = be(0.5, "float32"); - constants.tf127 = be(127.5, "float32"); - constants.rgb = mr([0.2989, 0.587, 0.114], "float32"); + constants.tf255 = Ce(255, "float32"); + constants.tf1 = Ce(1, "float32"); + constants.tf2 = Ce(2, "float32"); + constants.tf05 = Ce(0.5, "float32"); + constants.tf127 = Ce(127.5, "float32"); + constants.rgb = dr([0.2989, 0.587, 0.114], "float32"); } // src/tfjs/backend.ts @@ -32647,36 +32946,36 @@ function registerCustomOps(config3) { if (!env.kernels.includes("mod")) { const kernelMod = { kernelName: "Mod", - backendName: Aie(), - kernelFunc: (op2) => Ee(() => Ne(op2.inputs.a, ae(Ge(op2.inputs.a, op2.inputs.b), op2.inputs.b))) + backendName: Rie(), + kernelFunc: (op) => Ee(() => ke(op.inputs.a, ne(He(op.inputs.a, op.inputs.b), op.inputs.b))) }; - Ia(kernelMod); + ka(kernelMod); env.kernels.push("mod"); newKernels.push("mod"); } if (!env.kernels.includes("floormod")) { const kernelFloorMod = { kernelName: "FloorMod", - backendName: Aie(), - kernelFunc: (op2) => Ee(() => xe(ae(Jm(op2.inputs.a, op2.inputs.b), op2.inputs.b), wk(op2.inputs.a, op2.inputs.b))) + backendName: Rie(), + kernelFunc: (op) => Ee(() => be(ne(Km(op.inputs.a, op.inputs.b), op.inputs.b), Jv(op.inputs.a, op.inputs.b))) }; - Ia(kernelFloorMod); + ka(kernelFloorMod); env.kernels.push("floormod"); newKernels.push("floormod"); } if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { const kernelRotateWithOffset = { kernelName: "RotateWithOffset", - backendName: Aie(), - kernelFunc: (op2) => Ee(() => { - const backend = Aie(); + backendName: Rie(), + kernelFunc: (op) => Ee(() => { + const backend = Rie(); Eie("cpu"); - const t6 = uq.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center); + const t10 = eK.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); Eie(backend); - return t6; + return t10; }) }; - Ia(kernelRotateWithOffset); + ka(kernelRotateWithOffset); env.kernels.push("rotatewithoffset"); newKernels.push("rotatewithoffset"); } @@ -32689,7 +32988,7 @@ async function check(instance, force = false) { instance.state = "backend"; if (((_a2 = instance.config.backend) == null ? void 0 : _a2.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Aie() !== instance.config.backend) { + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Rie() !== instance.config.backend) { const timeStamp = now(); if (instance.config.backend && instance.config.backend.length > 0) { if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { @@ -32723,10 +33022,10 @@ async function check(instance, force = false) { } } } - let available = Object.keys(cr().registryFactory); + let available = Object.keys(sr().registryFactory); if (instance.config.backend === "humangl" && !available.includes("humangl")) { register(instance); - available = Object.keys(cr().registryFactory); + available = Object.keys(sr().registryFactory); } if (instance.config.debug) log("available backends:", available); @@ -32743,17 +33042,17 @@ async function check(instance, force = false) { O().set("CANVAS2D_WILL_READ_FREQUENTLY", true); if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof vee !== "undefined") - vee(instance.config.wasmPath, instance.config.wasmPlatformFetch); + if (typeof Gee !== "undefined") + Gee(instance.config.wasmPath, instance.config.wasmPlatformFetch); else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; + let mt2 = false; let simd = false; try { - mt = await O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + mt2 = await O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); simd = await O().getAsync("WASM_HAS_SIMD_SUPPORT"); if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt2 ? "multithreaded" : "singlethreaded"}`); if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); } catch (e) { @@ -32762,7 +33061,7 @@ async function check(instance, force = false) { } try { await Eie(instance.config.backend); - await $ie(); + await Aie(); } catch (err) { log("error: cannot set backend:", instance.config.backend, err); return false; @@ -32770,7 +33069,7 @@ async function check(instance, force = false) { if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(O().flags)); } - if (Aie() === "humangl" || Aie() === "webgl") { + if (Rie() === "humangl" || Rie() === "webgl") { if (O().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) O().set("WEBGL_USE_SHAPES_UNIFORMS", true); if (O().flagRegistry.WEBGL_EXP_CONV) @@ -32780,7 +33079,7 @@ async function check(instance, force = false) { O().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); } } - if (Aie() === "webgpu") { + if (Rie() === "webgpu") { } if (instance.config.debug) { const newFlags = O().flags; @@ -32791,7 +33090,7 @@ async function check(instance, force = false) { updatedFlags[key] = newFlags[key]; } if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", Aie(), "flags:", updatedFlags); + log("backend:", Rie(), "flags:", updatedFlags); } if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { if (instance.config.debug) @@ -32800,10 +33099,10 @@ async function check(instance, force = false) { O().set(key, val); } } - wie(); + Iie(); init(); instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = Aie(); + instance.config.backend = Rie(); await env.updateBackend(); registerCustomOps(instance.config); env.initial = false; @@ -32822,9 +33121,9 @@ function fakeOps(kernelNames, config3) { return (_a2 = param == null ? void 0 : param.inputs) == null ? void 0 : _a2.info; } }; - Ia(kernelConfig); + ka(kernelConfig); } - env.kernels = Am(Aie()).map((kernel) => kernel.kernelName.toLowerCase()); + env.kernels = Rm(Rie()).map((kernel) => kernel.kernelName.toLowerCase()); } // src/draw/draw.ts @@ -32859,48 +33158,48 @@ var getCanvasContext = (input) => { }; var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z8, opt) => { - if (!opt.useDepth || typeof z8 === "undefined") +var colorDepth = (z, opt) => { + if (!opt.useDepth || typeof z === "undefined") return opt.color; - const rgb2 = Uint8ClampedArray.from([127 + 2 * z8, 127 - 2 * z8, 255]); + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; }; function labels(ctx, str, startX, startY, localOptions2) { const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); const x = Math.max(0, startX); for (let i = line.length - 1; i >= 0; i--) { - const y10 = i * localOptions2.lineHeight + startY; + const y6 = i * localOptions2.lineHeight + startY; if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y10 + 16); + ctx.fillText(line[i], x + 5, y6 + 16); } ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y10 + 15); + ctx.fillText(line[i], x + 4, y6 + 15); } } -function point(ctx, x, y10, z8, localOptions2) { - ctx.fillStyle = colorDepth(z8, localOptions2); +function point(ctx, x, y6, z, localOptions2) { + ctx.fillStyle = colorDepth(z, localOptions2); ctx.beginPath(); - ctx.arc(x, y10, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.arc(x, y6, localOptions2.pointSize, 0, 2 * Math.PI); ctx.fill(); } -function rect(ctx, x, y10, width, height, localOptions2) { +function rect(ctx, x, y6, width, height, localOptions2) { ctx.beginPath(); ctx.lineWidth = localOptions2.lineWidth; if (localOptions2.useCurves) { const cx2 = (x + x + width) / 2; - const cy = (y10 + y10 + height) / 2; + const cy = (y6 + y6 + height) / 2; ctx.ellipse(cx2, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); } else { - ctx.moveTo(x + localOptions2.roundRect, y10); - ctx.lineTo(x + width - localOptions2.roundRect, y10); - ctx.quadraticCurveTo(x + width, y10, x + width, y10 + localOptions2.roundRect); - ctx.lineTo(x + width, y10 + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y10 + height, x + width - localOptions2.roundRect, y10 + height); - ctx.lineTo(x + localOptions2.roundRect, y10 + height); - ctx.quadraticCurveTo(x, y10 + height, x, y10 + height - localOptions2.roundRect); - ctx.lineTo(x, y10 + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y10, x + localOptions2.roundRect, y10); + ctx.moveTo(x + localOptions2.roundRect, y6); + ctx.lineTo(x + width - localOptions2.roundRect, y6); + ctx.quadraticCurveTo(x + width, y6, x + width, y6 + localOptions2.roundRect); + ctx.lineTo(x + width, y6 + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y6 + height, x + width - localOptions2.roundRect, y6 + height); + ctx.lineTo(x + localOptions2.roundRect, y6 + height); + ctx.quadraticCurveTo(x, y6 + height, x, y6 + height - localOptions2.roundRect); + ctx.lineTo(x, y6 + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y6, x + localOptions2.roundRect, y6); ctx.closePath(); } ctx.stroke(); @@ -32944,22 +33243,22 @@ function curves(ctx, points, localOptions2) { function arrow(ctx, from, to2, radius = 5) { let angle; let x; - let y10; + let y6; ctx.beginPath(); ctx.moveTo(from[0], from[1]); ctx.lineTo(to2[0], to2[1]); angle = Math.atan2(to2[1] - from[1], to2[0] - from[0]); x = radius * Math.cos(angle) + to2[0]; - y10 = radius * Math.sin(angle) + to2[1]; - ctx.moveTo(x, y10); + y6 = radius * Math.sin(angle) + to2[1]; + ctx.moveTo(x, y6); angle += 1 / 3 * (2 * Math.PI); x = radius * Math.cos(angle) + to2[0]; - y10 = radius * Math.sin(angle) + to2[1]; - ctx.lineTo(x, y10); + y6 = radius * Math.sin(angle) + to2[1]; + ctx.lineTo(x, y6); angle += 1 / 3 * (2 * Math.PI); x = radius * Math.cos(angle) + to2[0]; - y10 = radius * Math.sin(angle) + to2[1]; - ctx.lineTo(x, y10); + y6 = radius * Math.sin(angle) + to2[1]; + ctx.lineTo(x, y6); ctx.closePath(); ctx.stroke(); ctx.fill(); @@ -36721,7 +37020,7 @@ var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ // src/draw/face.ts var localOptions; function drawLabels(f, ctx) { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2; if (!localOptions.drawLabels || ((_a2 = localOptions.faceLabels) == null ? void 0 : _a2.length) === 0) return; let l = localOptions.faceLabels.slice(); @@ -36747,7 +37046,7 @@ function drawLabels(f, ctx) { } if ((_c2 = (_b2 = f.rotation) == null ? void 0 : _b2.angle) == null ? void 0 : _c2.roll) l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); - if ((_e2 = (_d2 = f.rotation) == null ? void 0 : _d2.angle) == null ? void 0 : _e2.yaw) + if ((_e = (_d2 = f.rotation) == null ? void 0 : _d2.angle) == null ? void 0 : _e.yaw) l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); if ((_g2 = (_f2 = f.rotation) == null ? void 0 : _f2.angle) == null ? void 0 : _g2.pitch) l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); @@ -36971,8 +37270,8 @@ function hand(inCanvas2, result, drawOptions) { return; for (let i = 0; i < part.length; i++) { ctx.beginPath(); - const z8 = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z8, localOptions2); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions2); ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); ctx.lineTo(part[i][0], part[i][1]); ctx.stroke(); @@ -37028,8 +37327,8 @@ function gesture(inCanvas2, result, drawOptions) { ctx.font = localOptions2.font; ctx.fillStyle = localOptions2.color; let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); + for (let j10 = 0; j10 < result.length; j10++) { + const [where, what] = Object.entries(result[j10]); if (what.length > 1 && what[1].length > 0) { const who = where[1] > 0 ? `#${where[1]}` : ""; let l = localOptions2.gestureLabels.slice(); @@ -37220,16 +37519,16 @@ function createAnchors() { const stride = strides[layerId]; const featureMapHeight = Math.ceil(inputSize / stride); const featureMapWidth = Math.ceil(inputSize / stride); - for (let y10 = 0; y10 < featureMapHeight; ++y10) { + for (let y6 = 0; y6 < featureMapHeight; ++y6) { for (let x = 0; x < featureMapWidth; ++x) { for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y10 + 0.5) / featureMapHeight }); + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y6 + 0.5) / featureMapHeight }); } } } layerId = lastSameStrideLayer; } - anchorTensor = { x: mr(anchors3.map((a) => a.x)), y: mr(anchors3.map((a) => a.y)) }; + anchorTensor = { x: dr(anchors3.map((a) => a.x)), y: dr(anchors3.map((a) => a.y)) }; } async function loadDetector(config3) { if (env.initial) @@ -37251,14 +37550,14 @@ function decodeBoxes(boxesTensor, anchor) { let yCenter = Up(split[1]); let width = Up(split[2]); let height = Up(split[3]); - xCenter = xe(Ge(xCenter, inputSize), anchor.x); - yCenter = xe(Ge(yCenter, inputSize), anchor.y); - width = ae(Ge(width, inputSize), cropFactor[0]); - height = ae(Ge(height, inputSize), cropFactor[1]); - const xMin = Ne(xCenter, Ge(width, 2)); - const yMin = Ne(yCenter, Ge(height, 2)); - const xMax = xe(xMin, width); - const yMax = xe(yMin, height); + xCenter = be(He(xCenter, inputSize), anchor.x); + yCenter = be(He(yCenter, inputSize), anchor.y); + width = ne(He(width, inputSize), cropFactor[0]); + height = ne(He(height, inputSize), cropFactor[1]); + const xMin = ke(xCenter, He(width, 2)); + const yMin = ke(yCenter, He(height, 2)); + const xMax = be(xMin, width); + const yMax = be(yMin, height); const boxes = Sr([xMin, yMin, xMax, yMax], 1); return boxes; }); @@ -37266,13 +37565,13 @@ function decodeBoxes(boxesTensor, anchor) { async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { var _a2, _b2; const detectedBoxes = []; - const t6 = {}; - t6.boxes = decodeBoxes(boxesTensor, anchorTensor); - t6.scores = zs(logitsTensor); - t6.nms = await uq.nonMaxSuppressionAsync(t6.boxes, t6.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b2 = config3.body["detector"]) == null ? void 0 : _b2.iouThreshold) || 0.1); - const nms = await t6.nms.data(); - const scores = await t6.scores.data(); - const boxes = await t6.boxes.array(); + const t10 = {}; + t10.boxes = decodeBoxes(boxesTensor, anchorTensor); + t10.scores = Ws(logitsTensor); + t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b2 = config3.body["detector"]) == null ? void 0 : _b2.iouThreshold) || 0.1); + const nms = await t10.nms.data(); + const scores = await t10.scores.data(); + const boxes = await t10.boxes.array(); for (const i of Array.from(nms)) { const score = scores[i]; const boxRaw = boxes[i]; @@ -37280,18 +37579,18 @@ async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { const detectedBox = { score, boxRaw, box }; detectedBoxes.push(detectedBox); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return detectedBoxes; } async function detectBoxes(input, config3, outputSize2) { - const t6 = {}; - t6.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t6.logitsRaw = He(t6.res, [0, 0, 0], [1, -1, 1]); - t6.boxesRaw = He(t6.res, [0, 0, 1], [1, -1, -1]); - t6.logits = Up(t6.logitsRaw); - t6.boxes = Up(t6.boxesRaw); - const boxes = await decodeResults(t6.boxes, t6.logits, config3, outputSize2); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + const t10 = {}; + t10.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t10.logitsRaw = Ke(t10.res, [0, 0, 0], [1, -1, 1]); + t10.boxesRaw = Ke(t10.res, [0, 0, 1], [1, -1, -1]); + t10.logits = Up(t10.logitsRaw); + t10.boxes = Up(t10.boxesRaw); + const boxes = await decodeResults(t10.boxes, t10.logits, config3, outputSize2); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return boxes; } @@ -37351,12 +37650,12 @@ async function loadPose(config3) { } function prepareImage(input, size2, cropBox) { var _a2, _b2; - const t6 = {}; + const t10 = {}; if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b2 = input == null ? void 0 : input.shape) == null ? void 0 : _b2[2])) return input; let final; if (cropBox) { - t6.cropped = uq.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + t10.cropped = eK.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); } if (input.shape[1] !== input.shape[2]) { const height = [ @@ -37373,16 +37672,16 @@ function prepareImage(input, size2, cropBox) { width, [0, 0] ]; - t6.pad = Hs(t6.cropped || input, padding); - t6.resize = uq.resizeBilinear(t6.pad, [size2, size2]); - final = Ge(t6.resize, constants.tf255); + t10.pad = Ks(t10.cropped || input, padding); + t10.resize = eK.resizeBilinear(t10.pad, [size2, size2]); + final = He(t10.resize, constants.tf255); } else if (input.shape[1] !== size2) { - t6.resize = uq.resizeBilinear(t6.cropped || input, [size2, size2]); - final = Ge(t6.resize, constants.tf255); + t10.resize = eK.resizeBilinear(t10.cropped || input, [size2, size2]); + final = He(t10.resize, constants.tf255); } else { - final = Ge(t6.cropped || input, constants.tf255); + final = He(t10.cropped || input, constants.tf255); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return final; } function rescaleKeypoints(keypoints, outputSize2, cropBox) { @@ -37425,12 +37724,12 @@ function fixKeypoints(keypoints) { async function detectLandmarks(input, config3, outputSize2) { if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t6 = {}; - [t6.ld, t6.segmentation, t6.heatmap, t6.world, t6.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t6.poseflag.data())[0]; - const points = await t6.ld.data(); - const distances = await t6.world.data(); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + const t10 = {}; + [t10.ld, t10.segmentation, t10.heatmap, t10.world, t10.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); + const poseScore = (await t10.poseflag.data())[0]; + const points = await t10.ld.data(); + const distances = await t10.world.data(); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); const keypointsRelative = []; const depth = 5; for (let i = 0; i < points.length / depth; i++) { @@ -37474,7 +37773,7 @@ async function predict(input, config3) { if ((_b2 = (_a2 = config3.body) == null ? void 0 : _a2["detector"]) == null ? void 0 : _b2["enabled"]) { const preparedImage = prepareImage(input, 224); boxes = await detectBoxes(preparedImage, config3, outputSize2); - Dt(preparedImage); + Ot(preparedImage); } else { boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; } @@ -37482,7 +37781,7 @@ async function predict(input, config3) { const preparedBox = prepareImage(input, 256, (_c2 = boxes[i]) == null ? void 0 : _c2.boxRaw); cache.length = 0; const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - Dt(preparedBox); + Ot(preparedBox); if (!bodyResult) continue; bodyResult.id = i; @@ -37598,18 +37897,18 @@ async function load(config3) { async function process3(res, outputShape, config3) { if (!res) return []; - const t6 = {}; + const t10 = {}; const results = []; const detections = await res.array(); - t6.squeeze = Up(res); - const arr = Oa(t6.squeeze, 6, 1); - t6.stack = Sr([arr[1], arr[0], arr[3], arr[2]], 1); - t6.boxes = Up(t6.stack); - t6.scores = Up(arr[4]); - t6.classes = Up(arr[5]); - Dt([res, ...arr]); - t6.nms = await uq.nonMaxSuppressionAsync(t6.boxes, t6.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t6.nms.data(); + t10.squeeze = Up(res); + const arr = Oa(t10.squeeze, 6, 1); + t10.stack = Sr([arr[1], arr[0], arr[3], arr[2]], 1); + t10.boxes = Up(t10.stack); + t10.scores = Up(arr[4]); + t10.classes = Up(arr[5]); + Ot([res, ...arr]); + t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t10.nms.data(); let i = 0; for (const id2 of Array.from(nms)) { const score = Math.trunc(100 * detections[0][id2][4]) / 100; @@ -37617,15 +37916,15 @@ async function process3(res, outputShape, config3) { if (Number.isNaN(classVal)) continue; const label = labels2[classVal].label; - const [x, y10] = [ + const [x, y6] = [ detections[0][id2][0] / inputSize3, detections[0][id2][1] / inputSize3 ]; const boxRaw = [ x, - y10, + y6, detections[0][id2][2] / inputSize3 - x, - detections[0][id2][3] / inputSize3 - y10 + detections[0][id2][3] / inputSize3 - y6 ]; const box = [ Math.trunc(boxRaw[0] * outputShape[0]), @@ -37635,7 +37934,7 @@ async function process3(res, outputShape, config3) { ]; results.push({ id: i++, score, class: classVal, label, box, boxRaw }); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return results; } async function predict2(input, config3) { @@ -37650,10 +37949,10 @@ async function predict2(input, config3) { skipped2 = 0; return new Promise(async (resolve) => { const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = uq.resizeBilinear(input, [inputSize3, inputSize3]); + const resize = eK.resizeBilinear(input, [inputSize3, inputSize3]); const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; lastTime2 = now(); - Dt(resize); + Ot(resize); const obj = await process3(objectT, outputSize2, config3); last2 = obj; resolve(obj); @@ -37709,19 +38008,19 @@ async function load2(config3) { } async function max2d(inputs, minScore) { const [width, height] = inputs.shape; - const reshaped = z(inputs, [height * width]); - const max = Us(reshaped, 0); + const reshaped = W(inputs, [height * width]); + const max = Gs(reshaped, 0); const newScore = (await max.data())[0]; if (newScore > minScore) { - const coordinates = bv(reshaped, 0); - const mod = wk(coordinates, width); + const coordinates = X0(reshaped, 0); + const mod = Jv(coordinates, width); const x = (await mod.data())[0]; - const div = Ge(coordinates, width); - const y10 = (await div.data())[0]; - Dt([reshaped, max, coordinates, mod, div]); - return [x, y10, newScore]; + const div = He(coordinates, width); + const y6 = (await div.data())[0]; + Ot([reshaped, max, coordinates, mod, div]); + return [x, y6, newScore]; } - Dt([reshaped, max]); + Ot([reshaped, max]); return [0, 0, newScore]; } async function predict3(image, config3) { @@ -37737,49 +38036,49 @@ async function predict3(image, config3) { return new Promise(async (resolve) => { const tensor = Ee(() => { var _a2, _b2; - const resize = uq.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b2[1]) || 0], false); - const enhance2 = ae(resize, constants.tf2); - const norm = Ne(enhance2, constants.tf1); + const resize = eK.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b2[1]) || 0], false); + const enhance2 = ne(resize, constants.tf2); + const norm = ke(enhance2, constants.tf1); return norm; }); let resT; if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor); lastTime3 = now(); - Dt(tensor); + Ot(tensor); if (resT) { cache2.keypoints.length = 0; const squeeze = Up(resT); - Dt(resT); + Ot(resT); const stack = so(squeeze, 2); - Dt(squeeze); + Ot(squeeze); for (let id2 = 0; id2 < stack.length; id2++) { - const [x10, y11, partScore] = await max2d(stack[id2], config3.body.minConfidence); + const [x6, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence); if (partScore > (config3.body.minConfidence || 0)) { cache2.keypoints.push({ score: Math.round(100 * partScore) / 100, part: kpt2[id2], positionRaw: [ - x10 / model4.inputs[0].shape[2], - y11 / model4.inputs[0].shape[1] + x6 / model4.inputs[0].shape[2], + y10 / model4.inputs[0].shape[1] ], position: [ - Math.round(image.shape[2] * x10 / model4.inputs[0].shape[2]), - Math.round(image.shape[1] * y11 / model4.inputs[0].shape[1]) + Math.round(image.shape[2] * x6 / model4.inputs[0].shape[2]), + Math.round(image.shape[1] * y10 / model4.inputs[0].shape[1]) ] }); } } - stack.forEach((s) => Dt(s)); + stack.forEach((s) => Ot(s)); } cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); const x = cache2.keypoints.map((a) => a.position[0]); - const y10 = cache2.keypoints.map((a) => a.position[1]); + const y6 = cache2.keypoints.map((a) => a.position[1]); cache2.box = [ Math.min(...x), - Math.min(...y10), + Math.min(...y6), Math.max(...x) - Math.min(...x), - Math.max(...y10) - Math.min(...y10) + Math.max(...y6) - Math.min(...y6) ]; const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); @@ -37827,9 +38126,9 @@ var cutAndResize = (box, image, cropSize) => { const h = image.shape[1]; const w = image.shape[2]; const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = uq.cropAndResize(image, [cutBox], [0], cropSize); - const norm = Ge(crop, constants.tf255); - Dt(crop); + const crop = eK.cropAndResize(image, [cutBox], [0], cropSize); + const norm = He(crop, constants.tf255); + Ot(crop); return norm; }; var enlargeBox = (box, factor) => { @@ -37846,13 +38145,13 @@ var squarifyBox = (box) => { }; var calculateLandmarksBoundingBox = (landmarks) => { const x = landmarks.map((d) => d[0]); - const y10 = landmarks.map((d) => d[1]); - return { startPoint: [Math.min(...x), Math.min(...y10)], endPoint: [Math.max(...x), Math.max(...y10)], landmarks }; + const y6 = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y6)], endPoint: [Math.max(...x), Math.max(...y6)], landmarks }; }; var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y10) => [[1, 0, x], [0, 1, y10], [0, 0, 1]]; +var buildTranslationMatrix = (x, y6) => [[1, 0, x], [0, 1, y6], [0, 0, 1]]; var dot = (v12, v22) => { let product = 0; for (let i = 0; i < v12.length; i++) @@ -37940,10 +38239,10 @@ function correctFaceRotation(rotate, box, input, inputSize10) { if (largeAngle) { const center = getBoxCenter(box); const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = uq.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + const rotated = eK.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); rotationMatrix = buildRotationMatrix(-angle, center); face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - Dt(rotated); + Ot(rotated); } else { face4 = cutAndResize(box, input, [inputSize10, inputSize10]); } @@ -37954,8 +38253,8 @@ function correctFaceRotation(rotate, box, input, inputSize10) { } var findFaceCenter = (mesh) => { const x = mesh.map((m) => m[0]); - const y10 = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y10) + (Math.max(...y10) - Math.min(...y10)) / 2]; + const y6 = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y6) + (Math.max(...y6) - Math.min(...y6)) / 2]; }; var calculateFaceBox = (mesh, previousBox) => { const center = findFaceCenter(mesh); @@ -37984,65 +38283,65 @@ async function load3(config3) { else if (config3.debug) log("cached model:", model5["modelUrl"]); inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = be(inputSize4, "int32"); - anchors = _i(generateAnchors(inputSize4)); + inputSizeT = Ce(inputSize4, "int32"); + anchors = Oi(generateAnchors(inputSize4)); return model5; } function decodeBoxes2(boxOutputs) { if (!anchors || !inputSizeT) - return Vr([0, 0]); - const t6 = {}; - t6.boxStarts = He(boxOutputs, [0, 1], [-1, 2]); - t6.centers = xe(t6.boxStarts, anchors); - t6.boxSizes = He(boxOutputs, [0, 3], [-1, 2]); - t6.boxSizesNormalized = Ge(t6.boxSizes, inputSizeT); - t6.centersNormalized = Ge(t6.centers, inputSizeT); - t6.halfBoxSize = Ge(t6.boxSizesNormalized, constants.tf2); - t6.starts = Ne(t6.centersNormalized, t6.halfBoxSize); - t6.ends = xe(t6.centersNormalized, t6.halfBoxSize); - t6.startNormalized = ae(t6.starts, inputSizeT); - t6.endNormalized = ae(t6.ends, inputSizeT); - const boxes = Lv([t6.startNormalized, t6.endNormalized], 1); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + return Br([0, 0]); + const t10 = {}; + t10.boxStarts = Ke(boxOutputs, [0, 1], [-1, 2]); + t10.centers = be(t10.boxStarts, anchors); + t10.boxSizes = Ke(boxOutputs, [0, 3], [-1, 2]); + t10.boxSizesNormalized = He(t10.boxSizes, inputSizeT); + t10.centersNormalized = He(t10.centers, inputSizeT); + t10.halfBoxSize = He(t10.boxSizesNormalized, constants.tf2); + t10.starts = ke(t10.centersNormalized, t10.halfBoxSize); + t10.ends = be(t10.centersNormalized, t10.halfBoxSize); + t10.startNormalized = ne(t10.starts, inputSizeT); + t10.endNormalized = ne(t10.ends, inputSizeT); + const boxes = fv([t10.startNormalized, t10.endNormalized], 1); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return boxes; } async function getBoxes(inputImage, config3) { var _a2, _b2, _c2, _d2; if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t6 = {}; - t6.resized = uq.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t6.div = Ge(t6.resized, constants.tf127); - t6.normalized = Ne(t6.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t6.normalized); + const t10 = {}; + t10.resized = eK.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t10.div = He(t10.resized, constants.tf127); + t10.normalized = ke(t10.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t10.normalized); if (Array.isArray(res) && res.length > 2) { const sorted = res.sort((a, b) => a.size - b.size); - t6.concat384 = gt([sorted[0], sorted[2]], 2); - t6.concat512 = gt([sorted[1], sorted[3]], 2); - t6.concat = gt([t6.concat512, t6.concat384], 1); - t6.batch = Up(t6.concat, [0]); + t10.concat384 = xt([sorted[0], sorted[2]], 2); + t10.concat512 = xt([sorted[1], sorted[3]], 2); + t10.concat = xt([t10.concat512, t10.concat384], 1); + t10.batch = Up(t10.concat, [0]); } else if (Array.isArray(res)) { - t6.batch = Up(res[0]); + t10.batch = Up(res[0]); } else { - t6.batch = Up(res); + t10.batch = Up(res); } - Dt(res); - t6.boxes = decodeBoxes2(t6.batch); - t6.logits = He(t6.batch, [0, 0], [-1, 1]); - t6.sigmoid = zs(t6.logits); - t6.scores = Up(t6.sigmoid); - t6.nms = await uq.nonMaxSuppressionAsync(t6.boxes, t6.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0); - const nms = await t6.nms.array(); + Ot(res); + t10.boxes = decodeBoxes2(t10.batch); + t10.logits = Ke(t10.batch, [0, 0], [-1, 1]); + t10.sigmoid = Ws(t10.logits); + t10.scores = Up(t10.sigmoid); + t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0); + const nms = await t10.nms.array(); const boxes = []; - const scores = await t6.scores.data(); + const scores = await t10.scores.data(); for (let i = 0; i < nms.length; i++) { const confidence = scores[nms[i]]; if (confidence > (((_d2 = config3.face.detector) == null ? void 0 : _d2.minConfidence) || 0)) { const b = {}; - b.bbox = He(t6.boxes, [nms[i], 0], [1, -1]); - b.slice = He(t6.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.bbox = Ke(t10.boxes, [nms[i], 0], [1, -1]); + b.slice = Ke(t10.batch, [nms[i], keypointsCount - 1], [1, -1]); b.squeeze = Up(b.slice); - b.landmarks = z(b.squeeze, [keypointsCount, -1]); + b.landmarks = W(b.squeeze, [keypointsCount, -1]); const points = await b.bbox.data(); const rawBox = { startPoint: [points[0], points[1]], @@ -38054,10 +38353,10 @@ async function getBoxes(inputImage, config3) { const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); const squaredBox = squarifyBox(enlargedBox); boxes.push(squaredBox); - Object.keys(b).forEach((tensor) => Dt(b[tensor])); + Object.keys(b).forEach((tensor) => Ot(b[tensor])); } } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return boxes; } @@ -38095,12 +38394,12 @@ function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { const { key, indices } = irisIndices[i]; const originalIndices = meshAnnotations[`${prefix}${key}`]; if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ + for (let j10 = 0; j10 < indices.length; j10++) { + const index2 = indices[j10]; + rawCoords[originalIndices[j10]] = [ newCoords[index2][0], newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + (newCoords[index2][2] + rawCoords[originalIndices[j10]][2]) / 2 ]; } } @@ -38114,15 +38413,15 @@ var getLeftToRightEyeDepthDifference = (rawCoords) => { var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); const boxSize = getBoxSize(box); - let crop = uq.cropAndResize(face4, [[ + let crop = eK.cropAndResize(face4, [[ box.startPoint[1] / meshSize, box.startPoint[0] / meshSize, box.endPoint[1] / meshSize, box.endPoint[0] / meshSize ]], [0], [inputSize5, inputSize5]); if (flip && env.kernels.includes("flipleftright")) { - const flipped = uq.flipLeftRight(crop); - Dt(crop); + const flipped = eK.flipLeftRight(crop); + Ot(crop); crop = flipped; } return { box, boxSize, crop }; @@ -38131,12 +38430,12 @@ var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { const eyeRawCoords = []; for (let i = 0; i < irisLandmarks.numCoordinates; i++) { const x = eyeData[i * 3]; - const y10 = eyeData[i * 3 + 1]; - const z8 = eyeData[i * 3 + 2]; + const y6 = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; eyeRawCoords.push([ (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y10 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z8 + y6 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z ]); } return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; @@ -38146,13 +38445,13 @@ var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; const averageZ = (upperCenterZ + lowerCenterZ) / 2; return irisCoords.map((coord, i) => { - let z8 = averageZ; + let z = averageZ; if (i === 2) { - z8 = upperCenterZ; + z = upperCenterZ; } else if (i === 4) { - z8 = lowerCenterZ; + z = lowerCenterZ; } - return [coord[0], coord[1], z8]; + return [coord[0], coord[1], z]; }); }; async function augmentIris(rawCoords, face4, meshSize) { @@ -38160,13 +38459,13 @@ async function augmentIris(rawCoords, face4, meshSize) { return rawCoords; const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = gt([leftEyeCrop, rightEyeCrop]); - Dt(leftEyeCrop); - Dt(rightEyeCrop); + const combined = xt([leftEyeCrop, rightEyeCrop]); + Ot(leftEyeCrop); + Ot(rightEyeCrop); const eyePredictions = model6.execute(combined); - Dt(combined); + Ot(combined); const eyePredictionsData = await eyePredictions.data(); - Dt(eyePredictions); + Ot(eyePredictions); const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); @@ -38188,30 +38487,30 @@ async function augmentIris(rawCoords, face4, meshSize) { // src/face/attention.ts async function augment(rawCoords, results) { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2; - const t6 = { + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2; + const t10 = { lips: await ((_b2 = (_a2 = results.filter((r) => r.size === 160)) == null ? void 0 : _a2[0]) == null ? void 0 : _b2.data()), irisL: await ((_d2 = (_c2 = results.filter((r) => r.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()), - eyeL: await ((_f2 = (_e2 = results.filter((r) => r.size === 142)) == null ? void 0 : _e2[0]) == null ? void 0 : _f2.data()), + eyeL: await ((_f2 = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f2.data()), irisR: await ((_h2 = (_g2 = results.filter((r) => r.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()), eyeR: await ((_j2 = (_i2 = results.filter((r) => r.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data()) }; - for (const val of Object.values(t6)) { + for (const val of Object.values(t10)) { if (!val) return rawCoords; } const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t6.irisL.length / 2; i++) - rawCoords.push([t6.irisL[2 * i + 0], t6.irisL[2 * i + 1], irisLDepth]); + for (let i = 0; i < t10.irisL.length / 2; i++) + rawCoords.push([t10.irisL[2 * i + 0], t10.irisL[2 * i + 1], irisLDepth]); const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t6.irisR.length / 2; i++) - rawCoords.push([t6.irisR[2 * i + 0], t6.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t6.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t6.eyeL[2 * i + 0], t6.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t6.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t6.eyeR[2 * i + 0], t6.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t6.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t6.lips[2 * i + 0], t6.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + for (let i = 0; i < t10.irisR.length / 2; i++) + rawCoords.push([t10.irisR[2 * i + 0], t10.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t10.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t10.eyeL[2 * i + 0], t10.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t10.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t10.eyeR[2 * i + 0], t10.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t10.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t10.lips[2 * i + 0], t10.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; return rawCoords; } @@ -38224,7 +38523,7 @@ var cache3 = { var model7 = null; var inputSize6 = 0; async function predict4(input, config3) { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2; if (!(model7 == null ? void 0 : model7["executor"])) return []; const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp; @@ -38258,12 +38557,12 @@ async function predict4(input, config3) { [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c2 = config3.face.detector) == null ? void 0 : _c2.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size()); if (config3.filter.equalization) { const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - Dt(face4.tensor); + Ot(face4.tensor); if (equilized) face4.tensor = equilized; } face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e2 = config3.face.mesh) == null ? void 0 : _e2.enabled)) { + if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { face4.box = clampBox(box, input); face4.boxRaw = getRawBox(box, input); face4.score = face4.boxScore; @@ -38281,11 +38580,11 @@ async function predict4(input, config3) { } else { if (((_f2 = config3.face.attention) == null ? void 0 : _f2.enabled) && !env.kernels.includes("atan2")) { config3.face.attention.enabled = false; - Dt(face4.tensor); + Ot(face4.tensor); return faces; } const results = model7.execute(face4.tensor); - const confidenceT = results.find((t6) => t6.shape[t6.shape.length - 1] === 1); + const confidenceT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1); const faceConfidence = await confidenceT.data(); face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) { @@ -38304,10 +38603,10 @@ async function predict4(input, config3) { } } } else { - const meshT = results.find((t6) => t6.shape[t6.shape.length - 1] === 1404); - const coordsReshaped = z(meshT, [-1, 3]); + const meshT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1404); + const coordsReshaped = W(meshT, [-1, 3]); let rawCoords = await coordsReshaped.array(); - Dt(coordsReshaped); + Ot(coordsReshaped); if ((_h2 = config3.face.attention) == null ? void 0 : _h2.enabled) { rawCoords = await augment(rawCoords, results); } else if ((_i2 = config3.face.iris) == null ? void 0 : _i2.enabled) { @@ -38323,18 +38622,18 @@ async function predict4(input, config3) { face4.boxRaw = getRawBox(calculatedBox, input); newCache.push(calculatedBox); } - Dt(results); + Ot(results); } if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1)) faces.push(face4); else - Dt(face4.tensor); + Ot(face4.tensor); } cache3.boxes = newCache; return faces; } async function load5(config3) { - var _a2, _b2, _c2, _d2, _e2, _f2; + var _a2, _b2, _c2, _d2, _e, _f2; if (env.initial) model7 = null; if (((_a2 = config3.face.attention) == null ? void 0 : _a2.enabled) && (model7 == null ? void 0 : model7["signature"])) { @@ -38349,7 +38648,7 @@ async function load5(config3) { } else if (config3.debug) { log("cached model:", model7["modelUrl"]); } - inputSize6 = model7["executor"] && ((_e2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e2[0].shape) ? (_f2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f2[0].shape[2] : 256; + inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f2[0].shape[2] : 256; return model7; } var triangulation = TRI468; @@ -38387,22 +38686,22 @@ async function predict5(image, config3, idx, count2) { var _a3; const obj = []; if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) { - const t6 = {}; + const t10 = {}; const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - t6.resize = uq.resizeBilinear(image, [inputSize10, inputSize10], false); - t6.channels = ae(t6.resize, constants.rgb); - t6.grayscale = et(t6.channels, 3, true); - t6.grayscaleSub = Ne(t6.grayscale, constants.tf05); - t6.grayscaleMul = ae(t6.grayscaleSub, constants.tf2); - t6.emotion = model8 == null ? void 0 : model8.execute(t6.grayscaleMul); + t10.resize = eK.resizeBilinear(image, [inputSize10, inputSize10], false); + t10.channels = ne(t10.resize, constants.rgb); + t10.grayscale = et(t10.channels, 3, true); + t10.grayscaleSub = ke(t10.grayscale, constants.tf05); + t10.grayscaleMul = ne(t10.grayscaleSub, constants.tf2); + t10.emotion = model8 == null ? void 0 : model8.execute(t10.grayscaleMul); lastTime4 = now(); - const data = await t6.emotion.data(); + const data = await t10.emotion.data(); for (let i = 0; i < data.length; i++) { if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); } obj.sort((a, b) => b.score - a.score); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); } last3[idx] = obj; lastCount = count2; @@ -38430,9 +38729,9 @@ function enhance(input) { const tensor = input.image || input.tensor || input; if (!(model9 == null ? void 0 : model9.inputs[0].shape)) return tensor; - const crop = uq.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - const norm = ae(crop, constants.tf255); - Dt(crop); + const crop = eK.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + const norm = ne(crop, constants.tf255); + Ot(crop); return norm; } async function predict6(image, config3, idx, count2) { @@ -38458,26 +38757,26 @@ async function predict6(image, config3, idx, count2) { const enhanced = enhance(image); const resT = model9 == null ? void 0 : model9.execute(enhanced); lastTime5 = now(); - Dt(enhanced); - const genderT = resT.find((t6) => t6.shape[1] === 1); + Ot(enhanced); + const genderT = resT.find((t10) => t10.shape[1] === 1); const gender2 = await genderT.data(); const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; if (confidence > (config3.face.description.minConfidence || 0)) { obj.gender = gender2[0] <= 0.5 ? "female" : "male"; obj.genderScore = Math.min(0.99, confidence); } - const argmax = bv(resT.find((t6) => t6.shape[1] === 100), 1); + const argmax = X0(resT.find((t10) => t10.shape[1] === 100), 1); const ageIdx = (await argmax.data())[0]; - Dt(argmax); - const ageT = resT.find((t6) => t6.shape[1] === 100); + Ot(argmax); + const ageT = resT.find((t10) => t10.shape[1] === 100); const all2 = await ageT.data(); obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t6) => t6.shape[1] === 1024); + const desc = resT.find((t10) => t10.shape[1] === 1024); const descriptor = desc ? await desc.data() : []; obj.descriptor = Array.from(descriptor); - resT.forEach((t6) => Dt(t6)); + resT.forEach((t10) => Ot(t10)); } last4[idx] = obj; lastCount2 = count2; @@ -38488,11 +38787,11 @@ async function predict6(image, config3, idx, count2) { // src/face/mask.ts var expandFact = 0.1; var alpha = 0.5; -function insidePoly(x, y10, polygon) { +function insidePoly(x, y6, polygon) { let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y10 !== polygon[j].y > y10 && x < (polygon[j].x - polygon[i].x) * (y10 - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + let j10 = polygon.length - 1; + for (let i = 0; i < polygon.length; j10 = i++) { + if (polygon[i].y > y6 !== polygon[j10].y > y6 && x < (polygon[j10].x - polygon[i].x) * (y6 - polygon[i].y) / (polygon[j10].y - polygon[i].y) + polygon[i].x) inside = !inside; } return inside; @@ -38511,12 +38810,12 @@ async function mask(face4) { if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); for (let x = 0; x < width; x++) { - for (let y10 = 0; y10 < height; y10++) { - const inside = insidePoly(x / width, y10 / width, silhouette); + for (let y6 = 0; y6 < height; y6++) { + const inside = insidePoly(x / width, y6 / width, silhouette); if (!inside) { - buffer.set(alpha * buffer.get(0, y10, x, 0), 0, y10, x, 0); - buffer.set(alpha * buffer.get(0, y10, x, 1), 0, y10, x, 1); - buffer.set(alpha * buffer.get(0, y10, x, 2), 0, y10, x, 2); + buffer.set(alpha * buffer.get(0, y6, x, 0), 0, y6, x, 0); + buffer.set(alpha * buffer.get(0, y6, x, 1), 0, y6, x, 1); + buffer.set(alpha * buffer.get(0, y6, x, 2), 0, y6, x, 2); } } } @@ -38552,13 +38851,13 @@ async function predict7(image, config3, idx, count2) { } skipped6 = 0; return new Promise(async (resolve) => { - const resize = uq.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const resize = eK.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); const res = model10 == null ? void 0 : model10.execute(resize); const num = (await res.data())[0]; cached[idx] = Math.round(100 * num) / 100; lastCount3 = count2; lastTime6 = now(); - Dt([resize, res]); + Ot([resize, res]); resolve(cached[idx]); }); } @@ -38591,13 +38890,13 @@ async function predict8(image, config3, idx, count2) { } skipped7 = 0; return new Promise(async (resolve) => { - const resize = uq.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const resize = eK.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); const res = model11 == null ? void 0 : model11.execute(resize); const num = (await res.data())[0]; cached2[idx] = Math.round(100 * num) / 100; lastCount4 = count2; lastTime7 = now(); - Dt([resize, res]); + Ot([resize, res]); resolve(cached2[idx]); }); } @@ -38635,28 +38934,28 @@ async function predict9(image, config3, idx, count2) { var _a3, _b3; if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t6 = {}; + const t10 = {}; const box = [[0, 0.1, 0.9, 0.9]]; - t6.resize = uq.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + t10.resize = eK.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; if ((_a3 = config3.face.gear) == null ? void 0 : _a3.enabled) - [t6.age, t6.gender, t6.race] = model12.execute(t6.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t6.gender.data(); + [t10.age, t10.gender, t10.race] = model12.execute(t10.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t10.gender.data(); obj.gender = gender2[0] > gender2[1] ? "male" : "female"; obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t6.race.data(); + const race = await t10.race.data(); for (let i = 0; i < race.length; i++) { if (race[i] > (((_b3 = config3.face.gear) == null ? void 0 : _b3.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); } obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t6.age.data()); + const ageDistribution = Array.from(await t10.age.data()); const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); let age2 = ageSorted[0][0]; for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); obj.age = Math.round(10 * age2) / 10; - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); last5[idx] = obj; lastCount5 = count2; lastTime8 = now(); @@ -38694,17 +38993,17 @@ async function predict10(image, config3, idx, count2) { var _a3; if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t6 = {}; - t6.resize = uq.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - t6.enhance = ae(t6.resize, constants.tf255); + const t10 = {}; + t10.resize = eK.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t10.enhance = ne(t10.resize, constants.tf255); const obj = { age: 0 }; if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled) - t6.age = model13.execute(t6.enhance); - if (t6.age) { - const data = await t6.age.data(); + t10.age = model13.execute(t10.enhance); + if (t10.age) { + const data = await t10.age.data(); obj.age = Math.trunc(10 * data[0]) / 10; } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); last6[idx] = obj; lastCount6 = count2; lastTime9 = now(); @@ -38744,24 +39043,24 @@ async function predict11(image, config3, idx, count2) { var _a3; if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t6 = {}; - t6.resize = uq.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - t6.enhance = Ee(() => { - const [red, green, blue] = Oa(t6.resize, 3, 3); - const redNorm = ae(red, rgb[0]); - const greenNorm = ae(green, rgb[1]); - const blueNorm = ae(blue, rgb[2]); - const grayscale = gv([redNorm, greenNorm, blueNorm]); - const normalize2 = ae(Ne(grayscale, constants.tf05), 2); + const t10 = {}; + t10.resize = eK.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t10.enhance = Ee(() => { + const [red, green, blue] = Oa(t10.resize, 3, 3); + const redNorm = ne(red, rgb[0]); + const greenNorm = ne(green, rgb[1]); + const blueNorm = ne(blue, rgb[2]); + const grayscale = K0([redNorm, greenNorm, blueNorm]); + const normalize2 = ne(ke(grayscale, constants.tf05), 2); return normalize2; }); const obj = { gender: "unknown", genderScore: 0 }; if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled) - t6.gender = model14.execute(t6.enhance); - const data = await t6.gender.data(); + t10.gender = model14.execute(t10.enhance); + const data = await t10.gender.data(); obj.gender = data[0] > data[1] ? "female" : "male"; obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); last7[idx] = obj; lastCount7 = count2; lastTime10 = now(); @@ -38799,12 +39098,12 @@ async function predict12(input, config3, idx, count2) { var _a3; let data = []; if (((_a3 = config3.face["mobilefacenet"]) == null ? void 0 : _a3.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t6 = {}; - t6.crop = uq.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t6.data = model15.execute(t6.crop); - const output = await t6.data.data(); + const t10 = {}; + t10.crop = eK.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t10.data = model15.execute(t10.crop); + const output = await t10.data.data(); data = Array.from(output); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); } last8[idx] = data; lastCount8 = count2; @@ -38842,12 +39141,12 @@ async function predict13(input, config3, idx, count2) { var _a3; let data = []; if (((_a3 = config3.face["insightface"]) == null ? void 0 : _a3.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t6 = {}; - t6.crop = uq.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t6.data = model16.execute(t6.crop); - const output = await t6.data.data(); + const t10 = {}; + t10.crop = eK.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t10.data = model16.execute(t10.crop); + const output = await t10.data.data(); data = Array.from(output); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); } last9[idx] = data; lastCount9 = count2; @@ -38886,15 +39185,15 @@ var calculateFaceAngle = (face4, imageSize) => { }; const subVectors = (a, b) => { const x = a[0] - b[0]; - const y10 = a[1] - b[1]; - const z8 = a[2] - b[2]; - return [x, y10, z8]; + const y6 = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y6, z]; }; const crossVectors = (a, b) => { const x = a[1] * b[2] - a[2] * b[1]; - const y10 = a[2] * b[0] - a[0] * b[2]; - const z8 = a[0] * b[1] - a[1] * b[0]; - return [x, y10, z8]; + const y6 = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y6, z]; }; const rotationMatrixToEulerAngle = (r) => { const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; @@ -38961,7 +39260,7 @@ function calculateCameraDistance(face4, width) { // src/face/face.ts var detectFace = async (instance, input) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m2, _n2, _o2, _p, _q2, _r2, _s2, _t, _u2, _v2, _w2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2; let timeStamp = now(); let ageRes; let gearRes; @@ -38988,7 +39287,7 @@ var detectFace = async (instance, input) => { } if ((_a2 = instance.config.face.detector) == null ? void 0 : _a2.mask) { const masked = await mask(faces[i]); - Dt(faces[i].tensor); + Ot(faces[i].tensor); if (masked) faces[i].tensor = masked; } @@ -39009,7 +39308,7 @@ var detectFace = async (instance, input) => { } else { instance.state = "run:antispoof"; timeStamp = now(); - antispoofRes = ((_e2 = instance.config.face.antispoof) == null ? void 0 : _e2.enabled) ? await predict7(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0; + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0; instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } instance.analyze("End AntiSpoof:"); @@ -39036,7 +39335,7 @@ var detectFace = async (instance, input) => { instance.analyze("Start SSRNet:"); if (instance.config.async) { ageRes = ((_j2 = instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) ? predict10(faces[i].tensor || nr([]), instance.config, i, faces.length) : null; - genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict11(faces[i].tensor || nr([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || nr([]), instance.config, i, faces.length) : null; } else { instance.state = "run:ssrnet"; timeStamp = now(); @@ -39057,7 +39356,7 @@ var detectFace = async (instance, input) => { instance.analyze("End MobileFaceNet:"); instance.analyze("Start InsightFace:"); if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || nr([]), instance.config, i, faces.length) : null; + insightfaceRes = ((_p2 = instance.config.face["insightface"]) == null ? void 0 : _p2.enabled) ? predict13(faces[i].tensor || nr([]), instance.config, i, faces.length) : null; } else { instance.state = "run:mobilefacenet"; timeStamp = now(); @@ -39104,7 +39403,7 @@ var detectFace = async (instance, input) => { } const irisSize = ((_v2 = instance.config.face.iris) == null ? void 0 : _v2.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; const tensor = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? Up(faces[i].tensor) : null; - Dt(faces[i].tensor); + Ot(faces[i].tensor); if (faces[i].tensor) delete faces[i].tensor; const res = { @@ -39697,7 +39996,7 @@ function cutBoxFromImageAndResize(box, image, cropSize) { box.endPoint[1] / h, box.endPoint[0] / w ]]; - return uq.cropAndResize(image, boxes, [0], cropSize); + return eK.cropAndResize(image, boxes, [0], cropSize); } function scaleBoxCoordinates2(box, factor) { const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; @@ -39732,7 +40031,7 @@ function computeRotation2(point1, point2) { const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); return normalizeRadians2(radians); } -var buildTranslationMatrix2 = (x, y10) => [[1, 0, x], [0, 1, y10], [0, 0, 1]]; +var buildTranslationMatrix2 = (x, y6) => [[1, 0, x], [0, 1, y6], [0, 0, 1]]; function dot2(v12, v22) { let product = 0; for (let i = 0; i < v12.length; i++) { @@ -42747,58 +43046,58 @@ var HandDetector = class { var _a2, _b2, _c2, _d2; this.model = model23; this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = _i(this.anchors); + this.anchorsTensor = Oi(this.anchors); this.inputSize = ((_d2 = (_c2 = (_b2 = (_a2 = this == null ? void 0 : this.model) == null ? void 0 : _a2.inputs) == null ? void 0 : _b2[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0; - this.inputSizeTensor = mr([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = mr([this.inputSize * 2, this.inputSize * 2]); + this.inputSizeTensor = dr([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = dr([this.inputSize * 2, this.inputSize * 2]); } normalizeBoxes(boxes) { - const t6 = {}; - t6.boxOffsets = He(boxes, [0, 0], [-1, 2]); - t6.boxSizes = He(boxes, [0, 2], [-1, 2]); - t6.div = Ge(t6.boxOffsets, this.inputSizeTensor); - t6.boxCenterPoints = xe(t6.div, this.anchorsTensor); - t6.halfBoxSizes = Ge(t6.boxSizes, this.doubleInputSizeTensor); - t6.sub = Ne(t6.boxCenterPoints, t6.halfBoxSizes); - t6.startPoints = ae(t6.sub, this.inputSizeTensor); - t6.add = xe(t6.boxCenterPoints, t6.halfBoxSizes); - t6.endPoints = ae(t6.add, this.inputSizeTensor); - const res = Lv([t6.startPoints, t6.endPoints], 1); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + const t10 = {}; + t10.boxOffsets = Ke(boxes, [0, 0], [-1, 2]); + t10.boxSizes = Ke(boxes, [0, 2], [-1, 2]); + t10.div = He(t10.boxOffsets, this.inputSizeTensor); + t10.boxCenterPoints = be(t10.div, this.anchorsTensor); + t10.halfBoxSizes = He(t10.boxSizes, this.doubleInputSizeTensor); + t10.sub = ke(t10.boxCenterPoints, t10.halfBoxSizes); + t10.startPoints = ne(t10.sub, this.inputSizeTensor); + t10.add = be(t10.boxCenterPoints, t10.halfBoxSizes); + t10.endPoints = ne(t10.add, this.inputSizeTensor); + const res = fv([t10.startPoints, t10.endPoints], 1); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return res; } normalizeLandmarks(rawPalmLandmarks, index2) { - const t6 = {}; - t6.reshape = z(rawPalmLandmarks, [-1, 7, 2]); - t6.div = Ge(t6.reshape, this.inputSizeTensor); - t6.landmarks = xe(t6.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = ae(t6.landmarks, this.inputSizeTensor); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + const t10 = {}; + t10.reshape = W(rawPalmLandmarks, [-1, 7, 2]); + t10.div = He(t10.reshape, this.inputSizeTensor); + t10.landmarks = be(t10.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = ne(t10.landmarks, this.inputSizeTensor); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return res; } async predict(input, config3) { var _a2; - const t6 = {}; - t6.resize = uq.resizeBilinear(input, [this.inputSize, this.inputSize]); - t6.div = Ge(t6.resize, constants.tf127); - t6.image = Ne(t6.div, constants.tf1); - t6.batched = this.model.execute(t6.image); - t6.predictions = Up(t6.batched); - t6.slice = He(t6.predictions, [0, 0], [-1, 1]); - t6.sigmoid = zs(t6.slice); - t6.scores = Up(t6.sigmoid); - const scores = await t6.scores.data(); - t6.boxes = He(t6.predictions, [0, 1], [-1, 4]); - t6.norm = this.normalizeBoxes(t6.boxes); - t6.nms = await uq.nonMaxSuppressionAsync(t6.norm, t6.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t6.nms.array(); + const t10 = {}; + t10.resize = eK.resizeBilinear(input, [this.inputSize, this.inputSize]); + t10.div = He(t10.resize, constants.tf127); + t10.image = ke(t10.div, constants.tf1); + t10.batched = this.model.execute(t10.image); + t10.predictions = Up(t10.batched); + t10.slice = Ke(t10.predictions, [0, 0], [-1, 1]); + t10.sigmoid = Ws(t10.slice); + t10.scores = Up(t10.sigmoid); + const scores = await t10.scores.data(); + t10.boxes = Ke(t10.predictions, [0, 1], [-1, 4]); + t10.norm = this.normalizeBoxes(t10.boxes); + t10.nms = await eK.nonMaxSuppressionAsync(t10.norm, t10.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t10.nms.array(); const hands = []; for (const index2 of nms) { const p = {}; - p.box = He(t6.norm, [index2, 0], [1, -1]); - p.slice = He(t6.predictions, [index2, 5], [1, 14]); + p.box = Ke(t10.norm, [index2, 0], [1, -1]); + p.slice = Ke(t10.predictions, [index2, 5], [1, 14]); p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = z(p.norm, [-1, 2]); + p.palmLandmarks = W(p.norm, [-1, 2]); const box = await p.box.data(); const startPoint = box.slice(0, 2); const endPoint = box.slice(2, 4); @@ -42806,9 +43105,9 @@ var HandDetector = class { const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); hands.push(scaled); - Object.keys(p).forEach((tensor) => Dt(p[tensor])); + Object.keys(p).forEach((tensor) => Ot(p[tensor])); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return hands; } }; @@ -42908,23 +43207,23 @@ var HandPipeline = class { const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; const palmCenter = getBoxCenter2(currentBox); const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? uq.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone(); + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? eK.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone(); const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = Ge(croppedInput, constants.tf255); - Dt(croppedInput); - Dt(rotatedImage); + const handImage = He(croppedInput, constants.tf255); + Ot(croppedInput); + Ot(rotatedImage); const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); lastTime13 = now(); - Dt(handImage); + Ot(handImage); const confidence = (await confidenceT.data())[0]; - Dt(confidenceT); + Ot(confidenceT); if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = z(keypoints, [-1, 3]); + const keypointsReshaped = W(keypoints, [-1, 3]); const rawCoords = await keypointsReshaped.array(); - Dt(keypoints); - Dt(keypointsReshaped); + Ot(keypoints); + Ot(keypointsReshaped); const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); const nextBoundingBox = this.getBoxForHandLandmarks(coords); this.storedBoxes[i] = { ...nextBoundingBox, confidence }; @@ -42939,7 +43238,7 @@ var HandPipeline = class { } else { this.storedBoxes[i] = null; } - Dt(keypoints); + Ot(keypoints); } else { const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); const result = { @@ -43110,31 +43409,31 @@ async function detectHands(input, config3) { const hands = []; if (!input || !models2[0]) return hands; - const t6 = {}; + const t10 = {}; const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); const width = Math.round(height * ratio2 / 8) * 8; - t6.resize = uq.resizeBilinear(input, [height, width]); - t6.cast = Ke(t6.resize, "int32"); - [t6.rawScores, t6.rawBoxes] = await models2[0].executeAsync(t6.cast, modelOutputNodes); - t6.boxes = Up(t6.rawBoxes, [0, 2]); - t6.scores = Up(t6.rawScores, [0]); - const classScores = so(t6.scores, 1); - Dt(classScores[faceIndex]); + t10.resize = eK.resizeBilinear(input, [height, width]); + t10.cast = je(t10.resize, "int32"); + [t10.rawScores, t10.rawBoxes] = await models2[0].executeAsync(t10.cast, modelOutputNodes); + t10.boxes = Up(t10.rawBoxes, [0, 2]); + t10.scores = Up(t10.rawScores, [0]); + const classScores = so(t10.scores, 1); + Ot(classScores[faceIndex]); classScores.splice(faceIndex, 1); - t6.filtered = Sr(classScores, 1); - Dt(classScores); - t6.max = Us(t6.filtered, 1); - t6.argmax = bv(t6.filtered, 1); + t10.filtered = Sr(classScores, 1); + Ot(classScores); + t10.max = Gs(t10.filtered, 1); + t10.argmax = X0(t10.filtered, 1); let id2 = 0; - t6.nms = await uq.nonMaxSuppressionAsync(t6.boxes, t6.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t6.nms.data(); - const scores = await t6.max.data(); - const classNum = await t6.argmax.data(); + t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t10.nms.data(); + const scores = await t10.max.data(); + const classNum = await t10.argmax.data(); for (const nmsIndex of Array.from(nms)) { - const boxSlice = He(t6.boxes, nmsIndex, 1); + const boxSlice = Ke(t10.boxes, nmsIndex, 1); const boxYX = await boxSlice.data(); - Dt(boxSlice); + Ot(boxSlice); const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; const boxRaw = scale(boxData, detectorExpandFact); const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; @@ -43143,7 +43442,7 @@ async function detectHands(input, config3) { const hand3 = { id: id2++, score, box: boxFull, boxRaw, label }; hands.push(hand3); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); hands.sort((a, b) => b.score - a.score); if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; @@ -43163,17 +43462,17 @@ async function detectFingers(input, h, config3) { annotations: {} }; if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t6 = {}; + const t10 = {}; const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t6.crop = uq.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t6.div = Ge(t6.crop, constants.tf255); - [t6.score, t6.keypoints] = models2[1].execute(t6.div, ["Identity_1", "Identity"]); - const rawScore = (await t6.score.data())[0]; + t10.crop = eK.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t10.div = He(t10.crop, constants.tf255); + [t10.score, t10.keypoints] = models2[1].execute(t10.div, ["Identity_1", "Identity"]); + const rawScore = (await t10.score.data())[0]; const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; if (score >= (config3.hand.minConfidence || 0)) { hand3.fingerScore = score; - t6.reshaped = z(t6.keypoints, [-1, 3]); - const coordsData = await t6.reshaped.array(); + t10.reshaped = W(t10.keypoints, [-1, 3]); + const coordsData = await t10.reshaped.array(); const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); @@ -43182,7 +43481,7 @@ async function detectFingers(input, h, config3) { hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); } } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); } return hand3; } @@ -43295,7 +43594,7 @@ var connected3 = { var bufferedResult = empty(); var interpolateTime = 0; function calc2(newResult, config3) { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m2, _n2, _o2, _p, _q2, _r2, _s2, _t, _u2, _v2, _w2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2; const t0 = now(); if (!newResult) return empty(); @@ -43309,27 +43608,27 @@ function calc2(newResult, config3) { bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); } else { for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a3, _b3, _c3, _d3, _e3, _f3, _g3, _h3, _i3; + const box = newResult.body[i].box.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j10] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j10] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j10) => { + var _a3, _b3, _c3, _d3, _e2, _f3, _g3, _h3, _i3; return { score: newKpt.score, part: newKpt.part, position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] ], positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] ], distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d3[1]) || 0) + (((_e3 = newKpt.distance) == null ? void 0 : _e3[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2] + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _d3[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1], + bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2] ] }; }); @@ -43343,9 +43642,9 @@ function calc2(newResult, config3) { coords = movenetcoords_exports; for (const [name, indexes] of Object.entries(coords.connected)) { const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp2) => kp2.part === indexes[j]); - const pt1 = keypoints.find((kp2) => kp2.part === indexes[j + 1]); + for (let j10 = 0; j10 < indexes.length - 1; j10++) { + const pt0 = keypoints.find((kp2) => kp2.part === indexes[j10]); + const pt1 = keypoints.find((kp2) => kp2.part === indexes[j10 + 1]); if (pt0 && pt1) pt.push([pt0.position, pt1.position]); } @@ -43358,18 +43657,18 @@ function calc2(newResult, config3) { bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); } else { for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + const box = newResult.hand[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j10] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j10] + b) / bufferedFactor); if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j10) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j10][k] || 1) + (coord || 0)) / bufferedFactor)) : []; let annotations2 = {}; if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { bufferedResult.hand[i].annotations = newResult.hand[i].annotations; annotations2 = bufferedResult.hand[i].annotations; } else if (newResult.hand[i].annotations) { for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f2 = (_e2 = (_d2 = newResult.hand[i]) == null ? void 0 : _d2.annotations) == null ? void 0 : _e2[key]) == null ? void 0 : _f2[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + annotations2[key] = ((_f2 = (_e = (_d2 = newResult.hand[i]) == null ? void 0 : _d2.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f2[0]) ? newResult.hand[i].annotations[key].map((val, j10) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j10][k] + coord) / bufferedFactor)) : null; } } bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; @@ -43379,15 +43678,15 @@ function calc2(newResult, config3) { bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); } else { for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + const box = newResult.face[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j10] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j10] + b) / bufferedFactor); if (newResult.face[i].rotation) { const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; rotation.matrix = (_g2 = newResult.face[i].rotation) == null ? void 0 : _g2.matrix; rotation.angle = { - roll: ((bufferedFactor - 1) * (((_i2 = (_h2 = bufferedResult.face[i].rotation) == null ? void 0 : _h2.angle) == null ? void 0 : _i2.roll) || 0) + (((_k2 = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k2.roll) || 0)) / bufferedFactor, + roll: ((bufferedFactor - 1) * (((_i2 = (_h2 = bufferedResult.face[i].rotation) == null ? void 0 : _h2.angle) == null ? void 0 : _i2.roll) || 0) + (((_k = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, yaw: ((bufferedFactor - 1) * (((_m2 = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m2.yaw) || 0) + (((_o2 = (_n2 = newResult.face[i].rotation) == null ? void 0 : _n2.angle) == null ? void 0 : _o2.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_q2 = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q2.pitch) || 0) + (((_s2 = (_r2 = newResult.face[i].rotation) == null ? void 0 : _r2.angle) == null ? void 0 : _s2.pitch) || 0)) / bufferedFactor + pitch: ((bufferedFactor - 1) * (((_q2 = (_p2 = bufferedResult.face[i].rotation) == null ? void 0 : _p2.angle) == null ? void 0 : _q2.pitch) || 0) + (((_s2 = (_r2 = newResult.face[i].rotation) == null ? void 0 : _r2.angle) == null ? void 0 : _s2.pitch) || 0)) / bufferedFactor }; rotation.gaze = { bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u2 = newResult.face[i].rotation) == null ? void 0 : _u2.gaze.bearing) || 0)) / bufferedFactor, @@ -43403,8 +43702,8 @@ function calc2(newResult, config3) { bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); } else { for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + const box = newResult.object[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j10] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j10] + b) / bufferedFactor); bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; } } @@ -43414,7 +43713,7 @@ function calc2(newResult, config3) { bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); } else { for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + bufferedResult.persons[i].box = newPersons[i].box.map((box, j10) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j10] + box) / bufferedFactor); } } } @@ -43444,30 +43743,30 @@ async function predict16(input, config3) { model17 = await load16(config3); if (!(model17 == null ? void 0 : model17["executor"]) || !((_a2 = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a2[0].shape)) return null; - const t6 = {}; - t6.resize = uq.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t6.norm = Ge(t6.resize, constants.tf255); - t6.res = model17.execute(t6.norm); - t6.squeeze = Up(t6.res, [0]); - [t6.bgRaw, t6.fgRaw] = so(t6.squeeze, 2); - t6.fg = y1(t6.fgRaw); - t6.mul = ae(t6.fg, constants.tf255); - t6.expand = Fa(t6.mul, 2); - t6.output = uq.resizeBilinear(t6.expand, [input.shape[1] || 0, input.shape[2] || 0]); + const t10 = {}; + t10.resize = eK.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t10.norm = He(t10.resize, constants.tf255); + t10.res = model17.execute(t10.norm); + t10.squeeze = Up(t10.res, [0]); + [t10.bgRaw, t10.fgRaw] = so(t10.squeeze, 2); + t10.fg = Jk(t10.fgRaw); + t10.mul = ne(t10.fg, constants.tf255); + t10.expand = Ra(t10.mul, 2); + t10.output = eK.resizeBilinear(t10.expand, [input.shape[1] || 0, input.shape[2] || 0]); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t6.input = Up(input); - t6.concat = gt([t6.input, t6.output], -1); - rgba = Ke(t6.concat, "int32"); + t10.input = Up(input); + t10.concat = xt([t10.input, t10.output], -1); + rgba = je(t10.concat, "int32"); break; case "alpha": - rgba = Ke(t6.output, "int32"); + rgba = je(t10.output, "int32"); break; default: rgba = nr(0); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return rgba; } @@ -43592,7 +43891,7 @@ function jitter(keypoints) { } function padInput(input, inputSize10) { var _a2, _b2; - const t6 = {}; + const t10 = {}; if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b2 = input == null ? void 0 : input.shape) == null ? void 0 : _b2[2])) return input; cache5.padding = [ @@ -43601,10 +43900,10 @@ function padInput(input, inputSize10) { [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], [0, 0] ]; - t6.pad = Hs(input, cache5.padding); - t6.resize = uq.resizeBilinear(t6.pad, [inputSize10, inputSize10]); - const final = Ke(t6.resize, "int32"); - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + t10.pad = Ks(input, cache5.padding); + t10.resize = eK.resizeBilinear(t10.pad, [inputSize10, inputSize10]); + const final = je(t10.resize, "int32"); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return final; } function rescaleBody(body4, outputSize2) { @@ -43740,18 +44039,18 @@ async function predict17(input, config3) { return cache6.bodies; } return new Promise(async (resolve) => { - const t6 = {}; + const t10 = {}; skipped14 = 0; - t6.input = padInput(input, inputSize8); - t6.res = model18 == null ? void 0 : model18.execute(t6.input); + t10.input = padInput(input, inputSize8); + t10.res = model18 == null ? void 0 : model18.execute(t10.input); cache6.last = now(); - const res = await t6.res.array(); - cache6.bodies = t6.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + const res = await t10.res.array(); + cache6.bodies = t10.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); for (const body4 of cache6.bodies) { rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); jitter(body4.keypoints); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); resolve(cache6.bodies); }); } @@ -43782,25 +44081,25 @@ async function process4(res, outputShape, config3) { const scoresT = Up(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); const scores = await scoresT.array(); const featuresT = Up(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = z(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]); - const boxIdxT = bv(boxesMaxT, 2); + const boxesMaxT = W(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]); + const boxIdxT = X0(boxesMaxT, 2); const boxIdx = await boxIdxT.array(); for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b2 = scoresT.shape) == null ? void 0 : _b2[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { + for (let j10 = 0; j10 < (((_b2 = scoresT.shape) == null ? void 0 : _b2[1]) || 0); j10++) { + const score = scores[i][j10]; + if (score > (config3.object.minConfidence || 0) && j10 !== 61) { const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize; const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y10] = [ + const [x, y6] = [ cx2 - scaleBox / strideSize * boxOffset[0], cy - scaleBox / strideSize * boxOffset[1] ]; const [w, h] = [ cx2 + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y10 + cy + scaleBox / strideSize * boxOffset[3] - y6 ]; - let boxRaw = [x, y10, w, h]; + let boxRaw = [x, y6, w, h]; boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); const box = [ boxRaw[0] * outputShape[0], @@ -43811,8 +44110,8 @@ async function process4(res, outputShape, config3) { const result = { id: id2++, score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, + class: j10 + 1, + label: labels2[j10].label, box: box.map((a) => Math.trunc(a)), boxRaw }; @@ -43820,15 +44119,15 @@ async function process4(res, outputShape, config3) { } } } - Dt([scoresT, featuresT, boxesMaxT, boxIdxT]); + Ot([scoresT, featuresT, boxesMaxT, boxIdxT]); } const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); const nmsScores = results.map((a) => a.score); let nmsIdx = []; if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await uq.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + const nms = await eK.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); nmsIdx = Array.from(await nms.data()); - Dt(nms); + Ot(nms); } results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); return results; @@ -43847,16 +44146,16 @@ async function predict18(image, config3) { return last10; return new Promise(async (resolve) => { const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0]; - const resizeT = uq.resizeBilinear(image, [inputSize9, inputSize9], false); - const normT = Ge(resizeT, constants.tf255); - const transposeT = Mp(normT, [0, 3, 1, 2]); + const resizeT = eK.resizeBilinear(image, [inputSize9, inputSize9], false); + const normT = He(resizeT, constants.tf255); + const transposeT = Gp(normT, [0, 3, 1, 2]); let objectT; if (config3.object.enabled) objectT = model19.execute(transposeT); lastTime15 = now(); const obj = await process4(objectT, outputSize2, config3); last10 = obj; - Dt([resizeT, normT, transposeT, ...objectT]); + Ot([resizeT, normT, transposeT, ...objectT]); resolve(obj); }); } @@ -43920,11 +44219,11 @@ var poseChain = [ ["rightKnee", "rightAnkle"] ]; function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y10 } }) => ({ + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y6 } }) => ({ maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y10), + maxY: Math.max(maxY, y6), minX: Math.min(minX, x), - minY: Math.min(minY, y10) + minY: Math.min(minY, y6) }), { maxX: Number.NEGATIVE_INFINITY, maxY: Number.NEGATIVE_INFINITY, @@ -43992,39 +44291,39 @@ var MaxHeap = class { } sink(k) { while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) - j++; - if (!this.less(k, j)) + let j10 = 2 * k; + if (j10 < this.numberOfElements && this.less(j10, j10 + 1)) + j10++; + if (!this.less(k, j10)) break; - this.exchange(k, j); - k = j; + this.exchange(k, j10); + k = j10; } } getValueAt(i) { return this.getElementValue(this.priorityQueue[i]); } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); + less(i, j10) { + return this.getValueAt(i) < this.getValueAt(j10); } - exchange(i, j) { - const t6 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t6; + exchange(i, j10) { + const t10 = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j10]; + this.priorityQueue[j10] = t10; } }; -function getOffsetPoint(y10, x, keypoint, offsets) { +function getOffsetPoint(y6, x, keypoint, offsets) { return { - y: offsets.get(y10, x, keypoint), - x: offsets.get(y10, x, keypoint + count) + y: offsets.get(y6, x, keypoint), + x: offsets.get(y6, x, keypoint + count) }; } function getImageCoords(part, outputStride2, offsets) { const { heatmapY, heatmapX, id: keypoint } = part; - const { y: y10, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + const { y: y6, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); return { x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y10 + y: part.heatmapY * outputStride2 + y6 }; } function clamp(a, min, max) { @@ -44139,13 +44438,13 @@ function buildPartWithScoreQueue(minConfidence2, scores) { } return queue; } -function withinRadius(poses, { x, y: y10 }, keypointId) { +function withinRadius(poses, { x, y: y6 }, keypointId) { return poses.some(({ keypoints }) => { var _a2; const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position; if (!correspondingKeypoint) return false; - return squaredDistance(y10, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + return squaredDistance(y6, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; }); } function getInstanceScore(existingPoses, keypoints) { @@ -44179,16 +44478,16 @@ async function predict19(input, config3) { const res = Ee(() => { if (!model20.inputs[0].shape) return []; - const resized = uq.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = Ne(Ge(Ke(resized, "float32"), 127.5), 1); + const resized = eK.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = ke(He(je(resized, "float32"), 127.5), 1); const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y10) => Up(y10, [0])); - results3d[1] = zs(results3d[1]); + const results3d = results.map((y6) => Up(y6, [0])); + results3d[1] = Ws(results3d[1]); return results3d; }); const buffers = await Promise.all(res.map((tensor) => tensor.buffer())); - for (const t6 of res) - Dt(t6); + for (const t10 of res) + Ot(t10); const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); if (!model20.inputs[0].shape) return []; @@ -44209,7 +44508,7 @@ var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; var t = {}; var ratio = 0; function init3(config3) { - Dt([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + Ot([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); t.r1i = nr(0); t.r2i = nr(0); t.r3i = nr(0); @@ -44227,32 +44526,32 @@ async function load20(config3) { } var normalize = (r) => Ee(() => { const squeeze = Up(r, [0]); - const mul = ae(squeeze, constants.tf255); - const cast = Ke(mul, "int32"); + const mul = ne(squeeze, constants.tf255); + const cast = je(mul, "int32"); return cast; }); function getRGBA(fgr, pha) { - const rgb2 = fgr ? normalize(fgr) : Ws([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : Ws([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = gt([rgb2, a], -1); - Dt([rgb2, a]); + const rgb2 = fgr ? normalize(fgr) : Us([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : Us([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = xt([rgb2, a], -1); + Ot([rgb2, a]); return rgba; } function getState(state) { return Ee(() => { const r = {}; r.unstack = so(state, -1); - r.concat = gt(r.unstack, 1); + r.concat = xt(r.unstack, 1); r.split = Oa(r.concat, 4, 1); - r.stack = gt(r.split, 2); + r.stack = xt(r.split, 2); r.squeeze = Up(r.stack, [0]); - r.expand = Fa(r.squeeze, -1); - r.add = xe(r.expand, 1); - r.mul = ae(r.add, 127.5); - r.cast = Ke(r.mul, "int32"); - r.tile = ki(r.cast, [1, 1, 3]); - r.alpha = Ws([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return gt([r.tile, r.alpha], -1); + r.expand = Ra(r.squeeze, -1); + r.add = be(r.expand, 1); + r.mul = ne(r.add, 127.5); + r.cast = je(r.mul, "int32"); + r.tile = Ai(r.cast, [1, 1, 3]); + r.alpha = Us([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return xt([r.tile, r.alpha], -1); }); } async function predict20(input, config3) { @@ -44260,7 +44559,7 @@ async function predict20(input, config3) { model21 = await load20(config3); if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = Ge(input, 255); + t.src = He(input, 255); if (ratio !== config3.segmentation.ratio) init3(config3); const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); @@ -44281,7 +44580,7 @@ async function predict20(input, config3) { default: rgba = nr(0); } - Dt([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + Ot([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; return rgba; } @@ -44301,27 +44600,27 @@ async function predict21(input, config3) { model22 = await load21(config3); if (!(model22 == null ? void 0 : model22["executor"]) || !((_a2 = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a2[0].shape)) return null; - const t6 = {}; - t6.resize = uq.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t6.norm = Ge(t6.resize, constants.tf255); - t6.res = model22.execute(t6.norm); - t6.squeeze = Up(t6.res, [0]); - t6.alpha = uq.resizeBilinear(t6.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t6.mul = ae(t6.alpha, constants.tf255); + const t10 = {}; + t10.resize = eK.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t10.norm = He(t10.resize, constants.tf255); + t10.res = model22.execute(t10.norm); + t10.squeeze = Up(t10.res, [0]); + t10.alpha = eK.resizeBilinear(t10.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t10.mul = ne(t10.alpha, constants.tf255); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t6.input = Up(input); - t6.concat = gt([t6.input, t6.mul], -1); - rgba = Ke(t6.concat, "int32"); + t10.input = Up(input); + t10.concat = xt([t10.input, t10.mul], -1); + rgba = je(t10.concat, "int32"); break; case "alpha": - rgba = Ke(t6.mul, "int32"); + rgba = je(t10.mul, "int32"); break; default: rgba = nr(0); } - Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); + Object.keys(t10).forEach((tensor) => Ot(t10[tensor])); return rgba; } @@ -44340,18 +44639,18 @@ function validateModel(instance, model23, name) { const executor = model23["executor"]; if ((_b2 = executor == null ? void 0 : executor.graph) == null ? void 0 : _b2.nodes) { for (const kernel of Object.values(executor.graph.nodes)) { - const op2 = kernel.op.toLowerCase(); - if (!ops.includes(op2)) - ops.push(op2); + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); } } else { if (!executor && instance.config.debug) { log("model not loaded", name); } } - for (const op2 of ops) { - if (!simpleOps.includes(op2) && !ignoreOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) { - missing.push(op2); + for (const op of ops) { + if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); } } if (instance.config.debug && missing.length > 0) @@ -44390,7 +44689,7 @@ var Models = class { this.models[model23] = null; } async load(instance) { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m2, _n2, _o2, _p, _q2, _r2, _s2, _t, _u2, _v2, _w2, _x, _y, _z2, _A2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2, _A2; if (env.initial) this.reset(); if (instance) @@ -44401,24 +44700,24 @@ var Models = class { m.liveness = this.instance.config.face.enabled && ((_b2 = this.instance.config.face.liveness) == null ? void 0 : _b2.enabled) && !this.models.liveness ? load9(this.instance.config) : null; m.faceres = this.instance.config.face.enabled && ((_c2 = this.instance.config.face.description) == null ? void 0 : _c2.enabled) && !this.models.faceres ? load7(this.instance.config) : null; m.emotion = this.instance.config.face.enabled && ((_d2 = this.instance.config.face.emotion) == null ? void 0 : _d2.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e2 = this.instance.config.face.iris) == null ? void 0 : _e2.enabled) && !((_f2 = this.instance.config.face.attention) == null ? void 0 : _f2.enabled) && !this.models.iris ? load4(this.instance.config) : null; + m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f2 = this.instance.config.face.attention) == null ? void 0 : _f2.enabled) && !this.models.iris ? load4(this.instance.config) : null; m.facemesh = this.instance.config.face.enabled && ((_g2 = this.instance.config.face.mesh) == null ? void 0 : _g2.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; m.gear = this.instance.config.face.enabled && ((_h2 = this.instance.config.face["gear"]) == null ? void 0 : _h2.enabled) && !this.models.gear ? load10(this.instance.config) : null; m.ssrnetage = this.instance.config.face.enabled && ((_i2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _i2.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; m.ssrnetgender = this.instance.config.face.enabled && ((_j2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k2 = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k2.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; + m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; m.insightface = this.instance.config.face.enabled && ((_l2 = this.instance.config.face["insightface"]) == null ? void 0 : _l2.enabled) && !this.models.insightface ? load14(this.instance.config) : null; m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m2 = this.instance.config.body.modelPath) == null ? void 0 : _m2.includes("blazepose")) ? loadPose(this.instance.config) : null; m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n2 = this.instance.config.body.modelPath) == null ? void 0 : _n2.includes("efficientpose")) ? load2(this.instance.config) : null; m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load19(this.instance.config) : null; m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q2 = this.instance.config.hand.detector) == null ? void 0 : _q2.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect2(this.instance.config) : null; m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; if ((_v2 = (_u2 = this.instance.config.hand.detector) == null ? void 0 : _u2.modelPath) == null ? void 0 : _v2.includes("handdetect")) [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w2 = this.instance.config.object.modelPath) == null ? void 0 : _w2.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x2 = this.instance.config.object.modelPath) == null ? void 0 : _x2.includes("nanodet")) ? load18(this.instance.config) : null; m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _z2.includes("meet")) ? load16(this.instance.config) : null; m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _A2.includes("rvm")) ? load20(this.instance.config) : null; @@ -44463,7 +44762,7 @@ var Models = class { // src/util/persons.ts function join2(faces, bodies, hands, gestures, shape) { - var _a2, _b2, _c2, _d2, _e2, _f2; + var _a2, _b2, _c2, _d2, _e, _f2; let id2 = 0; const persons = []; for (const face4 of faces) { @@ -44498,20 +44797,20 @@ function join2(faces, bodies, hands, gestures, shape) { person2.gestures.push(gesture2); } const x = []; - const y10 = []; + const y6 = []; const extractXY = (box) => { if (box && box.length === 4) { x.push(box[0], box[0] + box[2]); - y10.push(box[1], box[1] + box[3]); + y6.push(box[1], box[1] + box[3]); } }; extractXY(person2.face.box); extractXY((_d2 = person2.body) == null ? void 0 : _d2.box); - extractXY((_e2 = person2.hands.left) == null ? void 0 : _e2.box); + extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); extractXY((_f2 = person2.hands.right) == null ? void 0 : _f2.box); const minX = Math.min(...x); - const minY = Math.min(...y10); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y10) - minY]; + const minY = Math.min(...y6); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y6) - minY]; if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; persons.push(person2); @@ -45316,9 +45615,9 @@ async function warmupNode(instance) { else img = atob2(body3); let res; - if ("node" in tfjs_esm_exports && Aie() === "tensorflow") { + if ("node" in tfjs_esm_exports && Rie() === "tensorflow") { const data = (void 0).decodeJpeg(img); - const expanded = Fa(data, 0); + const expanded = Ra(data, 0); instance.tf.dispose(data); res = await instance.detect(expanded, instance.config); instance.tf.dispose(expanded); @@ -45342,13 +45641,13 @@ async function runCompile(instance) { var _a2, _b2, _c2, _d2; if (!O().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = Aie(); - const webGLBackend = Oie(); + const backendType = Rie(); + const webGLBackend = Pie(); if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { return; } O().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = cr().state.numTensors; + const numTensorsStart = sr().state.numTensors; const compiledModels = []; for (const [modelName, model23] of Object.entries(instance.models.models)) { if (!model23) @@ -45359,26 +45658,26 @@ async function runCompile(instance) { if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; } - const tensor = Vr(shape, dtype); + const tensor = Br(shape, dtype); try { const res = model23.execute(tensor); compiledModels.push(modelName); if (Array.isArray(res)) - res.forEach((t6) => Dt(t6)); + res.forEach((t10) => Ot(t10)); else - Dt(res); + Ot(res); } catch (e) { if (instance.config.debug) log("compile fail model:", modelName); } - Dt(tensor); + Ot(tensor); } const kernels = await webGLBackend["checkCompileCompletionAsync"](); webGLBackend["getUniformLocations"](); if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); O().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = cr().state.numTensors; + const numTensorsEnd = sr().state.numTensors; if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); } @@ -45455,7 +45754,7 @@ var Human = class { this.events.dispatchEvent(new Event(event)); }); __privateAdd(this, _loops, {}); - const tfVersion = (gne.tfjs || xW).replace(/-(.*)/, ""); + const tfVersion = (tse.tfjs || RK).replace(/-(.*)/, ""); config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; config.modelBasePath = env.browser ? "../models/" : "file://models/"; this.version = version; @@ -45527,7 +45826,7 @@ var Human = class { tensor = await predict16(processed.tensor, this.config); if ((_c2 = this.config.segmentation.modelPath) == null ? void 0 : _c2.includes("selfie")) tensor = await predict21(processed.tensor, this.config); - Dt(processed.tensor); + Ot(processed.tensor); return tensor; } compare(firstImageTensor, secondImageTensor) { @@ -45547,7 +45846,7 @@ var Human = class { if (this.env.initial) { if (!await check(this, false)) log("error: backend check failed"); - await $ie(); + await Aie(); if (this.env.browser) { if (this.config.debug) log("configuration:", this.config); @@ -45603,7 +45902,7 @@ var Human = class { async detect(input, userConfig) { this.state = "detect"; return new Promise(async (resolve) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m2, _n2, _o2, _p, _q2, _r2, _s2, _t, _u2; + var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2; this.state = "config"; let timeStamp; this.config = mergeDeep(this.config, userConfig); @@ -45674,7 +45973,7 @@ var Human = class { delete this.performance.body; } else { timeStamp = now(); - if ((_e2 = this.config.body.modelPath) == null ? void 0 : _e2.includes("posenet")) + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; else if ((_f2 = this.config.body.modelPath) == null ? void 0 : _f2.includes("blazepose")) bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; @@ -45691,7 +45990,7 @@ var Human = class { if (this.config.async) { if ((_j2 = (_i2 = this.config.hand.detector) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l2 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("handtrack")) + else if ((_l2 = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l2.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; if (this.performance.hand) delete this.performance.hand; @@ -45699,7 +45998,7 @@ var Human = class { timeStamp = now(); if ((_n2 = (_m2 = this.config.hand.detector) == null ? void 0 : _m2.modelPath) == null ? void 0 : _n2.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p.includes("handtrack")) + else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } @@ -45753,7 +46052,7 @@ var Human = class { return join2(faceRes, bodyRes, handRes, gestureRes, shape); } }; - Dt(img.tensor); + Ot(img.tensor); this.emit("detect"); this.state = "idle"; resolve(this.result); diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index b930ba68..c902971e 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../src/tfjs/load.ts", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", 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rd=N({batchToSpaceND_:XW});function $v(r){let e;return r.rank===0||r.rank===1?e=z(r,[1,1,1,r.size]):r.rank===2?e=z(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function YW(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(i.rank===p.rank,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),E(c==null||i.rank===c.rank,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),E(u==null||i.rank===u.rank,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let m={x:$v(a),scale:u,offset:c,mean:i,variance:p},d={varianceEpsilon:s},f=T.runKernel(an,m,d);return z(f,a.shape)}var wi=N({batchNorm_:YW});function QW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Av=N({batchNorm2d_:QW});function ZW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Rv=N({batchNorm3d_:ZW});function JW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Fv=N({batchNorm4d_:JW});function eU(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Ja,s,a)}var od=N({bincount_:eU});function tU(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(up,n)}var Dv=N({broadcastArgs_:tU});function rU(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(yt(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Br(t);let i={x:t},p={reps:s};return T.runKernel(to,i,p)}var Ii=N({broadcastTo_:rU});function oU(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(Uo,t)}var Ov=N({ceil_:oU});function Ws(r,e,t){yt(r);let o={shape:r,value:e,dtype:t};return T.runKernel(Cs,{},o)}function nU(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return Ws(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(lo,n,s)}var Pv=N({clipByValue_:nU});function sU(r){return gt(r,0)}var Mv=N({concat1d_:sU});function aU(r,e){return gt(r,e)}var Lv=N({concat2d_:aU});function iU(r,e){return gt(r,e)}var Bv=N({concat3d_:iU});function uU(r,e){return gt(r,e)}var Vv=N({concat4d_:uU});function pU(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Pt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");E(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ti,a,i)}var Yv=N({denseBincount_:CU});function SU(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${n} and ${e} for depthToSpace with input shape\n 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m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(Zo,m,d);return c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Bp=N({depthwiseConv2d_:wU});function IU(r){let t={x:v(r,\"x\",\"diag\")};return T.runKernel(hp,t)}var Zv=N({diag_:IU});function vU(r,e,t,o,n=[1,1],s=\"NHWC\"){let a=v(r,\"x\",\"dilation2d\"),i=v(e,\"filter\",\"dilation2d\");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=z(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:p,filter:i},l={strides:t,pad:o,dilations:n},m=T.runKernel(gp,c,l);return u?z(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Jv=N({dilation2d_:vU});function kU(r,e){let t=v(r,\"a\",\"equal\",\"string_or_numeric\"),o=v(e,\"b\",\"equal\",\"string_or_numeric\");[t,o]=Re(t,o),Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(tn,n)}var sd=N({equal_:kU});function NU(r,e,t){let o=v(e,\"a\",\"where\"),n=v(t,\"b\",\"where\"),s=v(r,\"condition\",\"where\",\"bool\"),a=Je(Je(s.shape,o.shape),n.shape),i=Ii(s,a),p=Ii(o,a),u=Ii(n,a),c={condition:i,t:p,e:u};return T.runKernel(Ts,c)}var os=N({where_:NU});function TU(r){let t={x:v(r,\"x\",\"zerosLike\")};return T.runKernel(Fs,t)}var Ut=N({zerosLike_:TU});function _U(r,e){let t=v(r,\"a\",\"div\"),o=v(e,\"b\",\"div\");[t,o]=Re(t,o);let n=Ge(t,o),s=Ut(n),a=sd(o,s);return os(a,s,n)}var ek=N({divNoNan_:_U});function EU(r,e){let t=v(r,\"t1\",\"dot\"),o=v(e,\"t2\",\"dot\");E((t.rank===1||t.rank===2)&&(o.rank===1||o.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${o.rank}.`);let n=t.rank===1?t.size:t.shape[1],s=o.rank===1?o.size:o.shape[0];if(E(n===s,()=>`Error in dot: inner 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i=r;r.rank===3&&(i=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let p=e;p.rank===3&&(p=z(e,[1,e.shape[0],e.shape[1],e.shape[2]])),E(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),E(p.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`),E(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s===\"NHWC\"?i.shape[3]:i.shape[1],c=s===\"NHWC\"?p.shape[3]:p.shape[1];E(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t[2]}.`),E(c===t[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t[3]}).`),Pt(\"conv2dDerFilter\",n,a);let l={x:i,dy:p},m={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,filterShape:t};return T.runKernel(cp,l,m)}var R1=N({conv2DBackpropFilter_:pH});function gu(r,e,t){if(t==null||t===\"linear\")return r;if(t===\"relu\")return ae(r,Od(e));throw new Error(`Cannot compute 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OH(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeNearestNeighbor\");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype===\"float32\"||n.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),E(o===!1||t===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=z(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(Pn,i,p);return a?z(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var J1=N({resizeNearestNeighbor_:OH});function PH(r,e=\"binary\",t=!1,o=.5){let n=v(r,\"image\",\"threshold\"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=ae(mr([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Jn,p,u)}var tN=N({transform_:LH});function BH(r,e,t){E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=z(Ni(0,s,1,\"int32\"),[-1,1]),p=Ni(0,a,1,\"int32\"),u=Ne(i,p),c=lu(Vp(u,be(+e,\"int32\")),cd(u,be(-t,\"int32\"))),l=Vr([s,a],o.dtype);return z(Sr(so(z(o,[-1,s,a])).map(m=>os(c,m,l))),n)}var rN=N({bandPart_:BH});function VH(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must 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aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:be(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className=\"Adadelta\";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className=\"Adagrad\";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className=\"Adam\";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className=\"Adamax\";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qs.className=\"SGD\";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className=\"Momentum\";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Di.className=\"RMSProp\";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static adagrad(e,t=.1){return new $i(e,t)}};var hMe={sgd:ns.sgd,momentum:ns.momentum,adadelta:ns.adadelta,adagrad:ns.adagrad,rmsprop:ns.rmsprop,adamax:ns.adamax,adam:ns.adam};var dq=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:r=>r())();function CC(){return new Promise(r=>dq(()=>r()))}var S={};Ue(S,{ERF_A1:()=>$q,ERF_A2:()=>Aq,ERF_A3:()=>Rq,ERF_A4:()=>Fq,ERF_A5:()=>Dq,ERF_P:()=>Eq,PARALLELIZE_THRESHOLD:()=>Ud,RowPartitionType:()=>Ks,SELU_SCALE:()=>_q,SELU_SCALEALPHA:()=>Tq,applyActivation:()=>yu,assertAndGetBroadcastShape:()=>Je,assertAxesAreInnerMostDims:()=>DU,assertParamsConsistent:()=>fq,assignToTypedArray:()=>Vq,axesAreInnerMostDims:()=>uC,calculateShapes:()=>J0,checkEinsumDimSizes:()=>qq,checkPadOnDimRoundingMode:()=>Pt,combineLocations:()=>nk,combineRaggedTensorToTensorShapes:()=>gq,complexWithEvenIndex:()=>Mq,complexWithOddIndex:()=>Lq,computeConv2DInfo:()=>uu,computeConv3DInfo:()=>Nv,computeDefaultPad:()=>iC,computeDilation2DInfo:()=>OW,computeOptimalWindowSize:()=>Cq,computeOutAndReduceShapes:()=>FU,computeOutShape:()=>hq,computePool2DInfo:()=>aC,computePool3DInfo:()=>PW,convertConv2DDataFormat:()=>Tv,decodeEinsumEquation:()=>Gq,eitherStridesOrDilationsAreOne:()=>lr,expandShapeToKeepDim:()=>Aa,exponent:()=>Wq,exponents:()=>zq,fromStringArrayToUint8:()=>dK,fromUint8ToStringArray:()=>mK,getAxesPermutation:()=>OU,getBroadcastDims:()=>X0,getComplexWithIndex:()=>Bq,getEinsumComputePath:()=>Kq,getEinsumPermutation:()=>Hq,getFusedBiasGradient:()=>xu,getFusedDyActivation:()=>gu,getImageCenter:()=>Sq,getInnerMostAxes:()=>MU,getPermuted:()=>Iq,getRaggedRank:()=>yq,getReductionAxes:()=>jm,getReshaped:()=>wq,getReshapedPermuted:()=>vq,getRowPartitionTypesHelper:()=>xq,getSliceBeginCoords:()=>kq,getSliceSize:()=>Nq,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Qq,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zq,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jq,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>rK,getSparseReshapeInputOutputMismatchErrorMessage:()=>nK,getSparseReshapeInputOutputMultipleErrorMessage:()=>oK,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eK,getSparseReshapeNegativeOutputDimErrorMessage:()=>tK,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>uK,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sK,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>aK,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>iK,getUndoAxesPermutation:()=>PU,isIdentityPermutation:()=>jq,log:()=>GV,mergeRealAndImagArrays:()=>Oq,prepareAndValidate:()=>Z0,prepareSplitSize:()=>Yq,segment_util:()=>wC,shouldFuse:()=>bu,slice_util:()=>ut,splitRealAndImagArrays:()=>Pq,tupleValuesAreOne:()=>iu,upcastType:()=>dt,validateDefaultValueShape:()=>bq,validateInput:()=>Qm,validateUpdateShape:()=>tC,warn:()=>Os});function fq(r,e){let t=r[0].length;r.forEach((n,s)=>{E(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),E(e>=0&&e`Error in concat${t}D: axis must be between 0 and ${t-1}.`);let o=r[0];r.forEach((n,s)=>{for(let a=0;a`Error in concat${t}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`)})}function hq(r,e){let t=r[0].slice();for(let o=1;o=0)if(i>=0){if(i!==s)throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n+r}] = ${s} but shape[${n+r}] = ${i}`)}else o[a]=s}return o}function xq(r){let e={FIRST_DIM_SIZE:Ks.FIRST_DIM_SIZE,VALUE_ROWIDS:Ks.VALUE_ROWIDS,ROW_LENGTHS:Ks.ROW_LENGTHS,ROW_SPLITS:Ks.ROW_SPLITS,ROW_LIMITS:Ks.ROW_LIMITS,ROW_STARTS:Ks.ROW_STARTS},t=[];for(let o of r)if(o in e)t.push(e[o]);else break;return t}function yq(r){return r.length===0?0:r[0]===Ks.FIRST_DIM_SIZE?r.length-1:r.length}function bq(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function Cq(r){return r<=Ud?r:sp(r,Math.floor(Math.sqrt(r)))}function Sq(r,e,t){let o=t*(typeof r==\"number\"?r:r[0]),n=e*(typeof r==\"number\"?r:r[1]);return[o,n]}function wq(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function vq(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let 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t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function Xq(r,e){let t=[];for(let o=0;o\"Number of splits must evenly divide the axis.\"),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);E(n<=1,()=>\"There should be only one negative value in split array.\");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}E(r.shape[t]===e.reduce((a,i)=>a+i),()=>\"The sum of sizes must match the size of the axis dimension.\"),o=e}return o}function Qq(r){return`Received SparseTensor with denseShape[0] = 0 but\n indices.shape[0] = ${r}`}function Zq(r,e){return`indices(${r}, 0) is invalid: ${e} < 0`}function Jq(r,e,t){return`indices(${r}, 0) is invalid: ${e} >= ${t}`}function eK(r,e){return`only one output dimension may be -1, not both ${r} and ${e}`}function tK(r,e){return`size ${r} must be non-negative, not ${e}`}function rK(){return\"reshape cannot infer the missing input size for an empty tensor unless all 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=AN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=DN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[be(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ul(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var MN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ul(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function HC(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var BN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=HC(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VN=(r,e,t,o=Ye)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[as(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>as(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[as(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jN=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qC(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>_N(a,i,p));case\"basic_math\":return n(()=>EN(a,i,p));case\"control\":return ON(a,i,p);case\"convolution\":return n(()=>MN(a,i,p));case\"creation\":return n(()=>LN(a,i,p));case\"dynamic\":return BN(a,i,p);case\"evaluation\":return n(()=>VN(a,i,p));case\"image\":return n(()=>UN(a,i,p));case\"graph\":return n(()=>zN(a,i,p));case\"logical\":return n(()=>GN(a,i,p));case\"matrices\":return n(()=>HN(a,i,p));case\"normalization\":return n(()=>qN(a,i,p));case\"ragged\":return n(()=>KN(a,i,p));case\"reduction\":return n(()=>jN(a,i,p));case\"slice_join\":return n(()=>XN(a,i,p));case\"sparse\":return n(()=>YN(a,i,p));case\"spectral\":return n(()=>QN(a,i,p));case\"string\":return n(()=>ZN(a,i,p));case\"transformation\":return n(()=>JN(a,i,p));case\"hash_table\":return WN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var cl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function KC(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((jC(m)||i6(m)||u6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function eT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var n6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],s6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],a6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function jC(r){return n6.indexOf(r.op)>=0}function i6(r){return s6.indexOf(r.op)>=0}function u6(r){return a6.indexOf(r.op)>=0}var Cu=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Cu(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=KC(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return eT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new cl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=vN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new cl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=KC(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=p.filter(C=>!jC(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=ss(l.node.name,o)),n[l.node.name]==null){let d=qC(l.node,n,o,this._resourceManager);m||([m]=ss(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=ss(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var p6=\"?tfjs-format=file\",c6=\"model.json\",ll=class{constructor(e,t={},o=Ea){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cu(pl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=pl.Instance.transformGraph(e.modelInitializer);this.initializer=new Cu(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Dt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function l6(r,e={},t=Ea){if(r==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");e==null&&(e={}),e.fromTFHub&&typeof r==\"string\"&&(r=d6(r));let o=new ll(r,e,t);return await o.load(),o}function m6(r){if(r==null)throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model\");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error(\"modelJSON must be the first element of the array\");if(!n||!(n instanceof ArrayBuffer))throw new Error(\"An ArrayBuffer of weights must be the second element of the array\");if(!(\"modelTopology\"in o))throw new Error(\"Model JSON is missing 'modelTopology'\");if(!(\"weightsManifest\"in o))throw new Error(\"Model JSON is missing 'weightsManifest'\");let s=Ea.getWeightSpecs(o.weightsManifest),a=Ea.getModelArtifactsForJSONSync(o,s,n);e=Ea.fromMemorySync(a)}else if(\"load\"in r)e=r;else if(\"modelTopology\"in r&&\"weightSpecs\"in r&&\"weightData\"in r)e=Ea.fromMemorySync(r);else throw new Error(\"Unknown model format\");let t=new ll(e);return t.load(),t}function d6(r){return r.endsWith(\"/\")||(r=r+\"/\"),`${r}${c6}${p6}`}var f6=\"4.1.0\";function K(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var h6=Lt.whereImpl,Oi=class extends Zr{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Do(this,cr())}nextDataId(){return Oi.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,O().get(\"IS_NODE\")&&S.warn(`\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================`));let n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:o,refCount:1}),n}makeTensorInfo(e,t,o){let n;if(t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return{dataId:n,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,o,n,s){this.data.set(e,{values:t,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:o}=this.data.get(e);if(t===\"complex64\"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return S.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(t,o,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:o}=this.data.get(e);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}memory(){return{unreliable:!0,reasons:[\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]}}where(e){K([e],\"where\");let t=this.readSync(e.dataId);return h6(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Oi.nextDataId=0;var Qp={};Ue(Qp,{addImpl:()=>QC,bincountImpl:()=>Kp,bincountReduceImpl:()=>uf,castImpl:()=>YC,ceilImpl:()=>ZC,concatImpl:()=>Su,equalImpl:()=>JC,expImpl:()=>tS,expm1Impl:()=>oS,floorImpl:()=>nS,gatherNdImpl:()=>pf,gatherV2Impl:()=>cf,greaterEqualImpl:()=>aS,greaterImpl:()=>sS,lessEqualImpl:()=>uS,lessImpl:()=>iS,linSpaceImpl:()=>lf,logImpl:()=>pS,maxImpl:()=>mf,maximumImpl:()=>cS,minimumImpl:()=>lS,multiplyImpl:()=>ml,negImpl:()=>mS,notEqualImpl:()=>dS,prodImpl:()=>fS,raggedGatherImpl:()=>df,raggedRangeImpl:()=>ff,raggedTensorToTensorImpl:()=>hf,rangeImpl:()=>Iu,rsqrtImpl:()=>hS,scatterImpl:()=>Ma,sigmoidImpl:()=>ET,simpleAbsImpl:()=>XC,sliceImpl:()=>vu,sparseFillEmptyRowsImpl:()=>gf,sparseReshapeImpl:()=>xf,sparseSegmentReductionImpl:()=>Yp,sqrtImpl:()=>RT,squaredDifferenceImpl:()=>xS,stridedSliceImpl:()=>yf,stringNGramsImpl:()=>ku,stringSplitImpl:()=>Nu,stringToHashBucketFastImpl:()=>Tu,subImpl:()=>bS,tileImpl:()=>bf,topKImpl:()=>Cf,transposeImpl:()=>jp,uniqueImpl:()=>Sf});function 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uj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;K([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,w=x-1-c.padInfo.top,k=le(a.shape,\"float32\"),_=1/(d*f),$=t.data.get(n.dataId).values,A=le(n.shape,\"float32\",$);for(let R=0;R=c.outHeight||Math.floor(q)!==q))for(let H=0;H=c.outWidth||Math.floor(j)!==j)continue;let X=A.get(R,q,j,D);V+=X}}k.set(V*_,R,P,M,D)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var i2={kernelName:wm,backendName:\"cpu\",kernelFunc:uj};function pj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||i.shape.length===a.shape.length,()=>\"Batch normalization 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i=s.reduce((x,b)=>x*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Me({inputs:{x:n},backend:t,attrs:{shape:p}}),f=Ct({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Me({inputs:{x:f},backend:t,attrs:{shape:c}}),g=No({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var p2={kernelName:xs,backendName:\"cpu\",kernelFunc:cj};function lj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Kp(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var c2={kernelName:Ja,backendName:\"cpu\",kernelFunc:lj};function mj(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var l2={kernelName:up,backendName:\"cpu\",kernelFunc:mj};var dj=Ie(lo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return ar({inputs:{x:p[0]},backend:t});if(p[0].dtype===\"complex64\"){let h=p.map(w=>wo({inputs:{input:w},backend:t})),g=p.map(w=>Xs({inputs:{input:w},backend:t})),x=Pi({inputs:h,backend:t,attrs:{axis:s}}),b=Pi({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(w=>t.disposeIntermediateTensorInfo(w)),g.forEach(w=>t.disposeIntermediateTensorInfo(w)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Me({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=Su(c,i,e[0].dtype,l),d=S.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var h2={kernelName:ys,backendName:\"cpu\",kernelFunc:Pi};function NS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;K([n,s],\"conv2d\");let l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",w=new st(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],A=C?k[1]:k[2],R=C?k[2]:1,D=C?1:k[1],P=w.strides[0],M=C?w.strides[1]:w.strides[2],L=C?w.strides[2]:1,W=C?1:w.strides[1],V=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,q=w.values;for(let H=0;H=m.inHeight)continue;let pe=J*_[0],he=j+ie*A;for(let we=0;we=m.inWidth)continue;let pt=pe+Le*_[1],Oe=he+nt*R,mt=pt;for(let at=0;at=u.inDepth)continue;let H=U*R[0],j=P+q*A[1];for(let X=0;X=u.inHeight)continue;let ie=H+Y*R[1],pe=j+J*A[2];for(let he=0;he=u.inWidth)continue;let nt=ie+$e*R[2],pt=pe+Le*u.inChannels,Oe=nt;for(let mt=0;mtMath.cos(r)),w2={kernelName:qo,backendName:\"cpu\",kernelFunc:Cj};var Sj=Ie(Ko,r=>Math.cosh(r)),I2={kernelName:Ko,backendName:\"cpu\",kernelFunc:Sj};function wj(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=le([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let W=h>1?(P-R)*(l-1)/(h-1):0,V=g>1?(M-D)*(m-1)/(g-1):0;for(let U=0;U1?R*(l-1)+U*W:.5*(R+P)*(l-1);if(q<0||q>l-1){for(let H=0;H1?D*(m-1)+Z*V:.5*(D+M)*(m-1);if(ee<0||ee>m-1){for(let pe=0;pe1?D*(m-1)+H*V:.5*(D+M)*(m-1);if(j<0||j>m-1){for(let ee=0;eex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,w=b.top,k=d.outChannels/d.inChannels,_=new st(d.outShape,n.dtype),$=t.data.get(n.dataId).values,A=t.data.get(s.dataId).values,R=_.values;for(let D=0;D=d.inHeight)continue;let H=U*l[0],j=P+q*c[1];for(let X=0;X=d.inWidth)continue;let ie=H+Y*l[1],pe=j+J*d.inChannels,he=Z,we=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:w,strideWidth:k,filterHeight:_,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:D}=S.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),P=y.sizeFromShape(D),M=D.length,L=y.getArrayFromDType(o.dtype,P);for(let V=0;V=0&&J=0&&peZ&&(Z=ve)}}}let ee=y.locToIndex([V,U,H,X],M,y.computeStrides(D));L[ee]=Z}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),D,o.dtype),shape:D,dtype:o.dtype}}};var D2={kernelName:bb,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===R.length,()=>`Error in ${bb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=ee,Z=J)}}}P[X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var O2={kernelName:yb,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===R.length,()=>`Error in ${yb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=Y,Z=ie)}}}P[L][X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function La(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;K(n,\"sum\");let i;n.dtype===\"bool\"?i=Io({inputs:{x:n},backend:t,attrs:{dtype:\"int32\"}}):i=ar({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=S.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=Ct({inputs:{x:i},backend:t,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,p)),S.assertAxesAreInnerMostDims(\"sum\",l,m.shape.length);let[d,f]=S.computeOutAndReduceShapes(m.shape,l),h=S.upcastType(m.dtype,\"int32\"),g=Hp(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let w=0;w=0&&(m=La({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var M2={kernelName:ri,backendName:\"cpu\",kernelFunc:$j};function Aj(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;K([o,n],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=1?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,\"float32\",s)}var L2={kernelName:km,backendName:\"cpu\",kernelFunc:Aj};var Rj=S.ERF_P,Fj=S.ERF_A1,Dj=S.ERF_A2,Oj=S.ERF_A3,Pj=S.ERF_A4,Mj=S.ERF_A5,Lj=Ie(ma,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+Rj*t);return e*(1-((((Mj*o+Pj)*o+Oj)*o+Dj)*o+Fj)*o*Math.exp(-t*t))}),B2={kernelName:ma,backendName:\"cpu\",kernelFunc:Lj};function Jp(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Me({inputs:{x:n},backend:t,attrs:{shape:i}})}var V2={kernelName:bs,backendName:\"cpu\",kernelFunc:Jp};var Bj=Be((r,e)=>r/e),hl=Qe(Jo,Bj),gl={kernelName:Jo,backendName:\"cpu\",kernelFunc:hl};function vf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&CMath.floor(r/e)),qj=Qe(sn,Hj,null,\"int32\"),G2={kernelName:sn,backendName:\"cpu\",kernelFunc:qj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=NS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;if(c===\"NCHW\"&&a.shape.length===1&&a.shape[0]!==1){let x=Me({inputs:{x:a},backend:t,attrs:{shape:[a.shape[0],1,1]}});h=js({inputs:{a:h,b:x},backend:t}),t.disposeIntermediateTensorInfo(x)}else h=js({inputs:{a:h,b:a},backend:t});t.disposeIntermediateTensorInfo(g)}if(d){let g=h;if(c===\"NCHW\"&&d===\"prelu\"&&i.shape.length===1&&i.shape[0]!==1){let x=Me({inputs:{x:i},backend:t,attrs:{shape:[i.shape[0],1,1]}});h=_u(t,h,d,x,f),t.disposeIntermediateTensorInfo(x)}else h=_u(t,h,d,i,f);t.disposeIntermediateTensorInfo(g)}return h}var H2={kernelName:ho,backendName:\"cpu\",kernelFunc:Kj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=TS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;h=js({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(d){let g=h;h=_u(t,h,d,i,f),t.disposeIntermediateTensorInfo(g)}return h}var q2={kernelName:go,backendName:\"cpu\",kernelFunc:jj};function Xj(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=y.sizeFromShape(o.shape),a=n.shape,i=a[a.length-1],[p,u,c,l]=S.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(p,o.dtype,[]);let m=t.data.get(n.dataId).values,d=t.bufferSync(o),f=pf(m,d,o.dtype,u,i,c,l,o.shape,s);return t.makeTensorInfo(p,o.dtype,f.values)}var K2={kernelName:un,backendName:\"cpu\",kernelFunc:Xj};function Yj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o;K([n,s],\"gatherV2\");let p=y.parseAxisParam(a,n.shape)[0],u=t.data.get(s.dataId).values,c=n.shape[p];for(let w=0;w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=S.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Me({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Me({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=cf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var j2={kernelName:Ss,backendName:\"cpu\",kernelFunc:Yj};function Qj(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Me({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=vf(i,!0,t),u=Me({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var X2={kernelName:ni,backendName:\"cpu\",kernelFunc:Qj};var 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Ce=O();Ce.registerFlag(\"HAS_WEBGL\",()=>Ce.getNumber(\"WEBGL_VERSION\")>0);Ce.registerFlag(\"WEBGL_VERSION\",()=>Ff(2)?2:Ff(1)?1:0);Ce.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Ce.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Ce.get(\"WEBGL_VERSION\")===2);Ce.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Ce.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Ce.registerFlag(\"WEBGL_PACK\",()=>Ce.getBool(\"HAS_WEBGL\"));Ce.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_CLIP\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>XS(Ce.getNumber(\"WEBGL_VERSION\")));Ce.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>YS(Ce.getNumber(\"WEBGL_VERSION\")));Ce.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let 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outputColor;\",i=O().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",p=\"\",u=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",e=\"attribute\",t=\"varying\",o=\"varying\",n=\"texture2D\",s=\"gl_FragColor\",a=\"\",i=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,p=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n 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s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function kE(r,e,t=\"index\"){let o=r.map((s,a)=>a),n=B5(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join(\"\")}function nc(r){let e=y.computeStrides(r).map(t=>t.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;\n }\n`}function sc(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Df=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:NE}=S;function TE(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:\"\"};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 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e.isPacked?(u=z5(e.logicalShape,a,t.enableShapeUniforms),c=H5(i)):(u=W5(e.logicalShape,a,t.enableShapeUniforms),c=G5(i)),t.packedInputs&&(l+=Y5),[l,p,c,n,u,s,t.userCode].join(`\n`)}function ic(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return u8(r,e);case 1:return c8(r,e);case 2:return m8(r,e);case 3:return f8(r,e);case 4:return g8(r,e);case 5:return x8(r);case 6:return y8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function _E(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return i8(r);case 1:return p8(r,e);case 2:return l8(r,e);case 3:return d8(r,e);default:return h8(r,e)}}function V5(r,e,t=!1,o){let n=\"\";t?n+=_E(r,o):n+=ic(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=b8(r,e):n+=C8(r,e)),n}function z5(r,e,t){switch(r.length){case 0:return EE();case 1:return Q5(r,e,t);case 2:return s8(r,e,t);case 3:return J5(r,e,t);default:return t8(r,e,t)}}function W5(r,e,t){switch(r.length){case 0:return EE();case 1:return Z5(r,e,t);case 2:return a8(r,e,t);case 3:return e8(r,e,t);case 4:return r8(r,e,t);case 5:return o8(r,e);case 6:return n8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function U5(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function G5(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function H5(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function q5(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${K5}\n ${j5}\n ${X5}\n `}var K5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,j5=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,X5=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Y5=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function EE(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function Q5(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${o[1]}.0);\n }\n `:o[1]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${o[0]}.0);\n }\n `:t?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${o[0]}, ${o[1]}));\n return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);\n }\n `}function Z5(r,e,t){return e[0]===1?t?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${e[1]}.0);\n }\n `:e[1]===1?t?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${e[0]}.0);\n }\n `:t?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${e[0]}, ${e[1]}));\n return resTexRC.x * ${e[1]} + resTexRC.y;\n }\n `}function J5(r,e,t){if(t)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),s=n*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${o[0]}, ${o[1]}));\n int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${n});\n int c = imod(index, ${n}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function e8(r,e,t){if(t)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${Au([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let o=us([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n 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t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?Au([\"r\",\"c\",\"d\"],e):us([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=`\n ${Df}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var 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t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,RS(t,e)):this.gl=Wr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=ec(this.gl,s),Ur(this.gl,a))this.textureHalfFloatExtension=ec(this.gl,a);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Ur(this.gl,n))this.colorBufferHalfFloatExtension=ec(this.gl,n);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",Ur(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Ur(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=ow(this.gl),this.indexBuffer=nw(this.gl),this.framebuffer=GS(this.gl),this.textureConfig=Sl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),sw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),aw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),iw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),mw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),lw(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),pw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),uw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>hw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return gw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return fw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=dw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>xw(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rw(t));let o=LS(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),BS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(cw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&wl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&wl(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?HS(this.gl,e,t):qS(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),KS(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ys(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&wl(this.gl,this.program),tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ec(this.gl,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=v8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Il(this.gl,e,this.framebuffer),this.debug&&tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Il(this.gl,this.outputTexture,this.framebuffer),this.debug&&tc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Il(n,e,this.framebuffer),this.debug&&tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function v8(r){let e=0;for(;e`${r}.${t}`)}function $t(r,e){return e===1?[r]:bw(r,e)}function I$(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var lc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${k8(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?sc():nc(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function k8(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?kE([\"r\",\"c\",\"d\"],\"inputShape\"):us([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=k$(t,o),s=N$(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=v$(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=k$(o,n),a=N$(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=v$(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function N8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function v$(r,e,t,o,n){let s=T8(e,o),a;if(n){let[p,u]=Ys(r[0],r[1]);a=p*u}else{let[p,u]=$u(r[0],r[1]);a=p*u}let i=N8(t,s);return a*i}function T8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function _8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function k$(r,e){if(r===ir.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ir.RENDER||r==null)return _8(e);if(r===ir.DOWNLOAD||r===ir.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function N$(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Bt=\"if (isnan(x)) return x;\",T$=\"return x;\",Cw=\"return abs(x);\";var _$=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",E$=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,$$=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Qs=\"return x;\",A$=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var F$=\"return x;\",D$=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,O$=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,P$=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,M$=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Ar=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let t=e.length,o=$t(\"rc\",t),n=_e(t),s=I$(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var $8=Lt.whereImpl,A8=1e-7,R8=1e-4,Zf={};function F8(r){return r in Zf||(Zf[r]={}),Zf[r]}var D8=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),O8=600;function P8(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*O8/1024/1024}var Bi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Fu)t=e;else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Fu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"));t=new Fu(o),this.binaryCache=F8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=P8(),this.texData=new Do(this,cr())}nextDataId(){return Bi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=rc(t),c=new vl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ir.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ir.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Qs):m=new Jt(i,Qs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Qs):f=new Jt(n,Qs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Cl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Ar(s,Qs):d=new Jt(s,Qs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=cr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D8){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Va(e.shape),...za(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Va(t),...za(t)],a=new lc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=rc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Cl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Mi.DENSE){let x=a!=null?a:Cl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Li(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=FE(e,c,l),d=this.getAndSaveBinary(m,()=>AE(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||RE(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=jS(o,p),t.texShape=l),s!=null){let m=rc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ys(l[0],l[1])),p?d=new Vf(m,g):d=new vl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ir.PIXELS:C.usage=ir.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=M8(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await CC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?($f(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=tw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromTexture(e,t,o){let{texture:n,height:s,width:a,channels:i}=e,p=cr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return cr().makeTensorFromDataId(u,t,o,p)}};Bi.nextDataId=0;function M8(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Bi,2);var L9e={forceHalfFloat:L$};var mc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Zs=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var To=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ct(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${_e(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=$t(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var B$={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Rr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var V$={kernelName:ei,backendName:\"webgl\",kernelFunc:Rr};var Sw=\"return (a < 0.) ? b * a : a;\",ww=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(ww,n.shape,a.shape):new io(Sw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var z$={kernelName:mn,backendName:\"webgl\",kernelFunc:B8};var Iw=\"return (a < 0.) ? b * a : a;\",vw=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function V8(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(vw,o.shape,n.shape):new io(Iw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var W$={kernelName:Rn,backendName:\"webgl\",kernelFunc:V8};var _o=\"if (isnan(x)) return x;\";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,$],dt(w.dtype,k.dtype))}),b=Rr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new To(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Wa(r,e=!1){if(r===\"linear\")return e?F$:T$;if(r===\"relu\")return e?O$:E$;if(r===\"elu\")return e?D$:_$;if(r===\"relu6\")return e?P$:$$;if(r===\"prelu\")return e?vw:Iw;if(r===\"leakyrelu\")return e?ww:Sw;if(r===\"sigmoid\")return e?M$:A$;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var dc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ct(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Li(n.shape,p)&&!(c.texture!==null&&Li(c.shape,p))?H$(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var q$={kernelName:Ns,backendName:\"webgl\",kernelFunc:te};var _l=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function W8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Gr(r,e,t,o){let n=W8(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=_e(this.rank),s=bw(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),A=te({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,W=p===\"leakyrelu\",V=p!=null?Wa(p,!0):null,U=M||L||W||V!=null,q;if((d===1||f===1)&&P>Nw&&U===!1){let j=$,X=A;t&&(j=xt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),R.push(j)),o&&(X=xt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(X));let Z=f!==1,ee=f===1,Y=j;Z&&(Y=te({inputs:{x:j},backend:n,attrs:{shape:[D,P,1]}}),R.push(Y));let J=f===1?2:1,ie=X;ee&&(ie=te({inputs:{x:X},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let pe=Tl({inputs:{a:Y,b:ie},backend:n});q=Ou({inputs:{x:pe},backend:n,attrs:{axis:J,keepDims:!0}}),R.push(pe)}else{let j=dt(r.dtype,e.dtype),X=new dc(k,_,[D,d,f],t,o,M,V,L,W),Z=[$,A];if(s!=null&&Z.push(s),L&&Z.push(a),W){let ee=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));Z.push(ee),R.push(ee)}q=n.runWebGLProgram(X,Z,j)}let H=te({inputs:{x:q},backend:n,attrs:{shape:w}});R.push(q);for(let j of R)n.disposeIntermediateTensorInfo(j);return H}function G8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Pu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var Y$={kernelName:fo,backendName:\"webgl\",kernelFunc:G8};var Q$=\"return abs(x);\";function H8(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=Kf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Ar(o.shape,Q$):n=new Jt(o.shape,Q$),t.runWebGLProgram(n,[o],o.dtype)}var Z$={kernelName:gs,backendName:\"webgl\",kernelFunc:H8};var q8=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,K8=ge({opSnippet:q8}),J$={kernelName:sa,backendName:\"webgl\",kernelFunc:K8};var j8=Bt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,backendName:\"webgl\",kernelFunc:X8};var tA=\"return a + b;\",Y8=tt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DE}),rA={kernelName:eo,backendName:\"webgl\",kernelFunc:Y8};var rh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var oh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var oA={kernelName:Mo,backendName:\"webgl\",kernelFunc:nh};function Q8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"all\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Lo,backendName:\"webgl\",kernelFunc:Q8};function Z8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"any\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Bo,backendName:\"webgl\",kernelFunc:Z8};var sh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=_e(p),c=$t(\"coords\",p),l,m;if(a===1){m=p+1;let A=_e(m);l=`\n ${A} sourceLocR = ${A}(${c.join()}, 0);\n ++${c[p-1]};\n ${A} sourceLocG = ${A}(${c.join()}, 0);\n ++${c[p-2]};\n ${A} sourceLocA = ${A}(${c.join()}, 0);\n --${c[p-1]};\n ${A} sourceLocB = ${A}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=$t(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=$t(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=$t(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=$t(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,$=n?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${$}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n ${l}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${k}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function aA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=aA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=iA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,e,o)}function J8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Vo,backendName:\"webgl\",kernelFunc:J8};function eY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Za,backendName:\"webgl\",kernelFunc:eY};var tY=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,rY=ge({opSnippet:tY}),cA={kernelName:ia,backendName:\"webgl\",kernelFunc:rY};var oY=Bt+\"return log(x + sqrt(x * x + 1.0));\",nY=ge({opSnippet:oY}),lA={kernelName:ua,backendName:\"webgl\",kernelFunc:nY};var sY=Bt+`\n return atan(x);\n`,aY=ge({opSnippet:sY}),mA={kernelName:pa,backendName:\"webgl\",kernelFunc:aY};var iY=mc+`\n return atan(a, b);\n`,uY=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Zs+`\n return result;\n`,pY=tt({opSnippet:iY,packedOpSnippet:uY}),dA={kernelName:la,backendName:\"webgl\",kernelFunc:pY};var cY=Bt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelName:ca,backendName:\"webgl\",kernelFunc:lY};var ps=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let A=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${A} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(w=\"avgValue / count\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${w});\n }\n `}},zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",w=\"0.0\";if(C||(w=\"-1.0 / 1e-20\"),o){let D=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / count\");let $=Math.floor(a/4)*4,A=a%4,R=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${w};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${w});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${R}\n }\n\n int xC = xCCorner + ${$};\n if (${A===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${R}\n }\n }\n setOutput(${_});\n }\n }\n `}};function mY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;is(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var hA={kernelName:zo,backendName:\"webgl\",kernelFunc:mY};function dY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new zi(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var gA={kernelName:ip,backendName:\"webgl\",kernelFunc:dY};var uh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function fY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Im,backendName:\"webgl\",kernelFunc:fY};function 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offset);\n }\n `}};var xY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:an,backendName:\"webgl\",kernelFunc:xY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let 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So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function RA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function TY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[RA(o,n.complexTensorInfos.real),RA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var FA={kernelName:pp,backendName:\"webgl\",kernelFunc:TY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${yh(i,u,g)}),\n vec2(${yh(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${yh(i,u,f)}),\n vec2(${yh(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Mu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var DA={kernelName:si,backendName:\"webgl\",kernelFunc:Mu};function fc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ua({inputs:{input:b},backend:t})),f=r.map(b=>Mu({inputs:{input:b},backend:t})),h=fc(d,e,t),g=fc(f,e,t),x=Rr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return te({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=LE(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Qs):new Ar(r[0].shape,Qs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=_Y(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function _Y(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function $w(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):fc(p,s,t)}var OA={kernelName:ys,backendName:\"webgl\",kernelFunc:$w};var hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?w=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:w=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${w}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Nw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Li(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let A=Pu({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Pu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",$),R=te({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Wa(i,!0):null,W=new dc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),V=f?[R,k]:[k,R];if(n&&V.push(n),P&&V.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));V.push(H),w.push(H)}let U=o.runWebGLProgram(W,V,\"float32\"),q=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return q}function EY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new gc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new hc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var PA={kernelName:Go,backendName:\"webgl\",kernelFunc:EY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${a}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var MA={kernelName:cp,backendName:\"webgl\",kernelFunc:$Y};function AY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var LA={kernelName:Ho,backendName:\"webgl\",kernelFunc:AY};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var BA={kernelName:lp,backendName:\"webgl\",kernelFunc:RY};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var VA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var zA={kernelName:mp,backendName:\"webgl\",kernelFunc:DY};var OY=_o+`\n return cos(x);\n`,PY=ge({opSnippet:OY}),WA={kernelName:qo,backendName:\"webgl\",kernelFunc:PY};var MY=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,LY=ge({opSnippet:MY}),UA={kernelName:Ko,backendName:\"webgl\",kernelFunc:LY};var Eh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${w};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var BY=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Eh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},GA={kernelName:Yo,backendName:\"webgl\",kernelFunc:BY};var Lu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Lu||(Lu={}));var El=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Lu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${HA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${_e(s)} coords = getOutputCoords();\n int end = ${qA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${qA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${HA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function HA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function qA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function $h(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=xt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new El(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new El(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=xt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function VY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Prod,n,t,s,a,i)}var KA={kernelName:jo,backendName:\"webgl\",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Sum,n,t,s,a,i)}var jA={kernelName:Xo,backendName:\"webgl\",kernelFunc:zY};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=qf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=OE(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ti,backendName:\"webgl\",kernelFunc:WY};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:Qo,backendName:\"webgl\",kernelFunc:UY};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new yc(l):m=new xc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var QA={kernelName:Zo,backendName:\"webgl\",kernelFunc:GY};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Fh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var ZA={kernelName:dp,backendName:\"webgl\",kernelFunc:HY};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Fh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var JA={kernelName:fp,backendName:\"webgl\",kernelFunc:qY};var Dh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function KY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var eR={kernelName:hp,backendName:\"webgl\",kernelFunc:KY};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var tR={kernelName:gp,backendName:\"webgl\",kernelFunc:jY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ou({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var rR={kernelName:ri,backendName:\"webgl\",kernelFunc:XY};var YY=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",QY=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,ZY=ge({opSnippet:YY,packedOpSnippet:QY}),oR={kernelName:en,backendName:\"webgl\",kernelFunc:ZY};var JY=\"return (b >= 1.0) ? a : a * (b + 1.0);\",eQ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,tQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(eQ,o.shape,n.shape):new io(JY,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},nR={kernelName:km,backendName:\"webgl\",kernelFunc:tQ};var rQ=`\n return vec4(equal(a, b));\n`,oQ=\"return float(a == b);\",nQ=tt({opSnippet:oQ,packedOpSnippet:rQ,dtype:\"bool\",cpuKernelImpl:BE}),sR={kernelName:tn,backendName:\"webgl\",kernelFunc:nQ};var sQ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,aQ=ge({opSnippet:sQ}),aR={kernelName:ma,backendName:\"webgl\",kernelFunc:aQ};var iQ=_o+`\n return exp(x);\n`,uQ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aw=ge({opSnippet:iQ,packedOpSnippet:uQ,cpuKernelImpl:VE,dtype:\"float32\"}),iR={kernelName:rn,backendName:\"webgl\",kernelFunc:Aw};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uR={kernelName:bs,backendName:\"webgl\",kernelFunc:Ph};var pR=\"return exp(x) - 1.0;\",pQ=ge({opSnippet:pR,packedOpSnippet:pR,cpuKernelImpl:zE}),cR={kernelName:da,backendName:\"webgl\",kernelFunc:pQ};var $l=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new $l(\"real\",p,e),c=new $l(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Rr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function cQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var lR={kernelName:oi,backendName:\"webgl\",kernelFunc:cQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ga(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var mR={kernelName:Cs,backendName:\"webgl\",kernelFunc:Ga};var Bh=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var dR={kernelName:on,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var fR=\"return floor(x);\",lQ=ge({opSnippet:fR,packedOpSnippet:fR,cpuKernelImpl:WE}),hR={kernelName:nn,backendName:\"webgl\",kernelFunc:lQ};var mQ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,dQ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,fQ=tt({opSnippet:mQ,packedOpSnippet:dQ,dtype:\"int32\"}),gR={kernelName:sn,backendName:\"webgl\",kernelFunc:fQ};var Vh=class{constructor(e){this.variableNames=[\"A\"];let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var zh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var xR={kernelName:Zi,backendName:\"webgl\",kernelFunc:hQ},bc,Rw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(bc==null||h!==Rw)&&(Rw=h,bc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Rw})),bc.canvas.width=p,bc.canvas.height=u,bc.drawImage(n,0,0,p,u),n=bc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ir.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=te({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return A};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Wa(d,!0):null,R=new gc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Wa(d,!1):null,R=new hc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),$}var yR={kernelName:ho,backendName:\"webgl\",kernelFunc:gQ};function xQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=_e(this.rank),n=bQ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function bQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=GE(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var SR={kernelName:Ss,backendName:\"webgl\",kernelFunc:Fw};var CQ=\"return float(a > b);\",SQ=`\n return vec4(greaterThan(a, b));\n`,wQ=tt({opSnippet:CQ,packedOpSnippet:SQ,cpuKernelImpl:HE,dtype:\"bool\"}),wR={kernelName:pn,backendName:\"webgl\",kernelFunc:wQ};var IQ=\"return float(a >= b);\",vQ=`\n return vec4(greaterThanEqual(a, b));\n`,kQ=tt({opSnippet:IQ,packedOpSnippet:vQ,dtype:\"bool\",cpuKernelImpl:qE}),IR={kernelName:cn,backendName:\"webgl\",kernelFunc:kQ};function NQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var vR={kernelName:ni,backendName:\"webgl\",kernelFunc:NQ};var TQ=\"return float(!isnan(x) && !isinf(x));\",_Q=ge({opSnippet:TQ,dtype:\"bool\"}),kR={kernelName:fa,backendName:\"webgl\",kernelFunc:_Q};var EQ=\"return float(isinf(x));\",$Q=ge({opSnippet:EQ,dtype:\"bool\"}),NR={kernelName:ha,backendName:\"webgl\",kernelFunc:$Q};var AQ=\"return float(isnan(x));\",RQ=ge({opSnippet:AQ,dtype:\"bool\"}),TR={kernelName:ln,backendName:\"webgl\",kernelFunc:RQ};var FQ=\"return float(a < b);\",DQ=`\n return vec4(lessThan(a, b));\n`,OQ=tt({opSnippet:FQ,packedOpSnippet:DQ,cpuKernelImpl:KE,dtype:\"bool\"}),_R={kernelName:dn,backendName:\"webgl\",kernelFunc:OQ};var PQ=\"return float(a <= b);\",MQ=`\n return vec4(lessThanEqual(a, b));\n`,LQ=tt({opSnippet:PQ,packedOpSnippet:MQ,cpuKernelImpl:jE,dtype:\"bool\"}),ER={kernelName:fn,backendName:\"webgl\",kernelFunc:LQ};function BQ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=XE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var $R={kernelName:xp,backendName:\"webgl\",kernelFunc:BQ};var VQ=_o+`\n return x < 0.0 ? 0./0. : log(x);\n`,zQ=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,WQ=ge({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:YE}),AR={kernelName:hn,backendName:\"webgl\",kernelFunc:WQ};var UQ=_o+`\n return log(1.0 + x);\n`,GQ=ge({opSnippet:UQ}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:GQ};var HQ=\"return float(a >= 1.0 && b >= 1.0);\",qQ=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,KQ=tt({opSnippet:HQ,packedOpSnippet:qQ,dtype:\"bool\"}),FR={kernelName:gn,backendName:\"webgl\",kernelFunc:KQ};var jQ=\"return float(!(x >= 1.0));\",XQ=ge({opSnippet:jQ}),DR={kernelName:xn,backendName:\"webgl\",kernelFunc:XQ};var YQ=\"return float(a >= 1.0 || b >= 1.0);\",QQ=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,ZQ=tt({opSnippet:YQ,packedOpSnippet:QQ,dtype:\"bool\"}),OR={kernelName:xa,backendName:\"webgl\",kernelFunc:ZQ};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var JQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},PR={kernelName:yp,backendName:\"webgl\",kernelFunc:JQ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var e7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},MR={kernelName:Nm,backendName:\"webgl\",kernelFunc:e7};function LR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Gr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Dw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var zR={kernelName:Cn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new zi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var WR={kernelName:bp,backendName:\"webgl\",kernelFunc:s7};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new zi(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var UR={kernelName:_m,backendName:\"webgl\",kernelFunc:a7};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;is([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new ps(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new Kh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GR={kernelName:Tm,backendName:\"webgl\",kernelFunc:i7};function HR(r,e,t,o){let n=new ps(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new ps(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var qR={kernelName:Cp,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var mF={kernelName:vs,backendName:\"webgl\",kernelFunc:E7};var Jh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=_e(n),a=t.map(u=>u[0]).join(\",\"),i=t.map((u,c)=>u[0]+e[c]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n }\n `}};var eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=_e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=$t(\"rc\",n),u=$t(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ga({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},dF={kernelName:$n,backendName:\"webgl\",kernelFunc:Lw};var $7=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,A7=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=S.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var hF={kernelName:Fn,backendName:\"webgl\",kernelFunc:F7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=n$(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var gF={kernelName:wp,backendName:\"webgl\",kernelFunc:D7};function O7(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=s$(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var xF={kernelName:Ip,backendName:\"webgl\",kernelFunc:O7};function P7(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=a$(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var yF={kernelName:vp,backendName:\"webgl\",kernelFunc:P7};var Bw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=i$(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},bF={kernelName:ks,backendName:\"webgl\",kernelFunc:Bw};var M7=\"return 1.0 / x;\",L7=ge({opSnippet:M7}),CF={kernelName:Dn,backendName:\"webgl\",kernelFunc:L7};var B7=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,V7=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z7=ge({opSnippet:B7,packedOpSnippet:V7}),SF={kernelName:On,backendName:\"webgl\",kernelFunc:z7};var W7=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,U7=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,G7=ge({opSnippet:W7,packedOpSnippet:U7}),wF={kernelName:Ln,backendName:\"webgl\",kernelFunc:G7};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function H7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var IF={kernelName:Mn,backendName:\"webgl\",kernelFunc:H7};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var vF={kernelName:$m,backendName:\"webgl\",kernelFunc:q7};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var kF={kernelName:Pn,backendName:\"webgl\",kernelFunc:K7};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var NF={kernelName:Em,backendName:\"webgl\",kernelFunc:j7};var ig=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=_e(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=$t(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=_e(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var TF={kernelName:Bn,backendName:\"webgl\",kernelFunc:X7};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var _F={kernelName:es,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Y7=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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eZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Cc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var AF={kernelName:zn,backendName:\"webgl\",kernelFunc:eZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=m$(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var zF={kernelName:ui,backendName:\"webgl\",kernelFunc:xZ};function yZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=d$(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var WF={kernelName:wa,backendName:\"webgl\",kernelFunc:yZ};function bZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var UF={kernelName:pi,backendName:\"webgl\",kernelFunc:bZ};function CZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 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fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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pD={kernelName:kp,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var cD={kernelName:Rs,backendName:\"webgl\",kernelFunc:VZ};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n 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segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function 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ms=O();ms.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);ms.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);ms.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);ms.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);ms.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);ms.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);ms.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);ms.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);ms.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);ms.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>0);ms.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);var kg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 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Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function $o(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function se(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function T3(r){let e;return e=`\n ${Eee()}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,e}function Eee(){return`\n @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ)\n`}function $ee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n const workgroupSizeX = ${t.workgroupSize[0]}u;\n const workgroupSizeY = ${t.workgroupSize[1]}u;\n const workgroupSizeZ = ${t.workgroupSize[2]}u;\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${F3(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=$3(t);return[_3,o.join(`\n`),E3(e.shape),t.getUserCode(),T3(f)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=Lee(s),o.push(s),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:wc(r[h].dtype,t.isVec4)}>;\n `)}),s!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=Oee(e.shape,t.dispatchLayout),c=[_3+Aee,o.join(`\n`),E3(e.shape),u,Pee(e.shape.length)];t.atomic||c.push(Mee(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>Dee(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=$3(t);return c.push(T3(m)),c.join(`\n`)}function R3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=F3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var _3=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n }\n`,Aee=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function E3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 {\n let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n return vec2(d0, d1);\n }`;let s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`;return`${p}; ${u};`}).join(\"\"),`\n fn getCoordsFromIndex(index : i32) -> ${o} {\n ${s}\n return ${o}(${n.join(\",\")});\n }\n `}function Ree(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n fn ${s}() -> vec4 {\n return vec4(${t}[0]);\n }\n `:`\n fn ${s}() ->f32 {\n return f32(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n fn ${s}(${i}) -> vec4 {\n return vec4(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p}) / 4]);\n }\n `:`\n fn ${s}(${i}) -> f32 {\n return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})]);\n }\n `}function Fee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return vec4(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return vec4(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n return f32(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> f32 {\n return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n }\n `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return get${s}();\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> f32{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${$o(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${$o(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> vec4 {\n var coords = coordsIn;\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> f32 {\n var coords = coordsIn;\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n`}function Dee(r,e,t,o){let n=Ree(r,t);return r.shape.length<=e.length&&(n+=Fee(r,e,t,o)),n}function Oee(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function Pee(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function F3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function wc(r,e){return r===\"float32\"?e?\"vec4\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4\":\"i32\":r}function Mee(r,e,t){let o=r.length,n=wc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n result[flatIndex] = ${n}(value);\n }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex / 4, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex / 4, value);\n }\n `:s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex, value);\n }\n `}return s}function Lee(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function $3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var nI={};Ue(nI,{ArrayBufferToTypedArray:()=>oI,GPUBytesPerElement:()=>rI,MatMulProgramType:()=>Ao,computeDispatch:()=>re,computeWorkPerThreadForConv2d:()=>Ll,computeWorkgroupInfoForMatMul:()=>tI,computeWorkgroupSizeForConv2d:()=>Ml,flatDispatchLayout:()=>ue,isWebGPUSupported:()=>Bl,tilesFitEvenlyIntoShape:()=>Vee});var zu=r=>{let e=1;for(let t=0;tt%r[o]===0)}function re(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(zu(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(zu(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(zu(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function tI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Ml(r,e,t=!1){if(t)return[8,8,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Ll(r,e,t=!1){if(t)return[4,4,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ue(r){return{x:r.map((e,t)=>t)}}function rI(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function oI(r,e){if(e===\"float32\")return new Float32Array(r);if(e===\"int32\")return new Int32Array(r);if(e===\"bool\"||e===\"string\")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${e}`)}function Bl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var Ao;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ao||(Ao={}));var zee=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Wee=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Ui=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Bl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Do(this,cr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Ui.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=oI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=cr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=rI(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[];e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4});let s=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(s,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(s,c,p.data.length).set(p.data):new Float32Array(s,c,p.data.length).set(p.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,s,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Wee(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=R3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=A3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=zee){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Ui(n,s)},3);var ye;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.MAX=12]=\"MAX\",r[r.MIN=13]=\"MIN\",r[r.MOD=14]=\"MOD\",r[r.MUL=15]=\"MUL\",r[r.NOT_EQUAL=16]=\"NOT_EQUAL\",r[r.POW=17]=\"POW\",r[r.PRELU=18]=\"PRELU\",r[r.SQUARED_DIFFERENCE=19]=\"SQUARED_DIFFERENCE\",r[r.SUB=20]=\"SUB\"})(ye||(ye={}));var D3=`\n if (isnan(a)) { return a; }\n if (isnan(b)) { return b; }\n `,O3=`\n if (isNaN.r) {\n resultTemp.r = valueForNaN;\n }\n if (isNaN.g) {\n resultTemp.g = valueForNaN;\n }\n if (isNaN.b) {\n resultTemp.b = valueForNaN;\n }\n if (isNaN.a) {\n resultTemp.a = valueForNaN;\n }\n `,aI=`\n let isNaN = isnanVec4(a) | isnanVec4(b);\n ${O3}\n `,Uee=\"return a + b;\",Gee=\"return areal * breal - aimag * bimag;\",Hee=\"return areal * bimag + aimag * breal;\",qee=\"return a / b;\",Kee=\"return f32(a == b);\",jee=\"return vec4(a == b);\",Xee=\"return f32(a > b);\",Yee=\"return vec4(a > b);\",Qee=\"return f32(a >= b);\",Zee=\"return vec4(a >= b);\",Jee=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,ete=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,tte=\"return f32(a < b);\",rte=\"return vec4(a < b);\",ote=\"return f32(a <= b);\",nte=\"return vec4(a <= b);\",ste=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",ate=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,ite=`\n ${D3}\n if (b == 0.) {\n return uniforms.NAN;\n }\n var resultTemp = a % b;\n if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n return resultTemp;\n } else {\n return (resultTemp + b) % b;\n }\n`,ute=`\n let valueForNaN = uniforms.NAN;\n var resultTemp = vec4(a % b);\n ${aI}\n\n if (b[0] == 0.) {\n resultTemp[0] = uniforms.NAN;\n }\n if (b[1] == 0.) {\n resultTemp[1] = uniforms.NAN;\n }\n if (b[2] == 0.) {\n resultTemp[2] = uniforms.NAN;\n }\n if (b[3] == 0.) {\n resultTemp[3] = uniforms.NAN;\n }\n\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n\n return resultTemp;\n`,pte=\"return a * b;\",cte=`\n if (isnan(a) || isnan(b)) {\n return 1.0;\n }\n return f32(a != b);\n`,lte=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n ${aI}\n\n return resultTemp;\n`,mte=`\n if(a < 0.0 && floor(b) < b) {\n return uniforms.NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n if (round(abs(b) % 2.0) != 1.0) {\n return pow(abs(a), b);\n }\n return sign(a) * pow(abs(a), b);\n`,dte=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n let valueForNaN = uniforms.NAN;\n ${O3}\n return resultTemp;\n`,fte=\"if (a < 0.0) { return b * a; } return a;\",hte=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,gte=\"return (a - b) * (a - b);\",xte=\"return a - b;\";function sI(r,e,t=\"uniforms.NAN\"){let o=e?aI:D3;return e?`\n let valueForNaN = ${t};\n var resultTemp = vec4(${r}(a, b));\n `+o+`\n return resultTemp;\n `:o+`\n return ${r}(a, b);\n `}function Ic(r,e){switch(r){case ye.ADD:return Uee;case ye.ATAN2:return sI(\"atan2\",e);case ye.COMPLEX_MULTIPLY_IMAG:return Hee;case ye.COMPLEX_MULTIPLY_REAL:return Gee;case ye.DIV:return qee;case ye.EQUAL:return e?jee:Kee;case ye.GREATER:return e?Yee:Xee;case ye.GREATER_EQUAL:return e?Zee:Qee;case ye.INT_DIV:return e?ete:Jee;case ye.LESS:return e?rte:tte;case ye.LESS_EQUAL:return e?nte:ote;case ye.LOGICAL_AND:return e?ate:ste;case ye.MAX:return sI(\"max\",e);case ye.MIN:return sI(\"min\",e);case ye.MOD:return e?ute:ite;case ye.MUL:return pte;case ye.NOT_EQUAL:return e?lte:cte;case ye.POW:return e?dte:mte;case ye.PRELU:return e?hte:fte;case ye.SQUARED_DIFFERENCE:return gte;case ye.SUB:return xte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.RSQRT=27]=\"RSQRT\",r[r.SIN=28]=\"SIN\",r[r.SINH=29]=\"SINH\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SQRT=31]=\"SQRT\",r[r.SQUARE=32]=\"SQUARE\",r[r.TAN=33]=\"TAN\",r[r.TANH=34]=\"TANH\",r[r.TO_INT=35]=\"TO_INT\"})(Q||(Q={}));var yte=\"return abs(a);\",bte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Cte=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,Ste=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,wte=\"return asinh(a);\",Ite=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,vte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,kte=\"return ceil(a);\",Nte=\"return cos(a);\",Tte=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,_te=\"return exp(a) - 1.0;\",Ete=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",$te=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,Ate=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${S.ERF_P};\n let a1 = ${S.ERF_A1};\n let a2 = ${S.ERF_A2};\n let a3 = ${S.ERF_A3};\n let a4 = ${S.ERF_A4};\n let a5 = ${S.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,Rte=\"return exp(a);\",Fte=\"return floor(a);\",Dte=\"return f32(!isnan(a) && !isinf(a));\",Ote=\"return f32(isinf(a));\",Pte=\"return f32(isnan(a));\",Mte=\"return a;\",Lte=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,Bte=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Vte=\"return f32(!(a >= 1.0));\",zte=\"return -a;\",Wte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Ute=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Gte=\"return 1.0 / a;\",Hte=\"return select(a, 0.0, a < 0.0);\",qte=\"return clamp(a, 0.0, 6.0);\",Kte=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",jte=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Xte=\"return inverseSqrt(a);\",Yte=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Qte=\"return sin(a);\",Zte=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Jte=\"return sqrt(a);\",ere=\"return a * a;\",tre=\"return tan(a);\",rre=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,ore=\"return f32(i32((a)));\";function Ha(r,e){switch(r){case Q.ABS:return yte;case Q.ACOS:return bte;case Q.ACOSH:return Cte;case Q.ASIN:return Ste;case Q.ASINH:return wte;case Q.ATAN:return Ite;case Q.ATANH:return vte;case Q.COS:return Nte;case Q.COSH:return Tte;case Q.CEIL:return kte;case Q.ELU:return e?$te:Ete;case Q.ERF:return Ate;case Q.EXP:return Rte;case Q.EXPM1:return _te;case Q.FLOOR:return Fte;case Q.IS_FINITE:return Dte;case Q.IS_INF:return Ote;case Q.IS_NAN:return Pte;case Q.LINEAR:return Mte;case Q.LOG:return Lte;case Q.LOG1P:return Bte;case Q.LOGICAL_NOT:return Vte;case Q.NEG:return zte;case Q.LEAKYRELU:return e?Ute:Wte;case Q.RECIPROCAL:return Gte;case Q.RELU:return e?jte:Hte;case Q.RELU6:return e?Kte:qte;case Q.RSQRT:return Xte;case Q.SIGMOID:return Yte;case Q.SIN:return Qte;case Q.SINH:return Zte;case Q.SQRT:return Jte;case Q.SQUARE:return ere;case Q.TAN:return tre;case Q.TANH:return rre;case Q.TO_INT:return ore;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error(`${r}-component is not supported.`)}};function ur(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Ha(Q.LINEAR);else if(r===\"relu\")n=Ha(Q.RELU,t);else if(r===\"elu\")n=Ha(Q.ELU,t);else if(r===\"relu6\")n=Ha(Q.RELU6,t);else if(r===\"prelu\")n=Ic(ye.PRELU,t);else if(r===\"sigmoid\")n=Ha(Q.SIGMOID,t);else if(r===\"leakyrelu\")n=Ha(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Hr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function iI(r,e,t,o,n=!1,s=!1,a=!1,i=1){y.assert(t&&i===1||!t,()=>`transposeA ${t} is not compatible with component size ${i}`);let p=`\n let batch = ${r?\"0\":\"batchIn\"};\n ${t?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,u=o?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n var value = ${kt(i)}(0.0);\n let col = colIn * ${i};\n ${n&&a?p:`\n ${t?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${p}\n }\n `}\n return value;\n }\n\n fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n let col = colIn * ${i};\n let batch = ${e?\"0\":\"batchIn\"};\n var value = ${kt(i)}(0.0);\n ${u}\n return value;\n }\n `}function Vl(r,e,t,o,n,s,a=!1,i=!1,p=!1,u=1){return`\n ${iI(t,o,n,s,a,i,p,u)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) {\n let col = colIn * ${u};\n ${a&&i?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Hr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var nre=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol);\n `,sre=(r,e)=>r?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${e===3?\"\":\"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];\"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n }`;function Wu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const innerElementSize = ${l};\n const tileInner = ${o};\n\n ${se()} {\n let localRow = i32(localId.y);\n let tileRow = ${a?\"0\":\"localRow * rowPerThread\"};\n let tileCol = i32(localId.x);\n\n let globalRow = ${a?\"0\":\"i32(globalId.y) * rowPerThread\"};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${nre(t)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${l===3?\"\":\"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];\"}\n\n ${sre(t,l)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var P3=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,are=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Uu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=r[1]*e[1],p=r[0]*e[0],u=t?i:o,c=t?o:i;y.assert(c%e[1]===0&&u%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let l=c/e[1],m=u/e[0],d=o/e[1],f=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${i};\n let globalColStart = i32(workgroupId.x) * ${p};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n ${P3(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * rowPerThread;\n let tileCol = i32(localId.x) * colPerThread;\n\n let globalRow = i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x) * colPerThread;\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let tileRowA = i32(localId.y) * ${l};\n let tileColA = i32(localId.x) * ${m};\n let tileRowB = i32(localId.y) * ${d};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${P3(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${are(t)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${c}>;\n var mm_Bsub : array, ${o}>;\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const tileInner = ${o};\n\n ${se()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${f}\n }\n `}var ire=r=>r?`\n mm_readA(batch, colA, globalRow),\n mm_readA(batch, colA + 1, globalRow),\n mm_readA(batch, colA + 2, globalRow),\n mm_readA(batch, colA + 3, globalRow)\n `:`\n mm_readA(batch, globalRow, colA),\n mm_readA(batch, globalRow, colA + 1),\n mm_readA(batch, globalRow, colA + 2),\n mm_readA(batch, globalRow, colA + 3)\n `;function ure(r,e=!1){return y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`),`\n const tileSize = ${r[0]*4};\n var mm_Asub : array, ${r[0]}>;\n\n ${se()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / tileSize + 1;\n let batch = i32(globalId.z);\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n let colA = t * tileSize + tileCol * 4;\n mm_Asub[tileCol] = vec4(${ire(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileSize / 4; k = k + 1) {\n let rowB = t * tileSize + k * 4;\n let BCached = vec4(mm_readB(batch, rowB, globalCol),\n mm_readB(batch, rowB + 1, globalCol),\n mm_readB(batch, rowB + 2, globalCol),\n mm_readB(batch, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var _g=class{constructor(e,t,o,n,s=!1,a=!1,i=null,p=null,u=null,c=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];if(this.isVec4=(l%4===0&&!s||t[1]%4===0&&s)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!s,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let f=tI(t[1],l,t[2],s);this.workgroupSize=f.workgroupSize,this.elementsPerThread=f.elementsPerThread}this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let m=i!=null,d=u!=null;m&&this.variableNames.push(\"bias\"),d&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=c,this.transposeA=s,this.transposeB=a,this.addBias=m,this.activation=p,this.hasPreluActivationWeights=d,this.batchAEqualOne=o,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?ure(this.workgroupSize,this.transposeA):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)}\n `}};function pre(){return`\n var sumValues : array;\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) {\n let dataA = mm_readA(batch, row, k);\n let dataB = mm_readB(batch, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = workgroupSizeX / 2u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Eg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize);let u=a!=null,c=p!=null;u&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=t,this.batchBEqualOne=o,this.shaderKey=`matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${pre()}\n `}};function cre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${se()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batch, globalRow, globalColA);\n var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batch, globalRow, globalColA);\n regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var $g=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${cre(this.workgroupSize)}\n `}};var Ag=class{constructor(e,t,o,n,s=!1,a=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(s&&this.outputShape[1]%4===0||!s&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=re(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=s,this.transposeB=a,this.batchAEqualOne=o,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=n=>`\n for (var i = 0; i < ${n}; i = i + 1)\n {\n var oldValue = atomicLoad(&(result[flatIndex + i]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + ${n>1?\"value[i]\":\"value\"};\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n `,t=this.isVec4?4:1;return`\n ${iI(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t)}) {\n let col = colIn * ${t};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n ${e(t)}\n }\n }\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Rg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Hr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Fg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function dr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Fg(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var M3={kernelName:Cs,backendName:\"webgpu\",kernelFunc:dr};function de(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var L3={kernelName:Ns,backendName:\"webgpu\",kernelFunc:de};function Gu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=br.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=de({inputs:{x:r},backend:n,attrs:{shape:k}}),A=de({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=x===1,M=b===1,L=[$,A],W=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],V,U,q=[D,d,f],H=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(H<0){let X=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Z=X>0?X:n.thresholdToIncreaseWorkgroups,ee=D*Math.ceil(d/32)*Math.ceil(f/32);ee<=Z||d<=8&&ee<=Z*2?D*d*f<=128?H=Ao.MatMulReduceProgram:D===1&&m>=2e3?H=Ao.MatMulSplitKProgram:H=Ao.MatMulSmallOutputSizeProgram:H=Ao.MatMulPackedProgram}switch(H){case Ao.MatMulReduceProgram:V=new Eg(q,P,M,t,o,s,p,a);break;case Ao.MatMulSplitKProgram:{if(U=dr({backend:n,attrs:{shape:q,value:0,dtype:r.dtype}}),V=new Ag(q,m,P,M,t,o),s||p){U=n.runWebGPUProgram(V,L,r.dtype,W,U);let Z=new Rg(U.shape,s,p,a),ee=null,Y=[U];s&&Y.push(s),a&&Y.push(a),p===\"leakyrelu\"&&(ee=[{type:\"float32\",data:[i]}],Z.uniforms+=\" alpha : f32,\");let J=n.runWebGPUProgram(Z,Y,U.dtype,ee);R.push(U);let ie=de({inputs:{x:J},backend:n,attrs:{shape:w}});R.push(J);for(let pe of R)n.disposeData(pe.dataId);return ie}break}case Ao.MatMulSmallOutputSizeProgram:V=new $g(k,_,q,t,o,s,p,a);break;case Ao.MatMulPackedProgram:let X=n.adapterInfo.isIntel();V=new _g(k,q,P,M,t,o,s,p,a,X);break;default:throw new Error(`Unsupported MatMulProgramType ${H}.`)}s&&L.push(s),a&&L.push(a),p===\"leakyrelu\"&&(W.push({type:\"float32\",data:[i]}),V.uniforms+=\" alpha : f32,\"),U=n.runWebGPUProgram(V,L,r.dtype,W,U);let j=de({inputs:{x:U},backend:n,attrs:{shape:w}});R.push(U);for(let X of R)n.disposeData(X.dataId);return j}function lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Gu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var B3={kernelName:fo,backendName:\"webgpu\",kernelFunc:lre};var zl=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Ic(this.op,!1)}\n }\n\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Hu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Ic(this.op,this.isVec4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${se(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n let b = getBByOutputIndex(index);\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function Ft(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var V3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Ft};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:t}),p=Ft({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var z3={kernelName:ei,backendName:\"webgpu\",kernelFunc:po};var Ro=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let o=128;this.workgroupSize=[o,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Ha(this.op,!1)}\n }\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function Se({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Ro(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function ot({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ye.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new zl(ye.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new zl(ye.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:W3,castImpl:U3,ceilImpl:G3,concatImpl:H3,equalImpl:q3,expImpl:K3,expm1Impl:j3,floorImpl:X3,gatherNdImpl:Y3,gatherV2Impl:Q3,greaterEqualImpl:Z3,greaterImpl:J3,lessEqualImpl:eM,lessImpl:tM,logImpl:rM,maxImpl:oM,maximumImpl:nM,minimumImpl:sM,multiplyImpl:aM,negImpl:iM,notEqualImpl:uM,prodImpl:pM,rangeImpl:cM,rsqrtImpl:lM,scatterImpl:mM,simpleAbsImpl:dM,sliceImpl:fM,stridedSliceImpl:hM,stringNGramsImpl:gM,subImpl:xM,tileImpl:yM,topKImpl:bM,transposeImpl:CM,uniqueImpl:kNt}=Qp;var mre=Se({opType:Q.ABS,cpuKernelImpl:dM}),SM={kernelName:gs,backendName:\"webgpu\",kernelFunc:mre};var dre=Se({opType:Q.ACOS}),wM={kernelName:sa,backendName:\"webgpu\",kernelFunc:dre};var fre=Se({opType:Q.ACOSH}),IM={kernelName:aa,backendName:\"webgpu\",kernelFunc:fre};var hre=ot({opType:ye.ADD,cpuKernelImpl:W3,supportsComplex:!0}),vM={kernelName:eo,backendName:\"webgpu\",kernelFunc:hre};var Dg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${se(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function gre(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Dg(s);return t.runWebGPUProgram(a,o,n)}var kM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:gre};var Og=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),`\n const tileSize = ${this.workgroupSize[0]};\n var tile : array, ${this.workgroupSize[0]}>;\n ${se()} {\n var x = i32(workgroupId.x) * tileSize + i32(localId.x);\n var y = i32(workgroupId.y) * tileSize + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * tileSize + i32(localId.x);\n y = i32(workgroupId.x) * tileSize + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var Pg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let o=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${o}\n }\n }\n `}};function qr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=oM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=pM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":ka(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Mg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=de({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"all\",t)}var TM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:yre};function bre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"any\",t)}var _M={kernelName:Bo,backendName:\"webgpu\",kernelFunc:bre};var vc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ue(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=re(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${$o(this.inputShape.length-1)}`,t=()=>{let o=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(o+=\"outputCoords,\");else for(let n=0;n u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let reduceLength = ${e()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = getX(${t()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${t()} 0);\n let reduceLength = ${e()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${t()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var EM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var $M={kernelName:Za,backendName:\"webgpu\",kernelFunc:Sre};var wre=Se({opType:Q.ASIN}),AM={kernelName:ia,backendName:\"webgpu\",kernelFunc:wre};var Ire=Se({opType:Q.ASINH}),RM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ire};var vre=Se({opType:Q.ATAN}),FM={kernelName:pa,backendName:\"webgpu\",kernelFunc:vre};var kre=ot({opType:ye.ATAN2}),DM={kernelName:la,backendName:\"webgpu\",kernelFunc:kre};var Nre=Se({opType:Q.ATANH}),OM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Nre};var Wl=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n var count = 0.0;\n\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, coords[3]);\n ${e}\n }\n }\n\n setOutputAtIndex(index, ${t});\n }\n }\n `}};var Lg=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.stride;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Ul(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return qr(n,s,a,\"max\",t)}var PM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Ul};function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"mean\",t)}var MM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:uI};function Bg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Ft({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=de({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=uI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Ul({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=de({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Lg(e):(t===\"avg\"?n=new Wl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Wl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"avg\",t)}var LM={kernelName:zo,backendName:\"webgpu\",kernelFunc:Tre};function _re(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Gu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var BM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:_re};var Vg=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=Ere(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},pI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Ere(r){if(r===1)return\"sourceLoc\";if(r<=6)return pI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function ds(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=fM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Vg(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var VM={kernelName:_s,backendName:\"webgpu\",kernelFunc:ds};var $re=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=de({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=de({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ds({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},zM={kernelName:xs,backendName:\"webgpu\",kernelFunc:$re};var Are=`\n fn bincount_write(index: i32, value: f32) {\n var oldValue = atomicLoad(& (result[index]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + value;\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(\n &(result[index]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n`,Rre=`\n fn bincount_write(index: i32, value: f32) {\n result[index] = value;\n }\n`,kc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Rre:Are}\n ${se(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(index))\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(coord[0], coord[1]))\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=dr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new kc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var WM={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Fre};var cI=ot({opType:ye.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:uM}),UM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:cI};function qa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var GM={kernelName:ai,backendName:\"webgpu\",kernelFunc:qa};function HM(r,e){let t=new Ro(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Ft({inputs:{x:n},backend:t});let a=Vr(n.shape),i=lI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=qa({inputs:{input:n},backend:t}),i=lI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=U3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return HM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=cI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var qM={kernelName:co,backendName:\"webgpu\",kernelFunc:lI};var Dre=Se({opType:Q.CEIL,cpuKernelImpl:G3}),KM={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Dre};var zg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue : vec4;\n for (var i = 0; i < 4; i = i + 1) {\n if (isnan(value[i])) {\n clampedValue[i] = value[i];\n } else {\n clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n }\n }\n\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var Wg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function Ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new zg(n.shape):i=new Wg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var jM={kernelName:lo,backendName:\"webgpu\",kernelFunc:Ore};var Ug=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sqa({inputs:{input:C},backend:t})),h=r.map(C=>qu({inputs:{input:C},backend:t})),g=Nc(f,e,t),x=Nc(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return de({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=H3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Ug(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=de({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Pre(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>de({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function mI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Nc(p,s,t)}var YM={kernelName:ys,backendName:\"webgpu\",kernelFunc:mI};function Mre(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${x} % inChannels;\n var resData = ${kt(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${kt(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),$=r?kt(p):kt(i);return`\n ${ur(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?C:w}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} {\n ${r?w:C}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Hr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var Gg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4\",\"vec4\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Uu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Mre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var Hg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${se(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var qg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${se(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n uniforms.pad[1];\n let xCol = offsetX + uniforms.dilation[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function Kg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=de({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=de({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Kg(s.shape,p);x!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Kg(n.shape,p);x!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Gu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=de({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Bre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],$=new qg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram($,[r],r.dtype,A),D=[];D.push(R);let P=de({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Kg(s.shape,C);U!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Kg(n.shape,C);U!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let W=Gu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),V=de({inputs:{x:W},backend:o,attrs:{shape:t.outShape}});D.push(W);for(let U of D)o.disposeData(U.dataId);return V}function jg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return Lre({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Bre({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Hg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new Gg(t,_,$,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=de({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=de({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Vre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return jg({x:n,filter:s,convInfo:m,backend:o})}var QM={kernelName:Go,backendName:\"webgpu\",kernelFunc:Vre};function zre(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${kt(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${kt(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${kt(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${kt(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var Xg=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize):Uu(this.elementsPerThread,this.workgroupSize);return`\n ${zre(this.isVec4?4:1)}\n ${e}\n `}};var Yg=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n if (${this.isChannelsLast}) {\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n } else {\n let xValue = getDy(batch, d2, idyR, idyC);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Wre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Yg(m);else{f=new Xg(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var ZM={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Wre};var Ure=Se({opType:Q.COS}),JM={kernelName:qo,backendName:\"webgpu\",kernelFunc:Ure};var Gre=Se({opType:Q.COSH}),eL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Gre};var Qg=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Hre=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Qg(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},tL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:Hre};var Ku;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Ku||(Ku={}));var Gl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Ku.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${rL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${oL(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${oL(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${rL(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function rL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function oL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zg(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Gl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Gl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Prod,n,t,s,a,i)}var nL={kernelName:jo,backendName:\"webgpu\",kernelFunc:qre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Sum,n,t,s,a,i)}var sL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Kre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=dr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new kc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var aL={kernelName:ti,backendName:\"webgpu\",kernelFunc:jre};var Jg=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Xre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new Jg(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:Xre};var ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pad;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return`\n ${ur(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n const strideHeight = ${this.convInfo.strideHeight};\n const strideWidth = ${this.convInfo.strideWidth};\n ${se()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var _c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilation[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilation[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Tc(d):(g=new _c(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uL={kernelName:Zo,backendName:\"webgpu\",kernelFunc:Yre};var dI=ot({opType:ye.MUL,cpuKernelImpl:aM,supportsComplex:!0}),pL={kernelName:kn,backendName:\"webgpu\",kernelFunc:dI};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"sum\",t)}var cL={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Hl};function Qre(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Hl({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var lL={kernelName:ri,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Se({opType:Q.ELU}),mL={kernelName:en,backendName:\"webgpu\",kernelFunc:Zre};var Jre=ot({opType:ye.EQUAL,dtype:\"bool\",cpuKernelImpl:q3}),dL={kernelName:tn,backendName:\"webgpu\",kernelFunc:Jre};var eoe=Se({opType:Q.ERF}),fL={kernelName:ma,backendName:\"webgpu\",kernelFunc:eoe};var fI=Se({opType:Q.EXP,cpuKernelImpl:K3,dtype:\"float32\"}),hL={kernelName:rn,backendName:\"webgpu\",kernelFunc:fI};function tx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),de({inputs:{x:s},backend:o,attrs:{shape:i}})}var gL={kernelName:bs,backendName:\"webgpu\",kernelFunc:tx};var toe=Se({opType:Q.EXPM1,cpuKernelImpl:j3}),xL={kernelName:da,backendName:\"webgpu\",kernelFunc:toe};var ql=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function rx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=de({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ql(\"real\",u),l=new ql(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=de({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function roe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!1,t)}var yL={kernelName:oi,backendName:\"webgpu\",kernelFunc:roe};var ox=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var bL={kernelName:on,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ooe=Se({opType:Q.FLOOR,cpuKernelImpl:X3}),CL={kernelName:nn,backendName:\"webgpu\",kernelFunc:ooe};var noe=ot({opType:ye.INT_DIV,dtype:\"int32\"}),SL={kernelName:sn,backendName:\"webgpu\",kernelFunc:noe};var nx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${se(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var wL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:soe},Ec,hI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),sx=new Map;function 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ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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ix=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function 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ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=poe(this.aShape);return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function poe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=de({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var TL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:gI};var coe=ot({opType:ye.GREATER,cpuKernelImpl:J3,dtype:\"bool\"}),_L={kernelName:pn,backendName:\"webgpu\",kernelFunc:coe};var loe=ot({opType:ye.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Z3}),EL={kernelName:cn,backendName:\"webgpu\",kernelFunc:loe};function moe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!0,t)}var $L={kernelName:ni,backendName:\"webgpu\",kernelFunc:moe};var doe=Se({opType:Q.IS_FINITE,dtype:\"bool\"}),AL={kernelName:fa,backendName:\"webgpu\",kernelFunc:doe};var foe=Se({opType:Q.IS_INF,dtype:\"bool\"}),RL={kernelName:ha,backendName:\"webgpu\",kernelFunc:foe};var hoe=Se({opType:Q.IS_NAN,dtype:\"bool\"}),FL={kernelName:ln,backendName:\"webgpu\",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Ro(n.shape,Q.LEAKYRELU);return i.uniforms=\"alpha : f32,\",t.runWebGPUProgram(i,[n],\"float32\",a)}var DL={kernelName:mn,backendName:\"webgpu\",kernelFunc:goe};var xoe=ot({opType:ye.LESS,dtype:\"bool\",cpuKernelImpl:tM}),OL={kernelName:dn,backendName:\"webgpu\",kernelFunc:xoe};var yoe=ot({opType:ye.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:eM}),PL={kernelName:fn,backendName:\"webgpu\",kernelFunc:yoe};var boe=Se({opType:Q.LOG,cpuKernelImpl:rM}),ML={kernelName:hn,backendName:\"webgpu\",kernelFunc:boe};var Coe=Se({opType:Q.LOG1P}),LL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Coe};var Soe=ot({opType:ye.LOGICAL_AND,dtype:\"bool\"}),BL={kernelName:gn,backendName:\"webgpu\",kernelFunc:Soe};var woe=Se({opType:Q.LOGICAL_NOT}),VL={kernelName:xn,backendName:\"webgpu\",kernelFunc:woe};var Ioe=ot({opType:ye.MAX,cpuKernelImpl:nM}),zL={kernelName:bn,backendName:\"webgpu\",kernelFunc:Ioe};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"max\",t)}var WL={kernelName:Cn,backendName:\"webgpu\",kernelFunc:voe};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"min\",t)}var UL={kernelName:wn,backendName:\"webgpu\",kernelFunc:koe};var Noe=ot({opType:ye.MIN,cpuKernelImpl:sM}),GL={kernelName:In,backendName:\"webgpu\",kernelFunc:Noe};var px=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + 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Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Lt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XL={kernelName:_n,backendName:\"webgpu\",kernelFunc:$oe};var cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Aoe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new cx(u,a),l=de({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=de({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YL={kernelName:En,backendName:\"webgpu\",kernelFunc:Aoe};function Kl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=Kl({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QL={kernelName:Fs,backendName:\"webgpu\",kernelFunc:Kl};function ZL(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=ZL({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JL={kernelName:Is,backendName:\"webgpu\",kernelFunc:ZL};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return tx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=tx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=mI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eB={kernelName:vs,backendName:\"webgpu\",kernelFunc:Roe};var lx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let start = ${s};\n let end = ${a};\n let outC = getCoordsFromIndex(index);\n\n if (${i} || ${p}) {\n setOutputAtIndex(index, uniforms.constantValue);\n } else {\n let coords = outC - start;\n setOutputAtIndex(index, getX(${u}));\n }\n }\n }\n `}};var xI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return dr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new lx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tB={kernelName:$n,backendName:\"webgpu\",kernelFunc:xI};var Foe=ot({opType:ye.POW}),rB={kernelName:An,backendName:\"webgpu\",kernelFunc:Foe};function Doe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Hu(ye.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Doe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"prod\",t)}var nB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ooe};var Poe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sB={kernelName:ks,backendName:\"webgpu\",kernelFunc:Poe};var yI=ot({opType:ye.DIV}),aB={kernelName:Jo,backendName:\"webgpu\",kernelFunc:yI};var Moe=Se({opType:Q.RECIPROCAL}),iB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:Moe};var Loe=Se({opType:Q.RELU}),uB={kernelName:On,backendName:\"webgpu\",kernelFunc:Loe};var Boe=Se({opType:Q.RELU6}),pB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Boe};var mx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new mx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Voe};var dx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function zoe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new dx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var lB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:zoe};var fx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=de({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new fx(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=de({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var mB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:Woe};var hx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var dB={kernelName:es,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new hx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Uoe=Se({opType:Q.RSQRT,cpuKernelImpl:lM}),fB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Uoe};var Gi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ue(e),this.dispatch=re(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(c,l)=>`coords[${l}]`).join(\", \")})`,p=(c,l)=>{let m=`atomicAdd(${c}, bitcast(${l}))`;this.type===\"float32\"&&(m=`\n {\n var oldBits = 0;\n var newBits = bitcast(${l});\n loop {\n let info = atomicCompareExchangeWeak(${c}, oldBits, newBits);\n if (info.exchanged) {\n break;\n }\n oldBits = info.old_value;\n let oldValue = bitcast(oldBits);\n let newValue = oldValue + (${l});\n newBits = bitcast(newValue);\n }\n }\n `);let d=`atomicStore(${c}, bitcast(${l}));`;return this.sumDupeIndices?m:d};return`\n ${s}\n\n ${se(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${wc(this.type,!1)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${p(\"&result[flatIndex]\",\"updateValue\")};\n }\n }`}};function Goe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=de({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=de({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=dr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Gi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=de({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var hB={kernelName:zn,backendName:\"webgpu\",kernelFunc:Goe};var gx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new gx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var gB={kernelName:ii,backendName:\"webgpu\",kernelFunc:Hoe};var xx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function qoe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new xx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var xB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:qoe};var Koe=Se({opType:Q.SIGMOID}),yB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Koe};var joe=Se({opType:Q.SIN}),bB={kernelName:Wn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Se({opType:Q.SINH}),CB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Xoe};var bI=ot({opType:ye.SUB,cpuKernelImpl:xM,supportsComplex:!0}),SB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:bI};function Yoe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Ul({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=de({inputs:{x:i},backend:t,attrs:{shape:p}}),c=bI({inputs:{a:n,b:u},backend:t}),l=fI({inputs:{x:c},backend:t}),m=Hl({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=de({inputs:{x:m},backend:t,attrs:{shape:p}}),f=yI({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var wB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},IB={kernelName:Es,backendName:\"webgpu\",kernelFunc:Qoe};var yx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=yM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new yx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var vB={kernelName:to,backendName:\"webgpu\",kernelFunc:CI};function Joe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=mM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=de({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?de({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=de({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=CI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Gi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let $=de({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),$}var kB={kernelName:li,backendName:\"webgpu\",kernelFunc:Joe};function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ds({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NB={kernelName:$s,backendName:\"webgpu\",kernelFunc:ene};var tne=Se({opType:Q.SQRT}),TB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:tne};var _B={kernelName:mi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Ro(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var rne=ot({opType:ye.SQUARED_DIFFERENCE}),EB={kernelName:Kn,backendName:\"webgpu\",kernelFunc:rne};var bx=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function one(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=de({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=ds({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=de({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),A=le(n.shape,n.dtype,$),R=hM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let $=new bx(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram($,[n],n.dtype,A);k=de({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var $B={kernelName:jn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=gM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var AB={kernelName:As,backendName:\"webgpu\",kernelFunc:nne};var sne=Se({opType:Q.TAN}),RB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:sne};var ane=Se({opType:Q.TANH}),FB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:ane};var Cx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function $c(r,e){e!==null&&r.disposeData(e.dataId)}function DB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let A=h(),R=new Cx($),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),$c(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new Sx([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram($,_,\"int32\",R),$c(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ds({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),$c(t,x);let b=gI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});$c(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=de({inputs:{x:f},attrs:{shape:C},backend:t}),$c(t,x);let w=b;return b=de({inputs:{x:b},attrs:{shape:C},backend:t}),$c(t,w),[b,f]}var OB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:ine};var wx=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var PB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB=\"4.1.0\",lne=\"4.1.0\",mne=\"4.1.0\",dne=\"4.1.0\",fne=\"4.1.0\",hne=\"0.0.1-alpha.16\",gne={tfjs:LB,\"tfjs-core\":LB,\"tfjs-converter\":lne,\"tfjs-backend-cpu\":mne,\"tfjs-backend-webgl\":dne,\"tfjs-backend-wasm\":fne,\"tfjs-backend-webgpu\":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,fv as acos,hv as acosh,xe as add,gv as addN,xv as all,yv as any,bv as argMax,Cv as argMin,Sv as asin,wv as asinh,Iv as atan,vv as atan2,kv as atanh,td as avgPool,_v as avgPool3d,Oie as backend,S as backend_util,Ev as basicLSTMCell,wi as batchNorm,Av as batchNorm2d,Rv as batchNorm3d,Fv as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,Dv as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Q0 as browser,le as buffer,Ke as cast,Ov as ceil,Pv as clipByValue,Br as clone,Tr as complex,gt as concat,Mv as concat1d,Lv as concat2d,Bv as concat3d,Vv as concat4d,zv as conv1d,vi as conv2d,Wv as conv2dTranspose,Uv as conv3d,Hv as conv3dTranspose,Dne as copyRegisteredKernels,qv as cos,Kv as cosh,il as cosineWindow,jv as cumprod,Xv as cumsum,Cr as customGrad,Yv as denseBincount,eC as deprecationWarn,Qv as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Zv as diag,Jv as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,j0 as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,pv as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,h0 as tensor_util,dv as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') { // TBD replace with navigator.userAgentData once in mainline\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam', 'no devices');\n return;\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam', 'no stream');\n return;\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh.keepInvalid) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=o||Sb(t,this.size),this.strides=xs(e)}set(e,...t){t.length===0&&(t=[0]),$(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let o=this.locToIndex(t);this.values[o]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let s=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(s)}t++}let o=e[e.length-1];for(let n=0;nRp(o))}catch(o){throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\")}}return e}dataToGPU(e){return this.throwIfDisposed(),ns().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=ns().readSync(this.dataId);if(this.dtype===\"string\")try{return e.map(t=>Rp(t))}catch(t){throw new Error(\"Failed to decode the string bytes into utf-8. 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it{constructor(e,t,o,n){super(e.shape,e.dtype,e.dataId,n),this.trainable=t,this.name=o}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Or(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ns().disposeTensor(this),this.dataId=e.dataId,ns().incRef(this,null)}dispose(){ns().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Na,Symbol.hasInstance,{value:r=>r instanceof it&&r.assign!=null&&r.assign instanceof Function});var C0={};Ge(C0,{assertTypesMatch:()=>Ub,getTensorsInContainer:()=>Qc,isTensorInList:()=>vz,makeTypesMatch:()=>De});var Lb;(function(r){r.R0=\"R0\",r.R1=\"R1\",r.R2=\"R2\",r.R3=\"R3\",r.R4=\"R4\",r.R5=\"R5\",r.R6=\"R6\"})(Lb||(Lb={}));var Bb;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"int32\",r.complex64=\"complex64\"})(Bb||(Bb={}));var Vb;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(Vb||(Vb={}));var zb;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(zb||(zb={}));var Wb;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Wb||(Wb={}));var Iz={float32:zb,int32:Bb,bool:Vb,complex64:Wb};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return Iz[r][e]}function Ta(r){return dt(r,\"int32\")}function De(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Ub(r,e){$(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function vz(r,e){return e.some(t=>t.id===r.id)}function Qc(r){let e=[];return b0(r,e,new Set),e}function b0(r,e,t){if(r==null)return;if(r instanceof it){e.push(r);return}if(!kz(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),b0(s,e,t))}}function kz(r){return Array.isArray(r)||typeof r==\"object\"}function Gb(r){return r.kernelName!=null}var Mm=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},vi=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Mm}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Rm(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Zr)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return vi.nextTensorId++}nextVariableId(){return vi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Kc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Gb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Gb(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Kc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Gb(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Eb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&Oo(e[0])&&(s=e.map(p=>Ii(p)));let a=n.write(s,t,o),i=new it(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=vb(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new it(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new Na(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*up(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof Na||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*up(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=Eb(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=lp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Qc(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if($(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));$(s instanceof it,()=>\"The result y returned by f() must be a tensor.\");let a=l0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?Nz(s.shape):o,m0(i,a,u=>this.tidy(u),Tz);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return $(gs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{$(t.every(i=>i instanceof it),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),$(o.value instanceof it,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(gs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),$(c.every(m=>m instanceof it),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=ou(),o=await this.backend.time(e);return o.wallMs=ou()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Mm;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};vi.nextTensorId=0;vi.nextVariableId=0;function Nz(r){let e=Wc(We(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Hb(){let r=Tb();if(r._tfengine==null){let e=new Uc(r);r._tfengine=new vi(e)}return jI(r._tfengine.ENV),g0(()=>r._tfengine),r._tfengine}var T=Hb();function Tz(r,e){let t={a:r,b:e};return T.runKernel(eo,t)}var ki={};Ge(ki,{isBrowser:()=>qb,isMobile:()=>Ez,mockIsMobile:()=>$z});function _z(){return typeof navigator!=\"undefined\"&&navigator!=null}var Kb;function $z(r){Kb=r}function Ez(r){if(Kb!==void 0)return Kb;if(r||_z()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),$(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),$(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),$(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&$(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&$(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),_i(a,i,p,c,u,s)}var uv=N({batchNorm3d_:AW});function RW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return 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s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(jo,s,a)}var kv=N({cumsum_:ZW});function JW(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");$(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),$(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),$(t>=0,()=>`size must be non-negative, but got ${t}.`),$(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ni,a,i)}var Nv=N({denseBincount_:JW});function eU(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];$(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),gt(n.shape,s.shape,\"Error in hingeLoss: \");let i=Ce(1);n=ke(ne(Ce(2),n),i);let p=Fi(ke(i,ne(n,s)));return ar(p,a,o)}var G1=N({hingeLoss_:MH});function LH(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),gt(s.shape,a.shape,\"Error in huberLoss: \");let p=Ce(o),u=Yt(ke(a,s)),c=fd(u,p),l=ke(u,c),m=be(ne(Ce(.5),Qt(c)),ne(p,l));return ar(m,i,n)}var H1=N({huberLoss_:LH});function BH(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),gt(s.shape,a.shape,\"Error in logLoss: \");let p=Ce(1),u=Ce(o),c=Cr(ne(s,Da(be(a,u)))),l=ne(ke(p,s),Da(be(ke(p,a),u))),m=ke(c,l);return ar(m,i,n)}var K1=N({logLoss_:BH});function VH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),gt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=_d(n,s);return ar(i,a,o)}var q1=N({meanSquaredError_:VH});function zH(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");gt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=Fi(o),s=ne(o,t),a=sd(So(Cr(Yt(o))));return be(ke(n,s),a)}function WH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),a=v(e,\"logits\",\"sigmoidCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"sigmoidCrossEntropy\")),gt(s.shape,a.shape,\"Error in sigmoidCrossEntropy: \"),o>0){let u=Ce(o),c=Ce(1),l=Ce(.5);s=be(ne(s,ke(c,u)),ne(l,u))}let p=zH(s,a);return ar(p,i,n)}var j1=N({sigmoidCrossEntropy_:WH});function UH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return br((n,s,a)=>{let p=ud(s,[t],!0),u=ke(je(s,\"float32\"),p);a([n,u]);let c=Cr(ne(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Ea(d.shape,[t]);return[ne(W(d,x),ke(je(h,\"float32\"),So(g))),ne(W(d,x),ke(So(g),je(h,\"float32\")))]}}})(r,e)}function GH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),gt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=Ce(o),c=Ce(1),l=Ce(s.shape[1]);s=be(ne(s,ke(c,u)),He(u,l))}let p=UH(s,a);return ar(p,i,n)}var X1=N({softmaxCrossEntropy_:GH});function HH(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(hi,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var Y1=N({sparseFillEmptyRows_:HH});function KH(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(va,a);return{outputIndices:i[0],outputShape:i[1]}}var Q1=N({sparseReshape_:KH});function qH(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(gi,a)}var Z1=N({sparseSegmentMean_:qH});function jH(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(xi,a)}var J1=N({sparseSegmentSum_:jH});function XH(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ds,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var eN=N({stringNGrams_:XH});function YH(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(Ci,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var tN=N({stringSplit_:YH});function QH(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Si,n,o)}var rN=N({stringToHashBucketFast_:QH});var ZH={fft:zp,ifft:du,rfft:Wp,irfft:Td},JH={hammingWindow:C1,hannWindow:Fd,frame:Od,stft:S1},eK={flipLeftRight:I1,grayscaleToRGB:v1,resizeNearestNeighbor:O1,resizeBilinear:F1,rotateWithOffset:k1,cropAndResize:w1,nonMaxSuppression:N1,nonMaxSuppressionAsync:$1,nonMaxSuppressionWithScore:E1,nonMaxSuppressionWithScoreAsync:A1,nonMaxSuppressionPadded:R1,nonMaxSuppressionPaddedAsync:D1,threshold:P1,transform:M1},tK={bandPart:L1,gramSchmidt:B1,qr:z1},rK={absoluteDifference:W1,computeWeightedLoss:ar,cosineDistance:U1,hingeLoss:G1,huberLoss:H1,logLoss:K1,meanSquaredError:q1,sigmoidCrossEntropy:j1,softmaxCrossEntropy:X1},oK={sparseFillEmptyRows:Y1,sparseReshape:Q1,sparseSegmentMean:Z1,sparseSegmentSum:J1},nK={stringNGrams:eN,stringSplit:tN,stringToHashBucketFast:rN};var oN={};Ge(oN,{Serializable:()=>il,SerializationMap:()=>qs,registerClass:()=>IC});var il=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},qs=class{constructor(){this.classNameMap={}}static getMap(){return qs.instance==null&&(qs.instance=new qs),qs.instance}static register(e){qs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function IC(r){$(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),$(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),$(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),qs.register(r)}var wr=class extends il{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:Ce(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var yu=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adadelta\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=be(ne(p,this.rho),ne(Qt(i),1-this.rho)),l=ne(He($r(be(u,this.epsilon)),$r(be(p,this.epsilon))),i),m=be(ne(u,this.rho),ne(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(ne(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var bu=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return\"Adagrad\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Us(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=be(i,Qt(a));i.assign(p);let u=be(ne(He(a,$r(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Cu=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adam\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=ke(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=be(ne(l,this.beta2),ne(Qt(u),1-this.beta2)),f=He(m,o),h=He(d,n);c.assign(m),l.assign(d);let g=be(ne(He(f,be($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ne(this.accBeta1,this.beta1)),this.accBeta2.assign(ne(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Aa(this.beta1,this.iterations_+1)),this.accBeta2.assign(Aa(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var Su=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adamax\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=He(-this.learningRate,be(ne(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=ne(l,this.beta2),f=Yt(u),h=dd(d,f);c.assign(m),l.assign(h);let g=be(ne(He(n,o),He(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(ne(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var Pa=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return\"SGD\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=be(ne(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var wu=class extends Pa{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=Ce(this.momentum)}static get className(){return\"Momentum\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=be(ne(this.m,a),i);this.useNesterov?p=be(ne(this.c,be(i,ne(u,this.m))),s):p=be(ne(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var Iu=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}static get className(){return\"RMSProp\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let 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s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case\"While\":case\"StatelessWhile\":{let o=I(\"body\",r,e,t),n=I(\"cond\",r,e,t),s=I(\"args\",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case\"LoopCond\":{let o=I(\"pred\",r,e,t);return[is(o)]}case\"Switch\":{let o=I(\"pred\",r,e,t),n=I(\"data\",r,e,t);return n.kept||(n=is(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case\"Merge\":{let o=r.inputNames.find(n=>Gt(n,e,t)!==void 0);if(o){let n=Gt(o,e,t);return[is(n)]}return}case\"Enter\":{let o=I(\"frameName\",r,e,t),n=I(\"tensor\",r,e,t);return t.enterFrame(o),[is(n)]}case\"Exit\":{let o=I(\"tensor\",r,e,t);return t.exitFrame(),[is(o)]}case\"NextIteration\":{let o=I(\"tensor\",r,e,t);return t.nextIteration(),[is(o)]}case\"TensorArrayV3\":{let o=I(\"size\",r,e,t),n=I(\"dtype\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"dynamicSize\",r,e,t),i=I(\"clearAfterRead\",r,e,t),p=I(\"identicalElementShapes\",r,e,t),u=I(\"name\",r,e,t),c=new nf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,Ce(1)]}case\"TensorArrayWriteV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case\"TensorArrayReadV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t);return[t.getTensorArray(o.id).read(n)]}case\"TensorArrayGatherV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"dtype\",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case\"TensorArrayScatterV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case\"TensorArrayConcatV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id),s=I(\"dtype\",r,e,t);return[n.concat(s)]}case\"TensorArraySplitV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"lengths\",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case\"TensorArraySizeV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return[Ce(n.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case\"TensorListSetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case\"TensorListGetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let o=I(\"indices\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"numElements\",r,e,t),i=BN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let o=I(\"elementShape\",r,e,t),n=I(\"elementDType\",r,e,t),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let a=I(s,r,e,t),i=r.op===\"TensorListReserve\"?-1:a,p=LN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case\"TensorListGather\":{let o=I(\"tensorListId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case\"TensorListStack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=MN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=VN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[Ce(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function WN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ll(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ll(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function rS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var HN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=rS(r,e,t),l=await 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n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[is(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>is(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[is(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ce(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eT=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tT=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rT=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oT=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nT=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sT=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function oS(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>FN(a,i,p));case\"basic_math\":return n(()=>ON(a,i,p));case\"control\":return zN(a,i,p);case\"convolution\":return n(()=>UN(a,i,p));case\"creation\":return n(()=>GN(a,i,p));case\"dynamic\":return HN(a,i,p);case\"evaluation\":return n(()=>KN(a,i,p));case\"image\":return n(()=>XN(a,i,p));case\"graph\":return n(()=>qN(a,i,p));case\"logical\":return n(()=>YN(a,i,p));case\"matrices\":return n(()=>QN(a,i,p));case\"normalization\":return n(()=>ZN(a,i,p));case\"ragged\":return n(()=>JN(a,i,p));case\"reduction\":return n(()=>eT(a,i,p));case\"slice_join\":return n(()=>tT(a,i,p));case\"sparse\":return n(()=>rT(a,i,p));case\"spectral\":return n(()=>oT(a,i,p));case\"string\":return n(()=>nT(a,i,p));case\"transformation\":return n(()=>sT(a,i,p));case\"hash_table\":return jN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var dl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function nS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((sS(m)||T6(m)||_6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function aT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var v6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],k6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],N6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function sS(r){return v6.indexOf(r.op)>=0}function T6(r){return k6.indexOf(r.op)>=0}function _6(r){return N6.indexOf(r.op)>=0}var ku=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ku(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=nS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return aT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new dl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=EN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new dl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=nS(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=p.filter(C=>!sS(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=as(l.node.name,o)),n[l.node.name]==null){let d=oS(l.node,n,o,this._resourceManager);m||([m]=as(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=as(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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`}function V8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`\n float ${n}() {\n return sampleTexture(${t}, resultUV);\n }\n `;let u=$e(p),c=R$(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];i===0?m=\"\":p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(`\n`);let f=\"\";return p<2&&i>0?f=\"coords\":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(\", \"),`\n float ${n}() {\n ${u} coords = getOutputCoords();\n ${m}\n return get${o}(${f});\n }\n `}function $e(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function 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t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?Pu([\"r\",\"c\",\"d\"],e):ps([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=`\n ${Ff}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var 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vec4(clipSpacePos, 1);\n resultUV = uv;\n }`;return qS(r,t)}function fw(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return QS(r,e)}function hw(r){let e=new Uint16Array([0,1,2,2,1,3]);return ZS(r,e)}function _l(r,e,t,o,n,s){ew(e,t);let a=JS(r),i=r.TEXTURE_2D;return pe(r,()=>r.bindTexture(i,a)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),O().getNumber(\"WEBGL_VERSION\")===1?pe(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)):pe(r,()=>r.texStorage2D(i,1,o,e,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:a,texShape:[t,e]}}function zf(r){return r.internalFormatFloat}function gw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,zf(o),o.textureFormatFloat,r.FLOAT)}function Wf(r){return r.internalFormatHalfFloat}function xw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Wf(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Uf(r){return r.downloadTextureFormat}function yw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Uf(o),r.RGBA,r.UNSIGNED_BYTE)}function Gf(r){return r.internalFormatPackedFloat}function bw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Gf(o),r.RGBA,r.FLOAT)}function Hf(r){return r.internalFormatPackedHalfFloat}function Cw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Hf(o),r.RGBA,o.textureTypeHalfFloat)}function Sw(r,e,t){return pe(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Af(r,e,\"clipSpacePos\",t,3,20,0)&&Af(r,e,\"uv\",t,2,20,12)}function ww(r,e,t,o,n,s){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,p=s.internalFormatPackedFloat),a.set(n),O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t,o,r.RGBA,i,a)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,p,t,o,0,r.RGBA,i,a)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Iw(r,e,t){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t.width,t.height,r.RGBA,r.UNSIGNED_BYTE,t.data)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,t)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function vw(r,e,t,o){let n=r.createBuffer();pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return pe(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function kw(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function Nw(r,e,t,o){let[n,s]=Ou(e,t),a=4,i=new Uint8Array(k$(e*t,a));return pe(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function Tw(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(N$(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function _w(r,e,t){let o=new Float32Array(e*t*4);return pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Lu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,US(t,e)):this.gl=zr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>pe(s,()=>s.createVertexArray()),this.bindVertexArray=a=>pe(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>pe(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>pe(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>pe(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>pe(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>pe(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>pe(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=tc(this.gl,s),Wr(this.gl,a))this.textureHalfFloatExtension=tc(this.gl,a);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Wr(this.gl,n))this.colorBufferHalfFloatExtension=tc(this.gl,n);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",Wr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=fw(this.gl),this.indexBuffer=hw(this.gl),this.framebuffer=tw(this.gl),this.textureConfig=vl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),gw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),xw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),yw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Iw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),ww(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),bw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Nw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return Tw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=vw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>_w(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=dw(t));let o=XS(t);pe(t,()=>t.attachShader(o,this.vertexShader)),pe(t,()=>t.attachShader(o,e)),YS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),pe(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(Sw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&kl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(pe(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&kl(this.gl,this.program)),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?rw(this.gl,e,t):ow(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),nw(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Qs(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&kl(this.gl,this.program),rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=tc(this.gl,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=G8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Nl(this.gl,e,this.framebuffer),this.debug&&rc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Nl(this.gl,this.outputTexture,this.framebuffer),this.debug&&rc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Nl(n,e,this.framebuffer),this.debug&&rc(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,o)),pe(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function G8(r){let e=0;for(;e`${r}.${t}`)}function Et(r,e){return e===1?[r]:Ew(r,e)}function $E(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${H8(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?ac():sc(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function H8(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?A$([\"r\",\"c\",\"d\"],\"inputShape\"):ps([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=AE(t,o),s=RE(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=EE(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=AE(o,n),a=RE(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=EE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function K8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function EE(r,e,t,o,n){let s=q8(e,o),a;if(n){let[p,u]=Qs(r[0],r[1]);a=p*u}else{let[p,u]=Ou(r[0],r[1]);a=p*u}let i=K8(t,s);return a*i}function q8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function j8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function AE(r,e){if(r===ur.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ur.RENDER||r==null)return j8(e);if(r===ur.DOWNLOAD||r===ur.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function RE(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},zt=\"if (isnan(x)) return x;\",DE=\"return x;\",Aw=\"return abs(x);\";var FE=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",OE=zt+`\n return (x < 0.0) ? 0.0 : x;\n`,PE=zt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Zs=\"return x;\",ME=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BE=\"return x;\",VE=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,zE=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,WE=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,UE=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Er=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.length,o=Et(\"rc\",t),n=$e(t),s=$E(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var Y8=Vt.whereImpl,Q8=1e-7,Z8=1e-4,Zf={};function J8(r){return r in Zf||(Zf[r]={}),Zf[r]}var eY=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),tY=600;function rY(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*tY/1024/1024}var Vi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Lu)t=e;else{let o=zr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Lu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=zr(O().getNumber(\"WEBGL_VERSION\"));t=new Lu(o),this.binaryCache=J8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=rY(),this.texData=new Fo(this,sr())}nextDataId(){return Vi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=oc(t),c=new Tl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ur.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Er(i,Zs):m=new Jt(i,Zs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Er(n,Zs):f=new Jt(n,Zs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Il(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;pe(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Er(s,Zs):d=new Jt(s,Zs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=sr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=eY){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ua(e.shape),...Ga(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ua(t),...Ga(t)],a=new mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=oc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Il(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Li.DENSE){let x=a!=null?a:Il(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Bi(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=B$(e,c,l),d=this.getAndSaveBinary(m,()=>M$(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||L$(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Q8:Z8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=sw(o,p),t.texShape=l),s!=null){let m=oc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Qs(l[0],l[1])),p?d=new Vf(m,g):d=new Tl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ur.PIXELS:C.usage=ur.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=oY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await AC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Ef(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of 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Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return sr().makeTensorFromDataId(u,t,o,p)}};Vi.nextDataId=0;function oY(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Vi,2);var CJe={forceHalfFloat:GE};var dc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Js=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var _o=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=lt(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${$e(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Et(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HE={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Ar(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KE={kernelName:ri,backendName:\"webgl\",kernelFunc:Ar};var Rw=\"return (a < 0.) ? b * a : a;\",Dw=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Dw,n.shape,a.shape):new io(Rw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qE={kernelName:ln,backendName:\"webgl\",kernelFunc:sY};var Fw=\"return (a < 0.) ? b * a : a;\",Ow=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function aY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Ow,o.shape,n.shape):new io(Fw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jE={kernelName:Rn,backendName:\"webgl\",kernelFunc:aY};var $o=\"if (isnan(x)) return x;\";function he({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Er(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,E],dt(w.dtype,k.dtype))}),b=Ar({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new _o(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ha(r,e=!1){if(r===\"linear\")return e?BE:DE;if(r===\"relu\")return e?zE:OE;if(r===\"elu\")return e?VE:FE;if(r===\"relu6\")return e?WE:PE;if(r===\"prelu\")return e?Ow:Fw;if(r===\"leakyrelu\")return e?Dw:Rw;if(r===\"sigmoid\")return e?UE:ME;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var fc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=lt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Bi(n.shape,p)&&!(c.texture!==null&&Bi(c.shape,p))?QE(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZE={kernelName:_s,backendName:\"webgl\",kernelFunc:re};var Al=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || 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);\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function uY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Ur(r,e,t,o){let n=uY(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=$e(this.rank),s=Ew(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=re({inputs:{x:r},backend:n,attrs:{shape:k}}),A=re({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,V=p===\"leakyrelu\",z=p!=null?Ha(p,!0):null,U=M||L||V||z!=null,K;if((d===1||f===1)&&P>Mw&&U===!1){let q=E,Z=A;t&&(q=yt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),R.push(q)),o&&(Z=yt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(Z));let ee=f!==1,oe=f===1,J=q;ee&&(J=re({inputs:{x:q},backend:n,attrs:{shape:[D,P,1]}}),R.push(J));let te=f===1?2:1,ie=Z;oe&&(ie=re({inputs:{x:Z},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let ce=El({inputs:{a:J,b:ie},backend:n});K=Vu({inputs:{x:ce},backend:n,attrs:{axis:te,keepDims:!0}}),R.push(ce)}else{let q=dt(r.dtype,e.dtype),Z=new fc(k,_,[D,d,f],t,o,M,z,L,V),ee=[E,A];if(s!=null&&ee.push(s),L&&ee.push(a),V){let oe=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));ee.push(oe),R.push(oe)}K=n.runWebGLProgram(Z,ee,q)}let H=re({inputs:{x:K},backend:n,attrs:{shape:w}});R.push(K);for(let q of R)n.disposeIntermediateTensorInfo(q);return H}function cY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return zu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rA={kernelName:ho,backendName:\"webgl\",kernelFunc:cY};var oA=\"return abs(x);\";function lY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=qf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Er(o.shape,oA):n=new Jt(o.shape,oA),t.runWebGLProgram(n,[o],o.dtype)}var nA={kernelName:ys,backendName:\"webgl\",kernelFunc:lY};var mY=zt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,dY=he({opSnippet:mY}),sA={kernelName:aa,backendName:\"webgl\",kernelFunc:dY};var fY=zt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,hY=he({opSnippet:fY}),aA={kernelName:ia,backendName:\"webgl\",kernelFunc:hY};var iA=\"return a + b;\",gY=tt({opSnippet:iA,packedOpSnippet:iA,supportsComplex:!0,cpuKernelImpl:V$}),uA={kernelName:eo,backendName:\"webgl\",kernelFunc:gY};var rh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var oh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Po,backendName:\"webgl\",kernelFunc:nh};function xY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=yt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=re({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Ur(h,h.dtype,\"all\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=re({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=re({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Mo,backendName:\"webgl\",kernelFunc:xY};function 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i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=$e(p),c=Et(\"coords\",p),l,m;if(a===1){m=p+1;let A=$e(m);l=`\n ${A} sourceLocR = ${A}(${c.join()}, 0);\n ++${c[p-1]};\n ${A} sourceLocG = ${A}(${c.join()}, 0);\n ++${c[p-2]};\n ${A} sourceLocA = ${A}(${c.join()}, 0);\n --${c[p-1]};\n ${A} sourceLocB = ${A}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=Et(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Et(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Et(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Et(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? 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n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=re({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=re({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function bY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Bo,backendName:\"webgl\",kernelFunc:bY};function CY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:ei,backendName:\"webgl\",kernelFunc:CY};var SY=zt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,wY=he({opSnippet:SY}),gA={kernelName:ua,backendName:\"webgl\",kernelFunc:wY};var IY=zt+\"return log(x + sqrt(x * x + 1.0));\",vY=he({opSnippet:IY}),xA={kernelName:pa,backendName:\"webgl\",kernelFunc:vY};var kY=zt+`\n return atan(x);\n`,NY=he({opSnippet:kY}),yA={kernelName:ca,backendName:\"webgl\",kernelFunc:NY};var TY=dc+`\n return atan(a, b);\n`,_Y=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = 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A=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. 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If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,A=a%4,R=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${w};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${w});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${E}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${R}\n }\n\n int xC = xCCorner + ${E};\n if (${A===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${R}\n }\n }\n }\n setOutput(${_});\n }\n `}};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function OY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;us([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:mp,backendName:\"webgl\",kernelFunc:OY};function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return zu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:zo,backendName:\"webgl\",kernelFunc:PY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var lh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var MY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:sn,backendName:\"webgl\",kernelFunc:MY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let 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fh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var hh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;O().getBool(\"WEBGL_PACK_CLIP\")?i=new hh(n.shape):i=new fh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var MA={kernelName:lo,backendName:\"webgl\",kernelFunc:KY};var gh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function LA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function qY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[LA(o,n.complexTensorInfos.real),LA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var BA={kernelName:hp,backendName:\"webgl\",kernelFunc:qY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${yh(i,u,g)}),\n vec2(${yh(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${yh(i,u,f)}),\n vec2(${yh(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Wu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:ci,backendName:\"webgl\",kernelFunc:Wu};function hc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ka({inputs:{input:b},backend:t})),f=r.map(b=>Wu({inputs:{input:b},backend:t})),h=hc(d,e,t),g=hc(f,e,t),x=Ar({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return re({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=G$(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Zs):new Er(r[0].shape,Zs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=jY(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=re({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function jY(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>re({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function zw(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):hc(p,s,t)}var zA={kernelName:Cs,backendName:\"webgl\",kernelFunc:zw};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?w=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:w=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${w}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Mw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Bi(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let A=zu({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=re({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=zu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=re({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=re({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",E),R=re({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Ha(i,!0):null,V=new fc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),z=f?[R,k]:[k,R];if(n&&z.push(n),P&&z.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(H),w.push(H)}let U=o.runWebGLProgram(V,z,\"float32\"),K=re({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return K}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new xc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new gc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=re({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var WA={kernelName:Uo,backendName:\"webgl\",kernelFunc:XY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${a}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var UA={kernelName:oi,backendName:\"webgl\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Go,backendName:\"webgl\",kernelFunc:QY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var HA={kernelName:gp,backendName:\"webgl\",kernelFunc:ZY};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:km,backendName:\"webgl\",kernelFunc:JY};function eQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:xp,backendName:\"webgl\",kernelFunc:eQ};var tQ=$o+`\n return cos(x);\n`,rQ=he({opSnippet:tQ}),jA={kernelName:Ho,backendName:\"webgl\",kernelFunc:rQ};var oQ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,nQ=he({opSnippet:oQ}),XA={kernelName:Ko,backendName:\"webgl\",kernelFunc:nQ};var $h=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${w};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var sQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new $h(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},YA={kernelName:Xo,backendName:\"webgl\",kernelFunc:sQ};var Uu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Uu||(Uu={}));var Rl=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Uu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${QA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${$e(s)} coords = getOutputCoords();\n int end = ${ZA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${ZA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${QA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function QA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Eh(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=yt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Rl(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new Rl(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=yt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function aQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Prod,n,t,s,a,i)}var JA={kernelName:qo,backendName:\"webgl\",kernelFunc:aQ};function iQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Sum,n,t,s,a,i)}var eR={kernelName:jo,backendName:\"webgl\",kernelFunc:iQ};function uQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Kf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=z$(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var tR={kernelName:ni,backendName:\"webgl\",kernelFunc:uQ};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function pQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var rR={kernelName:Yo,backendName:\"webgl\",kernelFunc:pQ};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var bc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new bc(l):m=new yc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var oR={kernelName:Qo,backendName:\"webgl\",kernelFunc:cQ};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Dh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function lQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var nR={kernelName:yp,backendName:\"webgl\",kernelFunc:lQ};function mQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Dh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sR={kernelName:bp,backendName:\"webgl\",kernelFunc:mQ};var Fh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function dQ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=re({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Fh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=re({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var aR={kernelName:si,backendName:\"webgl\",kernelFunc:dQ};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function fQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=re({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var iR={kernelName:ai,backendName:\"webgl\",kernelFunc:fQ};function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Vu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var uR={kernelName:ii,backendName:\"webgl\",kernelFunc:hQ};var gQ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",xQ=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,yQ=he({opSnippet:gQ,packedOpSnippet:xQ}),pR={kernelName:Jo,backendName:\"webgl\",kernelFunc:yQ};var bQ=\"return (b >= 1.0) ? a : a * (b + 1.0);\",CQ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,SQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(CQ,o.shape,n.shape):new io(bQ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},cR={kernelName:Nm,backendName:\"webgl\",kernelFunc:SQ};var wQ=`\n return vec4(equal(a, b));\n`,IQ=\"return float(a == b);\",vQ=tt({opSnippet:IQ,packedOpSnippet:wQ,dtype:\"bool\",cpuKernelImpl:H$}),lR={kernelName:en,backendName:\"webgl\",kernelFunc:vQ};var kQ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,NQ=he({opSnippet:kQ}),mR={kernelName:da,backendName:\"webgl\",kernelFunc:NQ};var TQ=$o+`\n return exp(x);\n`,_Q=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ww=he({opSnippet:TQ,packedOpSnippet:_Q,cpuKernelImpl:K$,dtype:\"float32\"}),dR={kernelName:tn,backendName:\"webgl\",kernelFunc:Ww};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),re({inputs:{x:s},backend:o,attrs:{shape:i}})}var fR={kernelName:Ss,backendName:\"webgl\",kernelFunc:Ph};var hR=\"return exp(x) - 1.0;\",$Q=he({opSnippet:hR,packedOpSnippet:hR,cpuKernelImpl:q$}),gR={kernelName:fa,backendName:\"webgl\",kernelFunc:$Q};var Dl=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; 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row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var IR={kernelName:Zi,backendName:\"webgl\",kernelFunc:OQ},Cc,Uw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function OQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Cc==null||h!==Uw)&&(Uw=h,Cc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Uw})),Cc.canvas.width=p,Cc.canvas.height=u,Cc.drawImage(n,0,0,p,u),n=Cc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ur.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function PQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=re({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return A};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Ha(d,!0):null,R=new xc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Ha(d,!1):null,R=new gc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let E=re({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),E}var vR={kernelName:go,backendName:\"webgl\",kernelFunc:PQ};function MQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=$e(this.rank),n=BQ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function BQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=re({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=re({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=Y$(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=re({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var TR={kernelName:Is,backendName:\"webgl\",kernelFunc:Gw};var VQ=\"return float(a > b);\",zQ=`\n return vec4(greaterThan(a, b));\n`,WQ=tt({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:Q$,dtype:\"bool\"}),_R={kernelName:un,backendName:\"webgl\",kernelFunc:WQ};var UQ=\"return float(a >= b);\",GQ=`\n return vec4(greaterThanEqual(a, b));\n`,HQ=tt({opSnippet:UQ,packedOpSnippet:GQ,dtype:\"bool\",cpuKernelImpl:Z$}),$R={kernelName:pn,backendName:\"webgl\",kernelFunc:HQ};function KQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var ER={kernelName:pi,backendName:\"webgl\",kernelFunc:KQ};var qQ=\"return float(!isnan(x) && !isinf(x));\",jQ=he({opSnippet:qQ,dtype:\"bool\"}),AR={kernelName:ha,backendName:\"webgl\",kernelFunc:jQ};var XQ=\"return float(isinf(x));\",YQ=he({opSnippet:XQ,dtype:\"bool\"}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:YQ};var QQ=\"return float(isnan(x));\",ZQ=he({opSnippet:QQ,dtype:\"bool\"}),DR={kernelName:cn,backendName:\"webgl\",kernelFunc:ZQ};var JQ=\"return float(a < b);\",e7=`\n return vec4(lessThan(a, b));\n`,t7=tt({opSnippet:JQ,packedOpSnippet:e7,cpuKernelImpl:J$,dtype:\"bool\"}),FR={kernelName:mn,backendName:\"webgl\",kernelFunc:t7};var r7=\"return float(a <= b);\",o7=`\n return vec4(lessThanEqual(a, b));\n`,n7=tt({opSnippet:r7,packedOpSnippet:o7,cpuKernelImpl:eE,dtype:\"bool\"}),OR={kernelName:dn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=tE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var PR={kernelName:li,backendName:\"webgl\",kernelFunc:s7};var a7=$o+`\n return x < 0.0 ? 0./0. : log(x);\n`,i7=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,u7=he({opSnippet:a7,packedOpSnippet:i7,cpuKernelImpl:rE}),MR={kernelName:fn,backendName:\"webgl\",kernelFunc:u7};var p7=$o+`\n return log(1.0 + x);\n`,c7=he({opSnippet:p7}),LR={kernelName:xa,backendName:\"webgl\",kernelFunc:c7};var l7=\"return float(a >= 1.0 && b >= 1.0);\",m7=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,d7=tt({opSnippet:l7,packedOpSnippet:m7,dtype:\"bool\"}),BR={kernelName:hn,backendName:\"webgl\",kernelFunc:d7};var f7=\"return float(!(x >= 1.0));\",h7=he({opSnippet:f7}),VR={kernelName:gn,backendName:\"webgl\",kernelFunc:h7};var g7=\"return float(a >= 1.0 || b >= 1.0);\",x7=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,y7=tt({opSnippet:g7,packedOpSnippet:x7,dtype:\"bool\"}),zR={kernelName:xn,backendName:\"webgl\",kernelFunc:y7};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var b7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},WR={kernelName:mi,backendName:\"webgl\",kernelFunc:b7};var Kh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var C7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},UR={kernelName:Tm,backendName:\"webgl\",kernelFunc:C7};function GR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=re({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Ur(i,r.dtype,\"max\",o),u=re({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Hw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var qR={kernelName:Cn,backendName:\"webgl\",kernelFunc:v7};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Wi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var jR={kernelName:Cp,backendName:\"webgl\",kernelFunc:k7};var qh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 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eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=$e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Et(\"rc\",n),u=Et(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return qa({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},bD={kernelName:En,backendName:\"webgl\",kernelFunc:Xw};var Y7=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Q7=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=S.expandShapeToKeepDim(d.shape,u);d=re({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var SD={kernelName:Dn,backendName:\"webgl\",kernelFunc:J7};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=cE(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var wD={kernelName:Ip,backendName:\"webgl\",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lE(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var ID={kernelName:vp,backendName:\"webgl\",kernelFunc:tZ};function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=mE(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var vD={kernelName:kp,backendName:\"webgl\",kernelFunc:rZ};var Yw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=dE(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},kD={kernelName:Ts,backendName:\"webgl\",kernelFunc:Yw};var oZ=\"return 1.0 / x;\",nZ=he({opSnippet:oZ}),ND={kernelName:Fn,backendName:\"webgl\",kernelFunc:nZ};var sZ=zt+`\n return (x < 0.0) ? 0.0 : x;\n`,aZ=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,iZ=he({opSnippet:sZ,packedOpSnippet:aZ}),TD={kernelName:On,backendName:\"webgl\",kernelFunc:iZ};var uZ=zt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,pZ=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,cZ=he({opSnippet:uZ,packedOpSnippet:pZ}),_D={kernelName:Ln,backendName:\"webgl\",kernelFunc:cZ};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $D={kernelName:Mn,backendName:\"webgl\",kernelFunc:lZ};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function mZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var ED={kernelName:Am,backendName:\"webgl\",kernelFunc:mZ};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var AD={kernelName:Pn,backendName:\"webgl\",kernelFunc:dZ};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function fZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var RD={kernelName:Em,backendName:\"webgl\",kernelFunc:fZ};var ig=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=$e(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Et(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=$e(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var DD={kernelName:Bn,backendName:\"webgl\",kernelFunc:hZ};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var FD={kernelName:ts,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var gZ=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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CZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=re({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=re({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Sc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=re({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var MD={kernelName:Wn,backendName:\"webgl\",kernelFunc:CZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=bE(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var jD={kernelName:va,backendName:\"webgl\",kernelFunc:LZ};function BZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var XD={kernelName:gi,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 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fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=re({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Gu(t,d);let x=mF(s),b=mF(c),C=null,w=()=>C===null?[g,g]:[g,C],k=(D,P,M)=>{let L=w(),V=new fg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[D],[P]],K=C;C=t.runWebGLProgram(V,L,\"int32\",U),Gu(t,K)};for(let D=1;D=1;M/=2)k(P,M,[h,b])}for(let D=b;D>x;D/=2){let P=w(),M=new hg([h,D/2]),V=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,P,\"int32\",V),Gu(t,z);let U=x/2,K=U*2;for(let H=U;H>=1;H/=2)k(K,H,C.shape)}let _=C;C=ls({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),Gu(t,_);let E=Gw({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});Gu(t,g);let A=u.slice(0,-1);A.push(s),_=C,C=re({inputs:{x:C},attrs:{shape:A},backend:t}),Gu(t,_);let R=E;return E=re({inputs:{x:E},attrs:{shape:A},backend:t}),Gu(t,R),[E,C]}var dF={kernelName:Jn,backendName:\"webgl\",kernelFunc:o9};var gg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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hF={kernelName:Np,backendName:\"webgl\",kernelFunc:s9};function a9(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var gF={kernelName:Fs,backendName:\"webgl\",kernelFunc:a9};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n 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segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function 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Ui;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(Ui||(Ui={}));var yF;function p9(r){yF=r.wasm.cwrap(ho,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function c9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let A=t.dataIdMap.get(a.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);f=A.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Ui[c];if(g==null)throw new Error(`${c} activation 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res.old_value;\n }\n }`;var M3=(r,e,t,o)=>{let n={dtype:o.dtype,shape:o.shape},s=Yee(t,n,e),a=r.createShaderModule({code:s,label:e.constructor.name});return r.createComputePipeline({compute:{module:a,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})};function Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ao(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function Q(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function D3(r,e){let t;return t=`\n ${Xee(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function Xee(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function Yee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${B3(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${vc(e.dtype,t.isVec4)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=P3(t);return[F3,o.join(`\n`),O3(e.shape),t.getUserCode(),D3(f,t)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=nte(s),o.push(s),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${vc(e.dtype,t.isVec4)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:vc(r[h].dtype,t.isVec4)}>;\n `)}),s!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=tte(e.shape,t.dispatchLayout),c=[F3,o.join(`\n`)+Qee,O3(e.shape),u,rte(e.shape.length)];t.atomic||c.push(ote(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>ete(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=P3(t);return c.push(D3(m,t)),c.join(`\n`)}function L3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=B3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var F3=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,Qee=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function O3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 {\n let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n return vec2(d0, d1);\n }`;let s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`;return`${p}; ${u};`}).join(\"\"),`\n fn getCoordsFromIndex(index : i32) -> ${o} {\n ${s}\n return ${o}(${n.join(\",\")});\n }\n `}function Zee(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n fn ${s}() -> vec4 {\n return vec4(${t}[0]);\n }\n `:`\n fn ${s}() ->f32 {\n return f32(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n fn ${s}(${i}) -> vec4 {\n return vec4(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p}) / 4]);\n }\n `:`\n fn ${s}(${i}) -> f32 {\n return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})]);\n }\n `}function Jee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return vec4(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return vec4(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n return f32(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> f32 {\n return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n }\n `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return get${s}();\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> f32{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Ao(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${Ao(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> vec4 {\n var coords = coordsIn;\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> f32 {\n var coords = coordsIn;\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n`}function ete(r,e,t,o){let n=Zee(r,t);return r.shape.length<=e.length&&(n+=Jee(r,e,t,o)),n}function tte(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function rte(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function B3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function vc(r,e){return r===\"float32\"?e?\"vec4\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4\":\"i32\":r}function ote(r,e,t){let o=r.length,n=vc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n result[flatIndex] = ${n}(value);\n }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex / 4, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex / 4, value);\n }\n `:s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex, value);\n }\n `}return s}function nte(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function P3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var hI={};Ge(hI,{GPUBytesPerElement:()=>_g,MatMulProgramType:()=>Ro,assertNotComplex:()=>fI,computeDispatch:()=>Y,computeWorkPerThreadForConv2d:()=>Vl,computeWorkgroupInfoForMatMul:()=>dI,computeWorkgroupSizeForConv2d:()=>Bl,flatDispatchLayout:()=>ae,isWebGPUSupported:()=>zl,tilesFitEvenlyIntoShape:()=>ate});var Ku=r=>{let e=1;for(let t=0;tt%r[o]===0)}function Y(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ku(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ku(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ku(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function dI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Bl(r,e,t=!1){if(t)return[8,8,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Vl(r,e,t=!1){if(t)return[4,4,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ae(r){return{x:r.map((e,t)=>t)}}function _g(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function zl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function fI(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Ro;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ro||(Ro={}));var ite=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),ute=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Gi=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!zl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Fo(this,sr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Gi.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=_g(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=_g(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type===\"int32\"?new Int32Array(a,l,u.data.length).set(u.data):u.type===\"uint32\"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=ute(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=L3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=M3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=ite){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Gi(n,s)},3);var ge;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.LOGICAL_OR=12]=\"LOGICAL_OR\",r[r.MAX=13]=\"MAX\",r[r.MIN=14]=\"MIN\",r[r.MOD=15]=\"MOD\",r[r.MUL=16]=\"MUL\",r[r.NOT_EQUAL=17]=\"NOT_EQUAL\",r[r.POW=18]=\"POW\",r[r.PRELU=19]=\"PRELU\",r[r.SQUARED_DIFFERENCE=20]=\"SQUARED_DIFFERENCE\",r[r.SUB=21]=\"SUB\"})(ge||(ge={}));var V3=`\n if (isnan(a)) { return a; }\n if (isnan(b)) { return b; }\n `,$g=`\n resultTemp = select(\n resultTemp, vec4(valueForNaN),\n vec4(isNaN) | isnanVec4(a) | isnanVec4(b));\n `,pte=\"return a + b;\",cte=\"return areal * breal - aimag * bimag;\",lte=\"return areal * bimag + aimag * breal;\",mte=\"return a / b;\",dte=\"return f32(a == b);\",fte=\"return vec4(a == b);\",hte=\"return f32(a > b);\",gte=\"return vec4(a > b);\",xte=\"return f32(a >= b);\",yte=\"return vec4(a >= b);\",bte=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,Cte=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,Ste=\"return f32(a < b);\",wte=\"return vec4(a < b);\",Ite=\"return f32(a <= b);\",vte=\"return vec4(a <= b);\",kte=\"return f32(a >= 1.0 && b >= 1.0);\",Nte=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Tte=\"return f32(a >= 1.0 || b >= 1.0);\",_te=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,$te=`\n ${V3}\n if (b == 0.) {\n return uniforms.NAN;\n }\n var resultTemp = a % b;\n if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n return resultTemp;\n } else {\n return (resultTemp + b) % b;\n }\n`,Ete=`\n let isNaN = !vec4(b);\n let valueForNaN = uniforms.NAN;\n var resultTemp = vec4(a % b);\n ${$g}\n\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n\n return resultTemp;\n`,Ate=\"return a * b;\",Rte=`\n if (isnan(a) || isnan(b)) {\n return 1.0;\n }\n return f32(a != b);\n`,Dte=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n ${$g}\n\n return resultTemp;\n`,Fte=`\n if(a < 0.0 && floor(b) < b) {\n return uniforms.NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n if (round(abs(b) % 2.0) != 1.0) {\n return pow(abs(a), b);\n }\n return sign(a) * pow(abs(a), b);\n`,Ote=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n let valueForNaN = uniforms.NAN;\n ${$g}\n return resultTemp;\n`,Pte=\"if (a < 0.0) { return b * a; } return a;\",Mte=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Lte=\"return (a - b) * (a - b);\",Bte=\"return a - b;\";function gI(r,e,t=\"uniforms.NAN\"){let o=e?$g:V3;return e?`\n let valueForNaN = ${t};\n var resultTemp = vec4(${r}(a, b));\n `+o+`\n return resultTemp;\n `:o+`\n return ${r}(a, b);\n `}function kc(r,e){switch(r){case ge.ADD:return pte;case ge.ATAN2:return gI(\"atan2\",e);case ge.COMPLEX_MULTIPLY_IMAG:return lte;case ge.COMPLEX_MULTIPLY_REAL:return cte;case ge.DIV:return mte;case ge.EQUAL:return e?fte:dte;case ge.GREATER:return e?gte:hte;case ge.GREATER_EQUAL:return e?yte:xte;case ge.INT_DIV:return e?Cte:bte;case ge.LESS:return e?wte:Ste;case ge.LESS_EQUAL:return e?vte:Ite;case ge.LOGICAL_AND:return e?Nte:kte;case ge.LOGICAL_OR:return e?_te:Tte;case ge.MAX:return gI(\"max\",e);case ge.MIN:return gI(\"min\",e);case ge.MOD:return e?Ete:$te;case ge.MUL:return Ate;case ge.NOT_EQUAL:return e?Dte:Rte;case ge.POW:return e?Ote:Fte;case ge.PRELU:return e?Mte:Pte;case ge.SQUARED_DIFFERENCE:return Lte;case ge.SUB:return Bte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var X;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(X||(X={}));var Vte=\"return abs(a);\",zte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Wte=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,Ute=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,Gte=\"return asinh(a);\",Hte=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,Kte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,qte=\"return ceil(a);\",jte=\"return cos(a);\",Xte=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Yte=\"return exp(a) - 1.0;\",Qte=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",Zte=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,Jte=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${S.ERF_P};\n let a1 = ${S.ERF_A1};\n let a2 = ${S.ERF_A2};\n let a3 = ${S.ERF_A3};\n let a4 = ${S.ERF_A4};\n let a5 = ${S.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,ere=\"return exp(a);\",tre=\"return floor(a);\",rre=\"return f32(!isnan(a) && !isinf(a));\",ore=\"return f32(isinf(a));\",nre=\"return f32(isnan(a));\",sre=\"return a;\",are=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,ire=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,ure=\"return f32(!(a >= 1.0));\",pre=\"return -a;\",cre=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",lre=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,mre=\"return 1.0 / a;\",dre=\"return select(a, 0.0, a < 0.0);\",fre=\"return clamp(a, 0.0, 6.0);\",hre=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",gre=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,xre=\"return round(a);\",yre=\"return inverseSqrt(a);\",bre=`\n if (a >= 0.0) {\n return ${S.SELU_SCALE} * a;\n } else {\n return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Cre=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Sre=\"return sign(a);\",wre=\"return sin(a);\",Ire=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,vre=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,kre=\"return sqrt(a);\",Nre=\"return a * a;\",Tre=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,_re=\"return tan(a);\",$re=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Ere=\"return f32(i32((a)));\";function ja(r,e){switch(r){case X.ABS:return Vte;case X.ACOS:return zte;case X.ACOSH:return Wte;case X.ASIN:return Ute;case X.ASINH:return Gte;case X.ATAN:return Hte;case X.ATANH:return Kte;case X.COS:return jte;case X.COSH:return Xte;case X.CEIL:return qte;case X.ELU:return e?Zte:Qte;case X.ERF:return Jte;case X.EXP:return ere;case X.EXPM1:return Yte;case X.FLOOR:return tre;case X.IS_FINITE:return rre;case X.IS_INF:return ore;case X.IS_NAN:return nre;case X.LINEAR:return sre;case X.LOG:return are;case X.LOG1P:return ire;case X.LOGICAL_NOT:return ure;case X.NEG:return pre;case X.LEAKYRELU:return e?lre:cre;case X.RECIPROCAL:return mre;case X.RELU:return e?gre:dre;case X.RELU6:return e?hre:fre;case X.ROUND:return xre;case X.RSQRT:return yre;case X.SELU:return bre;case X.SIGMOID:return Cre;case X.SIGN:return Sre;case X.SIN:return wre;case X.SINH:return Ire;case X.SOFTPLUS:return vre;case X.SQRT:return kre;case X.SQUARE:return Nre;case X.STEP:return Tre;case X.TAN:return _re;case X.TANH:return $re;case X.TO_INT:return Ere;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error(`${r}-component is not supported.`)}};function pr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=ja(X.LINEAR);else if(r===\"relu\")n=ja(X.RELU,t);else if(r===\"elu\")n=ja(X.ELU,t);else if(r===\"relu6\")n=ja(X.RELU6,t);else if(r===\"prelu\")n=kc(ge.PRELU,t);else if(r===\"sigmoid\")n=ja(X.SIGMOID,t);else if(r===\"leakyrelu\")n=ja(X.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Gr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function xI(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n var value = ${kt(s)}(0.0);\n let col = colIn * ${s};\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n let col = colIn * ${s};\n var value = ${kt(s)}(0.0);\n ${i}\n return value;\n }\n `}function Wl(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${xI(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) {\n let col = colIn * ${i};\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Gr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Are=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart / ${e} + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart / ${e} + inputCol);\n `,Rre=(r,e,t)=>r?`\n let ACached0 = mm_Asub[k * ${e}][localRow];\n let ACached1 = mm_Asub[k * ${e} + 1][localRow];\n let ACached2 = mm_Asub[k * ${e} + 2][localRow];\n ${e===3?\"\":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}\n for (var i = 0; i < ${t}; i++) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n }`:`\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n }`;function qu(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=e[1]*r[1],u=e[0]*r[0],c=t?p:o,l=t?o:p,m=c/e[0],d=o/e[1],f=r[1];return y.assert((t&&m===4&&r[1]===4||!t&&(m===3||m===4))&&c%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${m} must be 3 or 4.\n tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${c/m}>, ${l}>;\n var mm_Bsub : array, ${u/r[0]}>, ${o}>;\n\n ${Q()} {\n let localRow = i32(localId.y);\n let tileRow = ${a?\"0\":`localRow * ${f}`};\n let tileCol = i32(localId.x);\n\n let globalRow = ${a?\"0\":`i32(globalId.y) * ${f}`};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${f}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${d};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Are(t,m)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${o/m}; k++) {\n let BCached0 = mm_Bsub[k * ${m}][tileCol];\n let BCached1 = mm_Bsub[k * ${m} + 1][tileCol];\n let BCached2 = mm_Bsub[k * ${m} + 2][tileCol];\n ${m===3?\"\":`let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`}\n\n ${Rre(t,m,f)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var z3=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Dre=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function ju(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${z3(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${z3(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Dre(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${Q()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Fre=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function Ore(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${Q()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Fre(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Eg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=dI(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${pr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${Wl(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Ore(this.workgroupSize,this.transposeA):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function Pre(r){return`\n var sumValues : array;\n ${Q()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Ag=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${pr(this.activation,this.hasPreluActivationWeights)}\n ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Pre(this.workgroupSize[0])}\n `}};function Mre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${Q()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Rg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${pr(this.activation,this.hasPreluActivationWeights)}\n ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Mre(this.workgroupSize)}\n `}};var Dg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Y(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return`\n ${xI(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) {\n let col = colIn * ${e};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Ic(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Fg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${pr(this.activation,this.hasPreluActivationWeights)}\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Gr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Og=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function fr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Og(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var W3={kernelName:ws,backendName:\"webgpu\",kernelFunc:fr};function me(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var U3={kernelName:_s,backendName:\"webgpu\",kernelFunc:me};function Xu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=me({inputs:{x:r},backend:n,attrs:{shape:k}}),A=me({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=[E,A],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,V,z=[D,d,f],U=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let H=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),q=H>0?H:n.thresholdToIncreaseWorkgroups,Z=D*Math.ceil(d/32)*Math.ceil(f/32);Z<=q||d<=8&&Z<=q*2?D*d*f<=128?U=Ro.MatMulReduceProgram:D===1&&m>=2e3?U=Ro.MatMulSplitKProgram:U=Ro.MatMulSmallOutputSizeProgram:U=Ro.MatMulPackedProgram}switch(U){case Ro.MatMulReduceProgram:L=new Ag(z,t,o,s,p,a);break;case Ro.MatMulSplitKProgram:{if(V=fr({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Dg(z,m,t,o),s||p){V=n.runWebGPUProgram(L,P,r.dtype,M,V);let q=new Fg(V.shape,s,p,a),Z=null,ee=[V];s&&ee.push(s),a&&ee.push(a),p===\"leakyrelu\"&&(Z=[{type:\"float32\",data:[i]}],q.uniforms+=\" alpha : f32,\");let oe=n.runWebGPUProgram(q,ee,V.dtype,Z);R.push(V);let J=me({inputs:{x:oe},backend:n,attrs:{shape:w}});R.push(oe);for(let te of R)n.disposeData(te.dataId);return J}break}case Ro.MatMulSmallOutputSizeProgram:L=new Rg(k,_,z,t,o,s,p,a);break;case Ro.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Eg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&P.push(s),a&&P.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),V=n.runWebGPUProgram(L,P,r.dtype,M,V);let K=me({inputs:{x:V},backend:n,attrs:{shape:w}});R.push(V);for(let H of R)n.disposeData(H.dataId);return K}function Lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Xu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var G3={kernelName:ho,backendName:\"webgpu\",kernelFunc:Lre};var Ul=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${kc(this.op,!1)}\n }\n\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Yu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n let isNaN = false;\n {\n ${kc(this.op,this.isVec4)}\n }\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${Q(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n let b = getBByOutputIndex(index);\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function Dt(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var H3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Dt};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var K3={kernelName:ri,backendName:\"webgpu\",kernelFunc:po};var Hr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${ja(this.op,!1)}\n }\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Hr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function Ze({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ge.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new Ul(ge.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new Ul(ge.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:q3,castImpl:j3,ceilImpl:X3,concatImpl:Y3,equalImpl:Q3,expImpl:Z3,expm1Impl:J3,floorImpl:eM,gatherNdImpl:tM,gatherV2Impl:rM,greaterEqualImpl:oM,greaterImpl:nM,lessEqualImpl:sM,lessImpl:aM,logImpl:iM,maxImpl:uM,maximumImpl:pM,minimumImpl:cM,multiplyImpl:lM,negImpl:mM,notEqualImpl:dM,prodImpl:fM,rangeImpl:hM,rsqrtImpl:gM,scatterImpl:xM,simpleAbsImpl:yM,sliceImpl:bM,stridedSliceImpl:CM,stringNGramsImpl:SM,subImpl:wM,tileImpl:IM,topKImpl:vM,transposeImpl:kM,uniqueImpl:iTt}=Zp;var Bre=xe({opType:X.ABS,cpuKernelImpl:yM}),NM={kernelName:ys,backendName:\"webgpu\",kernelFunc:Bre};var Vre=xe({opType:X.ACOS}),TM={kernelName:aa,backendName:\"webgpu\",kernelFunc:Vre};var zre=xe({opType:X.ACOSH}),_M={kernelName:ia,backendName:\"webgpu\",kernelFunc:zre};var Wre=Ze({opType:ge.ADD,cpuKernelImpl:q3,supportsComplex:!0}),$M={kernelName:eo,backendName:\"webgpu\",kernelFunc:Wre};var Pg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${Q(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function Ure(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Pg(s);return t.runWebGPUProgram(a,o,n)}var EM={kernelName:Po,backendName:\"webgpu\",kernelFunc:Ure};var Mg=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${Q()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var Lg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${Q(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};function Kr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=uM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=fM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":Ta(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Bg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=me({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Hre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"all\",t)}var RM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:Hre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"any\",t)}var DM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Kre};var Nc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ae(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Ao(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${Q(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var FM={kernelName:Bo,backendName:\"webgpu\",kernelFunc:qre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var OM={kernelName:ei,backendName:\"webgpu\",kernelFunc:jre};var Xre=xe({opType:X.ASIN}),PM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Xre};var Yre=xe({opType:X.ASINH}),MM={kernelName:pa,backendName:\"webgpu\",kernelFunc:Yre};var Qre=xe({opType:X.ATAN}),LM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Ze({opType:ge.ATAN2}),BM={kernelName:ma,backendName:\"webgpu\",kernelFunc:Zre};var Jre=xe({opType:X.ATANH}),VM={kernelName:la,backendName:\"webgpu\",kernelFunc:Jre};var Gl=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n var count = 0.0;\n\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, coords[3]);\n ${e}\n }\n }\n\n setOutputAtIndex(index, ${t});\n }\n }\n `}};var Vg=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.stride;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Kr(n,s,a,\"max\",t)}var zM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Hl};function yI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"mean\",t)}var WM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:yI};function zg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Dt({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=me({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=yI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Hl({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=me({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Vg(e):(t===\"avg\"?n=new Gl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Gl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function eoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"avg\",t)}var UM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:eoe};var Wg=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`stride : vec2, pads : vec2, dilation : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avg_pool2d_backprop\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function toe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fI([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new Wg(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var GM={kernelName:mp,backendName:\"webgpu\",kernelFunc:toe};function roe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Xu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var HM={kernelName:zo,backendName:\"webgpu\",kernelFunc:roe};var Ug=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=ooe(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`),`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},bI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function ooe(r){if(r===1)return\"sourceLoc\";if(r<=6)return bI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function fs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=bM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Ug(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KM={kernelName:Es,backendName:\"webgpu\",kernelFunc:fs};var noe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=me({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),x=fs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},qM={kernelName:bs,backendName:\"webgpu\",kernelFunc:noe};var soe=`\n fn bincount_write(index: i32, value: f32) {\n ${Ic(\"&result[index]\",\"value\",\"float32\")}\n }\n`,aoe=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Tc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?aoe:soe}\n ${Q(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=fr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Tc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var jM={kernelName:ti,backendName:\"webgpu\",kernelFunc:ioe};var CI=Ze({opType:ge.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:dM}),XM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:CI};function Xa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var YM={kernelName:di,backendName:\"webgpu\",kernelFunc:Xa};function QM(r,e){let t=new Hr(r.shape,X.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function SI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Br(n.shape),i=SI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Xa({inputs:{input:n},backend:t}),i=SI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=j3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return QM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=CI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var ZM={kernelName:co,backendName:\"webgpu\",kernelFunc:SI};var uoe=xe({opType:X.CEIL,cpuKernelImpl:X3}),JM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:uoe};var Gg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var Hg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function poe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new Gg(n.shape):i=new Hg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var eL={kernelName:lo,backendName:\"webgpu\",kernelFunc:poe};var Kg=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sXa({inputs:{input:C},backend:t})),h=r.map(C=>Qu({inputs:{input:C},backend:t})),g=_c(f,e,t),x=_c(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return me({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=Y3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Kg(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=me({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function coe(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function wI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):_c(p,s,t)}var rL={kernelName:Cs,backendName:\"webgpu\",kernelFunc:wI};function loe(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${x} % inChannels;\n var resData = ${kt(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${kt(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),E=r?kt(p):kt(i);return`\n ${pr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?C:w}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {\n ${r?w:C}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Gr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var qg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4\",\"vec4\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):ju(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${loe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var jg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${pr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Gr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${Q(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var Xg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${Q(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n uniforms.pad[1];\n let xCol = offsetX + uniforms.dilation[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function Yg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function moe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=me({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=me({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Yg(s.shape,p);x!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Yg(n.shape,p);x!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Xu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=me({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function doe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],E=new Xg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram(E,[r],r.dtype,A),D=[];D.push(R);let P=me({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Yg(s.shape,C);U!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Yg(n.shape,C);U!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let V=Xu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=me({inputs:{x:V},backend:o,attrs:{shape:t.outShape}});D.push(V);for(let U of D)o.disposeData(U.dataId);return z}function Qg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return moe({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return doe({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new jg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new qg(t,_,E,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=me({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=me({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function foe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return Qg({x:n,filter:s,convInfo:m,backend:o})}var oL={kernelName:Uo,backendName:\"webgpu\",kernelFunc:foe};var Zg=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return`\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n if (${this.isChannelsLast}) {\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n } else {\n let xValue = getDy(batch, d2, idyR, idyC);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Jg=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pad : vec2, stride : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function hoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Jg(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var nL={kernelName:oi,backendName:\"webgpu\",kernelFunc:hoe};function goe(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${kt(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${kt(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${kt(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${kt(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var ex=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize):ju(this.elementsPerThread,this.workgroupSize);return`\n ${goe(this.isVec4?4:1)}\n ${e}\n `}};function xoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Zg(m);else{f=new ex(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var sL={kernelName:Go,backendName:\"webgpu\",kernelFunc:xoe};var yoe=xe({opType:X.COS}),aL={kernelName:Ho,backendName:\"webgpu\",kernelFunc:yoe};var boe=xe({opType:X.COSH}),iL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:boe};var tx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Coe=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new tx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},uL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Coe};var Zu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Zu||(Zu={}));var Kl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Zu.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${pL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${cL(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${cL(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${pL(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function pL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function cL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function rx(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Kl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Kl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Soe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Prod,n,t,s,a,i)}var lL={kernelName:qo,backendName:\"webgpu\",kernelFunc:Soe};function woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Sum,n,t,s,a,i)}var mL={kernelName:jo,backendName:\"webgpu\",kernelFunc:woe};function Ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=fr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Tc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var dL={kernelName:ni,backendName:\"webgpu\",kernelFunc:Ioe};var ox=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new ox(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var fL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:voe};var nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${Q()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pad;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${pr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${Q()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pad;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Gr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var Ec=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilation[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilation[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Gr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new $c(d):(g=new Ec(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var hL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:koe};var sx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=me({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new sx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=me({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var gL={kernelName:si,backendName:\"webgpu\",kernelFunc:Noe};var ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilation[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilation[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function Toe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var xL={kernelName:ai,backendName:\"webgpu\",kernelFunc:Toe};var II=Ze({opType:ge.MUL,cpuKernelImpl:lM,supportsComplex:!0}),yL={kernelName:kn,backendName:\"webgpu\",kernelFunc:II};function ql(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"sum\",t)}var bL={kernelName:Kn,backendName:\"webgpu\",kernelFunc:ql};function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=ql({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var CL={kernelName:ii,backendName:\"webgpu\",kernelFunc:_oe};var $oe=xe({opType:X.ELU}),SL={kernelName:Jo,backendName:\"webgpu\",kernelFunc:$oe};var Eoe=Ze({opType:ge.EQUAL,dtype:\"bool\",cpuKernelImpl:Q3}),wL={kernelName:en,backendName:\"webgpu\",kernelFunc:Eoe};var Aoe=xe({opType:X.ERF}),IL={kernelName:da,backendName:\"webgpu\",kernelFunc:Aoe};var vI=xe({opType:X.EXP,cpuKernelImpl:Z3,dtype:\"float32\"}),vL={kernelName:tn,backendName:\"webgpu\",kernelFunc:vI};function ix(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),me({inputs:{x:s},backend:o,attrs:{shape:i}})}var kL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:ix};var Roe=xe({opType:X.EXPM1,cpuKernelImpl:J3}),NL={kernelName:fa,backendName:\"webgpu\",kernelFunc:Roe};var jl=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function ux(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new jl(\"real\",u),l=new jl(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=me({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function Doe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!1,t)}var TL={kernelName:ui,backendName:\"webgpu\",kernelFunc:Doe};var px=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var _L={kernelName:rn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Foe=xe({opType:X.FLOOR,cpuKernelImpl:eM}),$L={kernelName:on,backendName:\"webgpu\",kernelFunc:Foe};var Ooe=Ze({opType:ge.INT_DIV,dtype:\"int32\"}),EL={kernelName:nn,backendName:\"webgpu\",kernelFunc:Ooe};var cx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${Q(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var AL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:Poe},Ac,kI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),lx=new Map;function Poe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d){let D=n;if(!lx.has(D)||lx.get(D).expired){let P={source:D};lx.set(D,t.device.importExternalTexture(P))}b={width:c,height:l,format:null,usage:null,texture:lx.get(D)}}else{if(f){let L=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Ac==null||L!==kI)&&(kI=L,Ac=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:kI})),Ac.canvas.width=c,Ac.canvas.height=l,Ac.drawImage(n,0,0,c,l),n=Ac.canvas}let D=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,P=\"rgba8unorm\",M=t.textureManager.acquireTexture(m[1],m[0],P,D);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:P,usage:D,texture:M}}let C=y.sizeFromShape(m),w=y.computeStrides(m),k=new cx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...w]}],E=t.makeTensorInfo([l,c],\"int32\"),A=t.tensorMap.get(E.dataId);A.resourceInfo=b;let R=t.runWebGPUProgram(k,[E],\"int32\",_);return t.disposeData(E.dataId),R}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let w=0;w(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var RL={kernelName:sn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new mx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Moe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return Qg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var DL={kernelName:go,backendName:\"webgpu\",kernelFunc:Moe};function Loe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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dx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function Boe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=me({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=me({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=tM(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new dx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=me({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var OL={kernelName:an,backendName:\"webgpu\",kernelFunc:Boe};var fx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Voe(this.aShape);return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function Voe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new fx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var PL={kernelName:Is,backendName:\"webgpu\",kernelFunc:NI};var zoe=Ze({opType:ge.GREATER,cpuKernelImpl:nM,dtype:\"bool\"}),ML={kernelName:un,backendName:\"webgpu\",kernelFunc:zoe};var Woe=Ze({opType:ge.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:oM}),LL={kernelName:pn,backendName:\"webgpu\",kernelFunc:Woe};function Uoe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!0,t)}var BL={kernelName:pi,backendName:\"webgpu\",kernelFunc:Uoe};var Goe=xe({opType:X.IS_FINITE,dtype:\"bool\"}),VL={kernelName:ha,backendName:\"webgpu\",kernelFunc:Goe};var Hoe=xe({opType:X.IS_INF,dtype:\"bool\"}),zL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Hoe};var Koe=xe({opType:X.IS_NAN,dtype:\"bool\"}),WL={kernelName:cn,backendName:\"webgpu\",kernelFunc:Koe};function qoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Hr(n.shape,X.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var UL={kernelName:ln,backendName:\"webgpu\",kernelFunc:qoe};var joe=Ze({opType:ge.LESS,dtype:\"bool\",cpuKernelImpl:aM}),GL={kernelName:mn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Ze({opType:ge.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:sM}),HL={kernelName:dn,backendName:\"webgpu\",kernelFunc:Xoe};var hx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function Yoe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new hx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var KL={kernelName:li,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=xe({opType:X.LOG,cpuKernelImpl:iM}),qL={kernelName:fn,backendName:\"webgpu\",kernelFunc:Qoe};var Zoe=xe({opType:X.LOG1P}),jL={kernelName:xa,backendName:\"webgpu\",kernelFunc:Zoe};var Joe=Ze({opType:ge.LOGICAL_AND,dtype:\"bool\"}),XL={kernelName:hn,backendName:\"webgpu\",kernelFunc:Joe};var ene=xe({opType:X.LOGICAL_NOT}),YL={kernelName:gn,backendName:\"webgpu\",kernelFunc:ene};var tne=Ze({opType:ge.LOGICAL_OR}),QL={kernelName:xn,backendName:\"webgpu\",kernelFunc:tne};var ZL=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,gx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${ZL}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},xx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${Q()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${ZL}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function rne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new gx(n.shape):u=new xx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var JL={kernelName:mi,backendName:\"webgpu\",kernelFunc:rne};var one=Ze({opType:ge.MAX,cpuKernelImpl:pM}),eB={kernelName:bn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"max\",t)}var tB={kernelName:Cn,backendName:\"webgpu\",kernelFunc:nne};function sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"min\",t)}var rB={kernelName:wn,backendName:\"webgpu\",kernelFunc:sne};var ane=Ze({opType:ge.MIN,cpuKernelImpl:cM}),oB={kernelName:In,backendName:\"webgpu\",kernelFunc:ane};var yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Rt(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var nB={kernelName:vn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var ine=Ze({opType:ge.MOD}),sB={kernelName:ya,backendName:\"webgpu\",kernelFunc:ine};function une(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=mM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Hr(o.shape,X.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aB={kernelName:vs,backendName:\"webgpu\",kernelFunc:une};function pne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var iB={kernelName:Tn,backendName:\"webgpu\",kernelFunc:pne};function cne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var uB={kernelName:_n,backendName:\"webgpu\",kernelFunc:cne};var bx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${Q(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function lne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new bx(u,a),l=me({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=me({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pB={kernelName:$n,backendName:\"webgpu\",kernelFunc:lne};function Xl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=Xl({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var cB={kernelName:Os,backendName:\"webgpu\",kernelFunc:Xl};function lB(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=lB({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mB={kernelName:ks,backendName:\"webgpu\",kernelFunc:lB};function mne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return ix({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=ix({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=wI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dB={kernelName:Ns,backendName:\"webgpu\",kernelFunc:mne};var Cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let start = ${s};\n let end = ${a};\n let outC = getCoordsFromIndex(index);\n\n if (${i} || ${p}) {\n setOutputAtIndex(index, uniforms.constantValue);\n } else {\n let coords = outC - start;\n setOutputAtIndex(index, getX(${u}));\n }\n }\n }\n `}};var TI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Dt({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return fr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Cx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fB={kernelName:En,backendName:\"webgpu\",kernelFunc:TI};var dne=Ze({opType:ge.POW}),hB={kernelName:An,backendName:\"webgpu\",kernelFunc:dne};function fne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Yu(ge.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var gB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:fne};function hne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"prod\",t)}var xB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:hne};var gne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:gne};var _I=Ze({opType:ge.DIV}),bB={kernelName:Zo,backendName:\"webgpu\",kernelFunc:_I};var xne=xe({opType:X.RECIPROCAL}),CB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:xne};var yne=xe({opType:X.RELU}),SB={kernelName:On,backendName:\"webgpu\",kernelFunc:yne};var bne=xe({opType:X.RELU6}),wB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:bne};var Sx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new Sx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var IB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Cne};var wx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new wx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var vB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:Sne};var Ix=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Dt({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=me({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new Ix(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=me({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var kB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:wne};var vx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var NB={kernelName:ts,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Ine=xe({opType:X.ROUND}),TB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Ine};var vne=xe({opType:X.RSQRT,cpuKernelImpl:gM}),_B={kernelName:zn,backendName:\"webgpu\",kernelFunc:vne};var Hi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ae(e),this.dispatch=Y(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${Q(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${vc(this.type,!1)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Ic(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function kne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=me({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=fr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Hi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=me({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var $B={kernelName:Wn,backendName:\"webgpu\",kernelFunc:kne};var kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function Nne(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new kx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var EB={kernelName:fi,backendName:\"webgpu\",kernelFunc:Nne};var Nx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function Tne(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new Nx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var AB={kernelName:$s,backendName:\"webgpu\",kernelFunc:Tne};var _ne=xe({opType:X.SELU}),RB={kernelName:Ca,backendName:\"webgpu\",kernelFunc:_ne};var $ne=xe({opType:X.SIGMOID}),DB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:$ne};var Ene=xe({opType:X.SIGN}),FB={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ene};var Ane=xe({opType:X.SIN}),OB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Ane};var Rne=xe({opType:X.SINH}),PB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Rne};var $I=Ze({opType:ge.SUB,cpuKernelImpl:wM,supportsComplex:!0}),MB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:$I};function Dne(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Hl({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=me({inputs:{x:i},backend:t,attrs:{shape:p}}),c=$I({inputs:{a:n,b:u},backend:t}),l=vI({inputs:{x:c},backend:t}),m=ql({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=me({inputs:{x:m},backend:t,attrs:{shape:p}}),f=_I({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var LB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Dne};var Fne=xe({opType:X.SOFTPLUS}),BB={kernelName:Ia,backendName:\"webgpu\",kernelFunc:Fne};var One=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},VB={kernelName:As,backendName:\"webgpu\",kernelFunc:One};var Tx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=IM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Tx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var zB={kernelName:to,backendName:\"webgpu\",kernelFunc:EI};function Mne(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=xM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=me({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?me({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Dt({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=me({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=EI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Hi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let E=me({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),E}var WB={kernelName:yi,backendName:\"webgpu\",kernelFunc:Mne};function Lne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=fs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Lne};var Bne=xe({opType:X.SQRT}),GB={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Bne};var HB={kernelName:bi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Hr(t.shape,X.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Vne=Ze({opType:ge.SQUARED_DIFFERENCE}),KB={kernelName:jn,backendName:\"webgpu\",kernelFunc:Vne};function zne({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Hr(o.shape,X.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qB={kernelName:fo,backendName:\"webgpu\",kernelFunc:zne};var _x=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=me({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),E=fs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=me({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),A=le(n.shape,n.dtype,E),R=CM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let E=new _x(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram(E,[n],n.dtype,A);k=me({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var jB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Wne};function Une(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var XB={kernelName:Ds,backendName:\"webgpu\",kernelFunc:Une};var Gne=xe({opType:X.TAN}),YB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Gne};var Hne=xe({opType:X.TANH}),QB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Hne};var $x=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},Ex=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function Rc(r,e){e!==null&&r.disposeData(e.dataId)}function ZB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let A=h(),R=new $x(E),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),Rc(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new Ex([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram(E,_,\"int32\",R),Rc(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=fs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Rc(t,x);let b=NI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Rc(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=me({inputs:{x:f},attrs:{shape:C},backend:t}),Rc(t,x);let w=b;return b=me({inputs:{x:b},attrs:{shape:C},backend:t}),Rc(t,w),[b,f]}var JB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Kne};var Ax=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${Q(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function qne(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ax(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var eV={kernelName:es,backendName:\"webgpu\",kernelFunc:qne};function jne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var tV={kernelName:Fs,backendName:\"webgpu\",kernelFunc:jne};var Xne=[G3,NM,TM,_M,$M,EM,RM,DM,FM,OM,PM,MM,LM,BM,VM,UM,GM,HM,qM,jM,ZM,JM,eL,K3,rL,oL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,CL,SL,wL,IL,vL,kL,NL,TL,W3,_L,AL,$L,EL,RL,DL,FL,OL,PL,ML,LL,H3,BL,tL,VL,zL,WL,UL,GL,HL,KL,jL,qL,XL,YL,QL,JL,zM,eB,tB,WM,rB,oB,nB,sB,yL,aB,iB,uB,XM,pB,mB,dB,fB,hB,gB,xB,yB,YM,bB,CB,SB,wB,U3,IB,vB,kB,NB,TB,_B,$B,EB,AB,RB,DB,FB,OB,PB,KM,qB,jB,XB,LB,BB,VB,WB,UB,GB,HB,KB,MB,bL,YB,QB,zB,JB,eV,AM,tV,cB];for(let r of Xne)ka(r);var rV=\"4.2.0\",Yne=\"4.2.0\",Qne=\"4.2.0\",Zne=\"4.2.0\",Jne=\"4.2.0\",ese=\"0.0.1-alpha.17\",tse={tfjs:rV,\"tfjs-core\":rV,\"tfjs-converter\":Yne,\"tfjs-backend-cpu\":Qne,\"tfjs-backend-webgl\":Zne,\"tfjs-backend-wasm\":Jne,\"tfjs-backend-webgpu\":ese};export{ys as Abs,aa as Acos,ia as Acosh,yu as AdadeltaOptimizer,bu as AdagradOptimizer,Cu as AdamOptimizer,Su as AdamaxOptimizer,eo as Add,Po as AddN,Mo as All,Lo as Any,Bo as ArgMax,ei as ArgMin,ua as Asin,pa as Asinh,ca as Atan,ma as Atan2,la as Atanh,Vo as AvgPool,dp as AvgPool3D,vm as AvgPool3DGrad,mp as AvgPoolGrad,Ll as BackendWasm,zo as BatchMatMul,bs as BatchToSpaceND,ti as Bincount,fp as BroadcastArgs,ise as BroadcastTo,co as Cast,Wo as Ceil,lo as ClipByValue,ri as Complex,hp as ComplexAbs,Cs as Concat,Uo as Conv2D,oi as Conv2DBackpropFilter,Go as Conv2DBackpropInput,gp as Conv3D,km as Conv3DBackpropFilterV2,xp as Conv3DBackpropInputV2,Ho as Cos,Ko as Cosh,Xo as CropAndResize,qo as Cumprod,jo as Cumsum,Fo as DataStorage,ni as DenseBincount,Yo as DepthToSpace,Qo as DepthwiseConv2dNative,yp as DepthwiseConv2dNativeBackpropFilter,bp as DepthwiseConv2dNativeBackpropInput,si as Diag,ai as Dilation2D,$b as Dilation2DBackpropFilter,_b as Dilation2DBackpropInput,kb as ENV,ii as Einsum,Jo as Elu,Nm as EluGrad,Uc as Environment,en as Equal,da as Erf,tn as Exp,Ss as ExpandDims,fa as Expm1,ui as FFT,ws as Fill,rn as FlipLeftRight,on as Floor,nn as FloorDiv,Zi as FromPixels,sn as FusedBatchNorm,go as FusedConv2D,xo as FusedDepthwiseConv2D,Lu as GPGPUContext,an as GatherNd,Is as GatherV2,fl as GraphModel,un as Greater,pn as GreaterEqual,pi as IFFT,mo as Identity,ci as Imag,ha as IsFinite,ga as IsInf,cn as IsNan,Zr as KernelBackend,mi as LRN,Tm as LRNGrad,ln as LeakyRelu,mn as Less,dn as LessEqual,li as LinSpace,fn as Log,xa as Log1p,use as LogSoftmax,hn as LogicalAnd,gn as LogicalNot,xn as LogicalOr,XI as LogicalXor,pse as LowerBound,Pi as MathBackendCPU,Vi as MathBackendWebGL,yn as Max,Cn as MaxPool,Cp as MaxPool3D,$m as MaxPool3DGrad,_m as MaxPoolGrad,Sp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,wu as MomentumOptimizer,wp as Multinomial,kn as Multiply,vs as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,jb as OP_SCOPE_SUFFIX,$n as OneHot,ks as OnesLike,wr as Optimizer,cl as OptimizerConstructors,Ns as Pack,En as PadV2,cse as Pool,An as Pow,Rn as Prelu,Dn as Prod,Iu as RMSPropOptimizer,Ip as RaggedGather,vp as RaggedRange,kp as RaggedTensorToTensor,Ts as Range,Lb as Rank,di as Real,Zo as RealDiv,Fn as Reciprocal,$t as Reduction,On as Relu,Ln as Relu6,_s as Reshape,Mn as ResizeBilinear,Am as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,ts as RotateWithOffset,Vn as Round,zn as Rsqrt,Pa as SGDOptimizer,Wn as ScatterNd,fi as SearchSorted,$s as Select,Ca as Selu,Gn as Sigmoid,wa as Sign,Un as Sin,Sa as Sinh,Es as Slice,qn as Softmax,Ia as Softplus,As as SpaceToBatchND,hi as SparseFillEmptyRows,va as SparseReshape,gi as SparseSegmentMean,xi as SparseSegmentSum,yi as SparseToDense,Rs as SplitV,Hn as Sqrt,bi as Square,jn as SquaredDifference,fo as Step,Xn as StridedSlice,Ds as StringNGrams,Ci as StringSplit,Si as StringToHashBucketFast,Yn as Sub,Kn as Sum,Qn as Tan,Zn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Jn as TopK,es as Transform,ro as Transpose,Np as Unique,Fs as Unpack,Tp as UnsortedSegmentSum,lse as UpperBound,Na as Variable,Gi as WebGPUBackend,Os as ZerosLike,ho as _FusedMatMul,Yt as abs,G0 as acos,H0 as acosh,be as add,K0 as addN,q0 as all,j0 as any,X0 as argMax,Y0 as argMin,Q0 as asin,Z0 as asinh,J0 as atan,ev as atan2,tv as atanh,qm as avgPool,nv as avgPool3d,Pie as backend,S as backend_util,sv as basicLSTMCell,_i as batchNorm,iv as batchNorm2d,uv as batchNorm3d,pv as batchNorm4d,jm as batchToSpaceND,Xm as bincount,VG as booleanMaskAsync,cv as broadcastArgs,$i as broadcastTo,yr as broadcast_util,gN as browser,le as buffer,je as cast,lv as ceil,mv as clipByValue,Lr as clone,Tr as complex,xt as concat,dv as concat1d,fv as concat2d,hv as concat3d,gv as concat4d,xv as conv1d,Ei as conv2d,yv as conv2dTranspose,bv as conv3d,Sv as conv3dTranspose,bse as copyRegisteredKernels,wv as cos,Iv as cosh,al as cosineWindow,vv as cumprod,kv as cumsum,br as customGrad,Nv as denseBincount,nC as deprecationWarn,Tv as depthToSpace,Bp as depthwiseConv2d,Mq as deregisterOp,ki as device_util,_v as diag,$v as dilation2d,kie as disableDeprecationWarnings,Ot as dispose,Nie as disposeVariables,He as div,Av as divNoNan,Rv as dot,ZG as dropout,Dv as einsum,Jm as elu,vie as enableDebugMode,Iie as enableProdMode,CC as enclosingPowerOfTwo,sr as engine,O as env,Zm as equal,Fv as erf,Mv as euclideanNorm,So as exp,Ra as expandDims,Lv as expm1,ed as eye,zp as fft,Us as fill,Fie as findBackend,Oie as findBackendFactory,td as floor,Km as floorDiv,GE as forceHalfFloat,SC as fused,rd as gather,YG as gatherND,Wd as gather_util,Rie as getBackend,Eb as getGradient,Kc as getKernel,Rm as getKernelsForBackend,Kee as getThreadsCount,$w as gpgpu_util,HU as grad,KU as grads,uu as greater,od as greaterEqual,du as ifft,Ri as imag,eK as image,eH as inTopKAsync,La as io,Td as irfft,Bv as isFinite,Vv as isInf,zv as isNaN,_r as keep,Vt as kernel_impls,nd as leakyRelu,Wv as less,Vp as lessEqual,tK as linalg,Uv as linspace,A6 as loadGraphModel,R6 as loadGraphModelSync,Gv as localResponseNormalization,Da as log,sd as log1p,Hv as logSigmoid,Kv as logSoftmax,ud as logSumExp,pu as logicalAnd,pd as logicalNot,cd as logicalOr,qv as logicalXor,rK as losses,jv as lowerBound,Xe as matMul,fN as math,Gs as max,md as maxPool,Xv as maxPool3d,Yv as maxPoolWithArgmax,dd as maximum,cu as mean,Tie as memory,Qv as meshgrid,rl as min,fd as minimum,Zv as mirrorPad,Jv as mod,ek as moments,UG as movingAverage,ne as mul,tk as multiRNNCell,rk as multinomial,Cr as neg,AC as nextFrame,iu as norm,hd as notEqual,nl as oneHot,Hs as ones,ok as onesLike,N as op,nk as outerProduct,Ks as pad,sk as pad1d,ak as pad2d,ik as pad3d,uk as pad4d,pk as pool,Aa as pow,xd as prelu,Hm as print,ck as prod,_ie as profile,lk as raggedGather,mk as raggedRange,dk as raggedTensorToTensor,fk as rand,Ok as randomGamma,Id as randomNormal,Pk as randomStandardNormal,vd as randomUniform,Di as range,Aie as ready,Fa as real,Mk as reciprocal,Ti as registerBackend,gse as registerGradient,ka as registerKernel,Pq as registerOp,Fi as relu,kd as relu6,Die as removeBackend,W as reshape,no as reverse,Lk as reverse1d,Bk as reverse2d,Vk as reverse3d,zk as reverse4d,Wp as rfft,Nd as round,Wk as rsqrt,Ce as scalar,HG as scatterND,sl as scatter_util,ol as searchSorted,Uk as selu,Gk as separableConv2d,oN as serialization,Eie as setBackend,Mie as setPlatform,Hee as setThreadsCount,Uee as setWasmPath,Gee as setWasmPaths,US as setWebGLContext,Hk as setdiff1dAsync,Zp as shared,Ws as sigmoid,Kk as sign,JH as signal,qk as sin,jk as sinh,Ke as slice,Xk as slice1d,Yk as slice2d,Qk as slice3d,Zk as slice4d,ut as slice_util,Jk as softmax,id as softplus,gd as spaceToBatchND,oK as sparse,jG as sparseToDense,ZH as spectral,Oa as split,$r as sqrt,Qt as square,_d as squaredDifference,Up as squeeze,Sr as stack,$d as step,e1 as stridedSlice,nK as string,ke as sub,et as sum,Ta as sumOutType,t1 as tan,tl as tanh,nr as tensor,dr as tensor1d,Oi as tensor2d,Ed as tensor3d,r1 as tensor4d,o1 as tensor5d,n1 as tensor6d,C0 as tensor_util,Fk as test_util,Ee as tidy,Ai as tile,$ie as time,s1 as topk,YMe as train,Gp as transpose,a1 as truncatedNormal,i1 as unique,yse as unregisterGradient,xse as unregisterKernel,u1 as unsortedSegmentSum,so as unstack,dt as upcastType,p1 as upperBound,y as util,qU as valueAndGrad,jU as valueAndGrads,c1 as variable,pC as variableGrads,tse as version,F6 as version_converter,RK as version_core,pj as version_cpu,qee as version_wasm,nY as version_webgl,CJe as webgl,nc as webgl_util,hI as webgpu_util,ss as where,Rd as whereAsync,Br as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') { // TBD replace with navigator.userAgentData once in mainline\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam', 'no devices');\n return;\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam', 'no stream');\n return;\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh.keepInvalid) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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Zm;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Zm||(Zm={}));var Ym;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Ym||(Ym={}));var aT={float32:Zm,int32:Xm,bool:Km,complex64:Ym};function ca(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return aT[e][t]}function Hd(e){return ca(e,"int32")}function St(e,t){if(e.dtype===t.dtype)return[e,t];let a=ca(e.dtype,t.dtype);return[e.cast(a),t.cast(a)]}function wA(e,t){P(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function nT(e,t){return t.some(a=>a.id===e.id)}function Q1(e){let t=[];return kA(e,t,new Set),t}function kA(e,t,a){if(e==null)return;if(e instanceof pt){t.push(e);return}if(!rT(e))return;let n=e;for(let r in n){let s=n[r];a.has(s)||(a.add(s),kA(s,t,a))}}function rT(e){return Array.isArray(e)||typeof e=="object"}function _m(e){return e.kernelName!=null}var W5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},ld=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new W5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let 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o,l=_m(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(_m(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Sc(h,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let x=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,x,A);let y=A.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(h,f,y);a=this.saveTensorsForBackwardMode(b)}return y}}else{let{forwardFunc:h}=e,f=m=>{!n||(a=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:p}=e,c=_m(e)?null:e.backwardsFunc,d;return 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this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*Vm(e.dtype)),this.state.numBytes+=a,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a})),e instanceof od||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let a=e.size*Vm(e.dtype);this.state.numBytes-=a}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,a=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-a;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,a,n,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:a,saved:r},o=Hm(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let c=a[p],d=Gc(c.size,c.dtype);return this.makeTensor(d,c.shape,c.dtype)}return u}),n(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Q1(e),a=new Set(t.map(r=>r.id));for(let r=0;r{!r.kept&&r.scopeId===n.id&&this.track(r)})}gradients(e,t,a,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),a!=null&&a.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${a.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(r instanceof pt,()=>"The result y returned by f() must be a tensor.");let s=qS(this.state.activeTape,t,r);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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e=="object"}function Rm(e){return e.kernelName!=null}var D5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},ld=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new D5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let 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t={x:R(e,"x","tanh","float32")};return z.runKernel(ho,t)}var $c=D({tanh_:CC});function NC(e,t,a,n,r,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(a,"lstmBias","basicLSTMCell"),u=R(n,"data","basicLSTMCell"),p=R(r,"c","basicLSTMCell"),c=R(s,"h","basicLSTMCell"),d=rt([u,c],1),h=it(d,o),f=be(h,l),m=f.shape[0],g=f.shape[1]/4,x=[m,g],y=De(f,[0,0],x),A=De(f,[0,g],x),b=De(f,[0,g*2],x),k=De(f,[0,g*3],x),S=be(ae(Da(y),$c(A)),ae(p,Da(be(i,b)))),C=ae($c(S),Da(k));return[S,C]}var Xy=D({basicLSTMCell_:NC});function EC(e,t,a){let n=R(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);P(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),P(a.length===t.length,()=>`crops.length is ${a.length} but should be equal to blockShape.length ${t.length}`),P(n.shape[0]%r===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:n},i={blockShape:t,crops:a};return z.runKernel(El,s,i)}var s1=D({batchToSpaceND_:EC});function RC(e){let t;return e.rank===0||e.rank===1?t=J(e,[1,1,1,e.size]):e.rank===2?t=J(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=J(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function MC(e,t,a,n,r,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let p;n!=null&&(p=R(n,"offset","batchNorm")),P(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),P(p==null||o.rank===p.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),P(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let c={x:RC(i),scale:u,offset:p,mean:o,variance:l},d={varianceEpsilon:s},h=z.runKernel(Ai,c,d);return J(h,i.shape)}var ap=D({batchNorm_:MC});function $C(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let p;return n!=null&&(p=R(n,"offset","batchNorm")),P(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),P(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),P(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&P(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),ap(i,o,l,p,u,s)}var Ky=D({batchNorm2d_:$C});function _C(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let p;return n!=null&&(p=R(n,"offset","batchNorm")),P(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),P(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),P(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&P(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),ap(i,o,l,p,u,s)}var Zy=D({batchNorm3d_:_C});function PC(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let p;return n!=null&&(p=R(n,"offset","batchNorm")),P(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),P(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),P(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&P(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),ap(i,o,l,p,u,s)}var Yy=D({batchNorm4d_:PC});function FC(e,t,a){let n=R(e,"x","bincount"),r=R(t,"weights","bincount");P(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),P(a>=0,()=>`size must be non-negative, but got ${a}.`),P(r.size===n.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${r.shape}.`);let s={x:n,weights:r},i={size:a};return z.runKernel(Td,s,i)}var i1=D({bincount_:FC});function OC(e,t){let a=R(e,"s0","broadcastArgs","int32"),n=R(t,"s1","broadcastArgs","int32");if(a.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${a.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let r={s0:a,s1:n};return z.runKernel(Yc,r)}var Jy=D({broadcastArgs_:OC});function DC(e,t){let a=R(e,"broadcastTo","x"),n=a.shape;if(Ja(t),t.lengtha.rank){let l=a.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(a.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return ka(a);let i={x:a},o={reps:s};return z.runKernel(rs,i,o)}var rl=D({broadcastTo_:DC});function zC(e){let t={x:R(e,"x","ceil","float32")};return z.runKernel(ai,t)}var Qy=D({ceil_:zC});function nr(e,t,a){Ja(e);let n={shape:e,value:t,dtype:a};return z.runKernel(Pl,{},n)}function LC(e,t,a){let n=R(e,"x","clipByValue");if(P(t<=a,()=>`Error in clip: min (${t}) must be less than or equal to max (${a}).`),t===a)return nr(n.shape,t,n.dtype);let r={x:n},s={clipValueMin:t,clipValueMax:a};return z.runKernel(ns,r,s)}var eA=D({clipByValue_:LC});function BC(e){return rt(e,0)}var tA=D({concat1d_:BC});function WC(e,t){return rt(e,t)}var ru=D({concat2d_:WC});function VC(e,t){return rt(e,t)}var aA=D({concat3d_:VC});function UC(e,t){return rt(e,t)}var nA=D({concat4d_:UC});function GC(e,t,a,n,r="NHWC",s=[1,1],i){let o=R(e,"x","conv2d","float32"),l=R(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=J(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Sn("conv2d",n,i);let c=r==="NHWC"?u.shape[3]:u.shape[1];P(c===l.shape[2],()=>`Error in conv2d: depth of input (${c}) must match input depth for filter ${l.shape[2]}.`),P(kr(a,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`),P(Bs(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),P(Bs(a),()=>"Error in conv2D: Strides should be larger than 0.");let d={x:u,filter:l},h={strides:a,pad:n,dataFormat:r,dilations:s,dimRoundingMode:i},f=z.runKernel(ni,d,h);return p?J(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var np=D({conv2d_:GC});function HC(e,t,a,n,r="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=J(o,[1,o.shape[0],o.shape[1]])),P(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),P(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Sn("conv1d",n,i),P(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),P(kr(a,s),()=>`Error in conv1D: Either stride or dilation must be 1. 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n=Math.exp(t*a*a);return a<=e?n:0}function W5(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function NM(e,t,a,n=.5,r=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppressionAsync"),i=R(t,"scores","nonMaxSuppressionAsync"),o=iu(s,i,a,n,r);a=o.maxOutputSize,n=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],p=l[1],{selectedIndices:c}=Ub(u,p,a,n,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),Ht(c,"int32")}var EM=NM;function RM(e,t,a,n=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=iu(i,o,a,n,r,s);a=l.maxOutputSize,n=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},p={maxOutputSize:a,iouThreshold:n,scoreThreshold:r,softNmsSigma:s},c=z.runKernel(Vi,u,p);return{selectedIndices:c[0],selectedScores:c[1]}}var MM=D({nonMaxSuppressionWithScore_:RM});async function $M(e,t,a,n=.5,r=Number.NEGATIVE_INFINITY,s=0){let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Eh.className="Adamax";ss(Eh);var ip=class extends is{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=z.registeredVariables[t];$e(()=>{let s=be(ae(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=On(Fe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a),o=k("includeBatchInIndex",e,t,a),{result:l,indexes:u}=n.maxPoolWithArgmax(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.avgPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.maxPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("dilations",e,t,a),o=r[1],l=r[2],u=i[1],p=i[2];return[n.dilation2d(k("x",e,t,a),k("filter",e,t,a),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},YP=(e,t,a,n=Zt)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,a),s=k("dtype",e,t,a),i=k("value",e,t,a);return[n.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("num",e,t,a);return[n.linspace(r,s,i)]}case"Multinomial":{let 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implemented`)}},cF=(e,t,a,n=Zt)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hF=(e,t,a,n=Zt)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function nx(e,t,a,n,r=$e){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>VP(i,o,l));case"basic_math":return r(()=>UP(i,o,l));case"control":return KP(i,o,l);case"convolution":return r(()=>ZP(i,o,l));case"creation":return r(()=>YP(i,o,l));case"dynamic":return JP(i,o,l);case"evaluation":return r(()=>QP(i,o,l));case"image":return r(()=>nF(i,o,l));case"graph":return r(()=>eF(i,o,l));case"logical":return r(()=>rF(i,o,l));case"matrices":return r(()=>sF(i,o,l));case"normalization":return r(()=>iF(i,o,l));case"ragged":return r(()=>oF(i,o,l));case"reduction":return r(()=>lF(i,o,l));case"slice_join":return r(()=>uF(i,o,l));case"sparse":return r(()=>dF(i,o,l));case"spectral":return r(()=>pF(i,o,l));case"string":return r(()=>cF(i,o,l));case"transformation":return r(()=>hF(i,o,l));case"hash_table":return aF(i,o,l,n);case"custom":let u=w4(i.op);if(u&&u.customExecutor)return u.customExecutor(new WP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var rx=class{constructor(e={},t={},a={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function sx(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>ja(d)[0]),p=[];n!=null&&(p=n.map(d=>ja(d.name)[0]));let c=[...t];for(;c.length>0;){let d=c.pop();if((G4(d)||AF(d)||yF(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&u.indexOf(d.name)===-1&&p.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function fF(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(p=>ja(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{n.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{n.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(d=>l.has(d.name))&&s.push(c)})}return u}var mF=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],gF=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],xF=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function G4(e){return mF.indexOf(e.op)>=0}function AF(e){return gF.indexOf(e.op)>=0}function yF(e){return xF.indexOf(e.op)>=0}var A1=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new A1(e.functions[a],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(a=>e[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=sx(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return fF(this.graph,this.weightMap,a)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return On(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(p=>this.graph.nodes[ja(p)[0]]),r=t.map(p=>ja(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return $e(()=>{let p=new rx(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(f=>{let[m,g]=ja(f),x=[];x[g]=e[f],c[m]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(x))});let d=this.getFrozenTensorIds(c),h={};for(let f=0;fba(f,c,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=bP(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new rx(this.weightMap,n,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>ba(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[ja(A)[0]]),i=a.map(A=>ja(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:c}=sx(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[y,b]=ja(A),w=[];w[b]=e[A],h[y]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let A=this.processStack(s,d,t,h,g,m,i,f,l);await Promise.all(A)}p==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=o.filter(A=>!G4(A)&&!ba(A.name,h,t)).map(A=>A.name);if(x.length>0){let A="";throw p!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${A}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&k("isConstant",p.node,n,a)&&([c]=mr(p.node.name,a)),n[p.node.name]==null){let d=nx(p.node,n,a,this._resourceManager);c||([c]=mr(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=mr(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=ja(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=ja(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=ja(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},bF=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},vF="?tfjs-format=file",wF="model.json",op=class{constructor(e,t={},a=jn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new bF}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new A1(Q5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Q5.Instance.transformGraph(e.modelInitializer);this.initializer=new A1(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof pt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof pt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=IF(e));let n=new op(e,t,a);return await n.load(),n}function kF(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function tx(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>ja(d)[0]),p=[];n!=null&&(p=n.map(d=>ja(d.name)[0]));let c=[...t];for(;c.length>0;){let d=c.pop();if((V4(d)||yF(d)||AF(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&u.indexOf(d.name)===-1&&p.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function fF(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(p=>ja(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{n.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{n.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(d=>l.has(d.name))&&s.push(c)})}return u}var mF=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],gF=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],xF=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function V4(e){return mF.indexOf(e.op)>=0}function yF(e){return gF.indexOf(e.op)>=0}function AF(e){return xF.indexOf(e.op)>=0}var f2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new f2(e.functions[a],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(a=>e[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=tx(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return fF(this.graph,this.weightMap,a)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return On(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(p=>this.graph.nodes[ja(p)[0]]),r=t.map(p=>ja(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return Fe(()=>{let p=new ex(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(f=>{let[m,g]=ja(f),x=[];x[g]=e[f],c[m]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(x))});let d=this.getFrozenTensorIds(c),h={};for(let f=0;fba(f,c,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=bP(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new ex(this.weightMap,n,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>ba(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(y=>this.graph.nodes[ja(y)[0]]),i=a.map(y=>ja(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:c}=tx(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[A,b]=ja(y),k=[];k[b]=e[y],h[A]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(s,d,t,h,g,m,i,f,l);await Promise.all(y)}p==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=o.filter(y=>!V4(y)&&!ba(y.name,h,t)).map(y=>y.name);if(x.length>0){let y="";throw p!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${y}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&w("isConstant",p.node,n,a)&&([c]=mr(p.node.name,a)),n[p.node.name]==null){let d=Q5(p.node,n,a,this._resourceManager);c||([c]=mr(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=mr(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=ja(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=ja(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=ja(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},bF=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},vF="?tfjs-format=file",kF="model.json",dp=class{constructor(e,t={},a=jn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new bF}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f2(K5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=K5.Instance.transformGraph(e.modelInitializer);this.initializer=new f2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof pt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof pt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let 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o=s.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=mt({inputs:{x:r},backend:a,attrs:{shape:l}}),f=La({inputs:{x:h},backend:a,attrs:{perm:u}}),m=mt({inputs:{x:f},backend:a,attrs:{shape:p}}),g=Gs({inputs:{x:m},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var TD={kernelName:Rl,backendName:"cpu",kernelFunc:SD};function CD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=i3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var ND={kernelName:Id,backendName:"cpu",kernelFunc:CD};function ED(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RD={kernelName:jc,backendName:"cpu",kernelFunc:ED},MD=ot(as,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;um.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return er({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>Us({inputs:{input:b},backend:a})),g=l.map(b=>gl({inputs:{input:b},backend:a})),x=xl({inputs:m,backend:a,attrs:{axis:s}}),A=xl({inputs:g,backend:a,attrs:{axis:s}}),y=Xa({inputs:{real:x,imag:A},backend:a});return m.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(A),y}let u=l.map(m=>{let g=[-1,v.sizeFromShape(m.shape.slice(s))];return mt({inputs:{x:m},backend:a,attrs:{shape:g}})}),p=u.map(m=>({vals:a.data.get(m.dataId).values,shape:m.shape}));o=T.computeOutShape(u.map(m=>m.shape),1);let c=u[0].shape[0]===1,d=o3(p,o,t[0].dtype,c),h=T.computeOutShape(l.map(m=>m.shape),s),f=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var OD={kernelName:Ml,backendName:"cpu",kernelFunc:xl};function z7(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;ye([r,s],"conv2d");let c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,x=d.padInfo.left,A=d.padInfo.top,y=d.dataFormat==="channelsLast",b=new jt(d.outShape,r.dtype),w=v.computeStrides(r.shape),S=v.computeStrides(s.shape),C=w[0],E=y?w[1]:w[2],_=y?w[2]:1,$=y?1:w[1],M=b.strides[0],I=y?b.strides[1]:b.strides[2],N=y?b.strides[2]:1,O=y?1:b.strides[1],L=a.data.get(r.dataId).values,B=a.data.get(s.dataId).values,G=b.values;for(let j=0;j=d.inHeight)continue;let he=re*S[0],oe=U+ee*E;for(let Ae=0;Ae=d.inWidth)continue;let nt=he+Ge*S[1],ut=oe+Ke*_,et=nt;for(let rt=0;rt=u.inDepth)continue;let j=B*_[0],U=M+G*E[1];for(let H=0;H=u.inHeight)continue;let ee=j+Z*_[1],he=U+re*E[2];for(let oe=0;oe=u.inWidth)continue;let Ke=ee+Re*_[2],nt=he+Ge*u.inChannels,ut=Ke;for(let et=0;etMath.cos(e)),KD={kernelName:si,backendName:"cpu",kernelFunc:XD},ZD=ot(ii,e=>Math.cosh(e)),YD={kernelName:ii,backendName:"cpu",kernelFunc:ZD};function JD(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,f=s.shape[0],[m,g]=o,x=Me([f,m,g,h],"float32"),A=a.data.get(s.dataId).values,y=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),S=v.computeStrides(x.shape);for(let C=0;C=p)continue;let O=m>1?(M-_)*(c-1)/(m-1):0,L=g>1?(I-$)*(d-1)/(g-1):0;for(let B=0;B1?_*(c-1)+B*O:.5*(_+M)*(c-1);if(G<0||G>c-1){for(let j=0;j1?$*(d-1)+V*L:.5*($+I)*(d-1);if(Q<0||Q>d-1){for(let he=0;he1?$*(d-1)+j*L:.5*($+I)*(d-1);if(U<0||U>d-1){for(let Q=0;Qx+f-A-1:(x,A)=>x+A;for(let x=0;xx+f-A-1:(x,A)=>x+A;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),f=a.data.get(r.dataId).values,m=new Float32Array(o*c*d*h),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:x,padInfo:A}=h,y=A.left,b=A.top,w=h.outChannels/h.inChannels,S=new jt(h.outShape,r.dtype),C=a.data.get(r.dataId).values,E=a.data.get(s.dataId).values,_=S.values;for(let $=0;$=h.inHeight)continue;let j=B*c[0],U=M+G*p[1];for(let H=0;H=h.inWidth)continue;let ee=j+Z*c[1],he=U+re*h.inChannels,oe=V,Ae=ee;for(let we=0;we{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:x,outWidth:A,padInfo:y,strideHeight:b,strideWidth:w,filterHeight:S,filterWidth:C,dilationHeight:E,dilationWidth:_,outShape:$}=T.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),M=v.sizeFromShape($),I=$.length,N=v.getArrayFromDType(n.dtype,M);for(let O=0;O=0&&Z=0&&eeH&&(H=Ae)}}}let V=v.locToIndex([O,L,G,U],I,v.computeStrides($));N[V]=H}}}return{dataId:l.write(v.toTypedArray(N,n.dtype),$,n.dtype),shape:$,dtype:n.dtype}}},gz={kernelName:Gm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:x,padInfo:A,strideHeight:y,strideWidth:b,filterHeight:w,filterWidth:S,dilationHeight:C,dilationWidth:E,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Gm}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let I=0;I=0&&Q=0&&rej&&(j=ee,U=V,H=Z)}}}M[U][H][G]+=$[I][N][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},xz={kernelName:Um,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:x,padInfo:A,strideHeight:y,strideWidth:b,filterHeight:w,filterWidth:S,dilationHeight:C,dilationWidth:E,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Um}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I=0&&Q=0&&rej&&(j=ee,U=Q,H=re)}}}M[I][U][H][G]+=$[I][N][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function lp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;ye(r,"sum");let o;r.dtype==="bool"?o=Jr({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=er({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=T.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=La({inputs:{x:o},backend:a,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,l)),T.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,f]=T.computeOutAndReduceShapes(d.shape,c),m=T.upcastType(d.dtype,"int32"),g=$c(a,h,m),x=v.sizeFromShape(f),A=a.data.get(g.dataId).values,y=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=lp({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var bz={kernelName:Cd,backendName:"cpu",kernelFunc:yz};function vz(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;ye([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var wz={kernelName:j1,backendName:"cpu",kernelFunc:vz},kz=T.ERF_P,Iz=T.ERF_A1,Sz=T.ERF_A2,Tz=T.ERF_A3,Cz=T.ERF_A4,Nz=T.ERF_A5,Ez=ot($l,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+kz*a);return t*(1-((((Nz*n+Cz)*n+Tz)*n+Sz)*n+Iz)*n*Math.exp(-a*a))}),Rz={kernelName:$l,backendName:"cpu",kernelFunc:Ez};function Fc(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mt({inputs:{x:r},backend:a,attrs:{shape:o}})}var Mz={kernelName:_l,backendName:"cpu",kernelFunc:Fc},$z=Lt((e,t)=>e/t),x3=Yt(ci,$z),b1={kernelName:ci,backendName:"cpu",kernelFunc:x3};function B7(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&AMath.floor(e/t)),Vz=Yt(Ai,Wz,null,"int32"),Uz={kernelName:Ai,backendName:"cpu",kernelFunc:Vz};function Gz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=z7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d}});if(i){let g=m;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=mt({inputs:{x:i},backend:a,attrs:{shape:[i.shape[0],1,1]}});m=ml({inputs:{a:m,b:x},backend:a}),a.disposeIntermediateTensorInfo(x)}else m=ml({inputs:{a:m,b:i},backend:a});a.disposeIntermediateTensorInfo(g)}if(h){let g=m;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let x=mt({inputs:{x:o},backend:a,attrs:{shape:[o.shape[0],1,1]}});m=Pc(a,m,h,x,f),a.disposeIntermediateTensorInfo(x)}else m=Pc(a,m,h,o,f);a.disposeIntermediateTensorInfo(g)}return m}var Hz={kernelName:qr,backendName:"cpu",kernelFunc:Gz};function jz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=L7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d}});if(i){let g=m;m=ml({inputs:{a:m,b:i},backend:a}),a.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Pc(a,m,h,o,f),a.disposeIntermediateTensorInfo(g)}return m}var qz={kernelName:Xr,backendName:"cpu",kernelFunc:jz};function Xz(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=v.sizeFromShape(n.shape),i=r.shape,o=i[i.length-1],[l,u,p,c]=T.prepareAndValidate(n,r);if(u===0)return a.makeTensorInfo(l,n.dtype,[]);let d=a.data.get(r.dataId).values,h=a.bufferSync(n),f=a7(d,h,n.dtype,u,o,p,c,n.shape,s);return a.makeTensorInfo(l,n.dtype,f.values)}var Kz={kernelName:bi,backendName:"cpu",kernelFunc:Xz};function Zz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n;ye([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=a.data.get(s.dataId).values,p=r.shape[l];for(let b=0;b=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=T.segment_util.collectGatherOpShapeInfo(r,s,l,c),f=mt({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=mt({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],x=a.bufferSync(m),A=a.bufferSync(f),y=n7(A,x,g);return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),a.makeTensorInfo(h.outputShape,y.dtype,y.values)}var Yz={kernelName:Ol,backendName:"cpu",kernelFunc:Zz};function Jz(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=mt({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=B7(o,!0,a),u=mt({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var Qz={kernelName:Ed,backendName:"cpu",kernelFunc:Jz},eL=ot(Dl,e=>Number.isFinite(e)?1:0,"bool"),tL={kernelName:Dl,backendName:"cpu",kernelFunc:eL},aL=ot(zl,e=>Math.abs(e)===1/0?1:0,"bool"),nL={kernelName:zl,backendName:"cpu",kernelFunc:aL},rL=ot(Ii,e=>Number.isNaN(e)?1:0,"bool"),sL={kernelName:Ii,backendName:"cpu",kernelFunc:rL};function iL(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=l7(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var oL={kernelName:th,backendName:"cpu",kernelFunc:iL},lL=ot(Ll,e=>Math.log1p(e)),uL={kernelName:Ll,backendName:"cpu",kernelFunc:lL},dL=Lt((e,t)=>e&&t),pL=Yt(Ei,dL,null,"bool"),cL={kernelName:Ei,backendName:"cpu",kernelFunc:pL},hL=ot(Ri,e=>e?0:1,"bool"),fL={kernelName:Ri,backendName:"cpu",kernelFunc:hL},mL=Lt((e,t)=>e||t),gL=Yt(Bl,mL,null,"bool"),xL={kernelName:Bl,backendName:"cpu",kernelFunc:gL};function AL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;ye(r,"LRN");let 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o=s.reduce((x,y)=>x*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=mt({inputs:{x:r},backend:a,attrs:{shape:l}}),f=La({inputs:{x:h},backend:a,attrs:{perm:u}}),m=mt({inputs:{x:f},backend:a,attrs:{shape:p}}),g=Gs({inputs:{x:m},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var TD={kernelName:El,backendName:"cpu",kernelFunc:SD};function CD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=o3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var ND={kernelName:Td,backendName:"cpu",kernelFunc:CD};function ED(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RD={kernelName:Yc,backendName:"cpu",kernelFunc:ED},MD=ot(ns,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;um.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return er({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>Us({inputs:{input:b},backend:a})),g=l.map(b=>ml({inputs:{input:b},backend:a})),x=gl({inputs:m,backend:a,attrs:{axis:s}}),y=gl({inputs:g,backend:a,attrs:{axis:s}}),A=Xa({inputs:{real:x,imag:y},backend:a});return m.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(y),A}let u=l.map(m=>{let g=[-1,v.sizeFromShape(m.shape.slice(s))];return mt({inputs:{x:m},backend:a,attrs:{shape:g}})}),p=u.map(m=>({vals:a.data.get(m.dataId).values,shape:m.shape}));o=T.computeOutShape(u.map(m=>m.shape),1);let c=u[0].shape[0]===1,d=l3(p,o,t[0].dtype,c),h=T.computeOutShape(l.map(m=>m.shape),s),f=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var OD={kernelName:Rl,backendName:"cpu",kernelFunc:gl};function O7(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ae([r,s],"conv2d");let c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,x=d.padInfo.left,y=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new jt(d.outShape,r.dtype),k=v.computeStrides(r.shape),S=v.computeStrides(s.shape),C=k[0],E=A?k[1]:k[2],_=A?k[2]:1,$=A?1:k[1],M=b.strides[0],I=A?b.strides[1]:b.strides[2],N=A?b.strides[2]:1,O=A?1:b.strides[1],L=a.data.get(r.dataId).values,W=a.data.get(s.dataId).values,G=b.values;for(let H=0;H=d.inHeight)continue;let fe=re*S[0],ie=U+ee*E;for(let ye=0;ye=d.inWidth)continue;let dt=fe+je*S[1],st=ie+Ze*_,tt=dt;for(let at=0;at=u.inDepth)continue;let H=W*_[0],U=M+G*E[1];for(let j=0;j=u.inHeight)continue;let ee=H+Z*_[1],fe=U+re*E[2];for(let ie=0;ie=u.inWidth)continue;let Ze=ee+Ee*_[2],dt=fe+je*u.inChannels,st=Ze;for(let tt=0;ttMath.cos(e)),KD={kernelName:si,backendName:"cpu",kernelFunc:XD},ZD=ot(ii,e=>Math.cosh(e)),YD={kernelName:ii,backendName:"cpu",kernelFunc:ZD};function JD(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,f=s.shape[0],[m,g]=o,x=_e([f,m,g,h],"float32"),y=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,k=v.computeStrides(r.shape),S=v.computeStrides(x.shape);for(let C=0;C=p)continue;let O=m>1?(M-_)*(c-1)/(m-1):0,L=g>1?(I-$)*(d-1)/(g-1):0;for(let W=0;W1?_*(c-1)+W*O:.5*(_+M)*(c-1);if(G<0||G>c-1){for(let H=0;H1?$*(d-1)+V*L:.5*($+I)*(d-1);if(Q<0||Q>d-1){for(let fe=0;fe1?$*(d-1)+H*L:.5*($+I)*(d-1);if(U<0||U>d-1){for(let Q=0;Qx+f-y-1:(x,y)=>x+y;for(let x=0;xx+f-y-1:(x,y)=>x+y;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),f=a.data.get(r.dataId).values,m=new Float32Array(o*c*d*h),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:x,padInfo:y}=h,A=y.left,b=y.top,k=h.outChannels/h.inChannels,S=new jt(h.outShape,r.dtype),C=a.data.get(r.dataId).values,E=a.data.get(s.dataId).values,_=S.values;for(let $=0;$=h.inHeight)continue;let H=W*c[0],U=M+G*p[1];for(let j=0;j=h.inWidth)continue;let ee=H+Z*c[1],fe=U+re*h.inChannels,ie=V,ye=ee;for(let Se=0;Se{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:x,outWidth:y,padInfo:A,strideHeight:b,strideWidth:k,filterHeight:S,filterWidth:C,dilationHeight:E,dilationWidth:_,outShape:$}=T.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),M=v.sizeFromShape($),I=$.length,N=v.getArrayFromDType(n.dtype,M);for(let O=0;O=0&&Z=0&&eej&&(j=ye)}}}let V=v.locToIndex([O,L,G,U],I,v.computeStrides($));N[V]=j}}}return{dataId:l.write(v.toTypedArray(N,n.dtype),$,n.dtype),shape:$,dtype:n.dtype}}},gz={kernelName:Bm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:x,padInfo:y,strideHeight:A,strideWidth:b,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:E,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Bm}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let I=0;I=0&&Q=0&&reH&&(H=ee,U=V,j=Z)}}}M[U][j][G]+=$[I][N][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},xz={kernelName:Lm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:x,padInfo:y,strideHeight:A,strideWidth:b,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:E,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Lm}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I=0&&Q=0&&reH&&(H=ee,U=Q,j=re)}}}M[I][U][j][G]+=$[I][N][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function pp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ae(r,"sum");let o;r.dtype==="bool"?o=Qr({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=er({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=T.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=La({inputs:{x:o},backend:a,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,l)),T.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,f]=T.computeOutAndReduceShapes(d.shape,c),m=T.upcastType(d.dtype,"int32"),g=Fc(a,h,m),x=v.sizeFromShape(f),y=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=pp({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var bz={kernelName:$d,backendName:"cpu",kernelFunc:Az};function vz(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ae([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var kz={kernelName:W2,backendName:"cpu",kernelFunc:vz},wz=T.ERF_P,Iz=T.ERF_A1,Sz=T.ERF_A2,Tz=T.ERF_A3,Cz=T.ERF_A4,Nz=T.ERF_A5,Ez=ot(Ml,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+wz*a);return t*(1-((((Nz*n+Cz)*n+Tz)*n+Sz)*n+Iz)*n*Math.exp(-a*a))}),Rz={kernelName:Ml,backendName:"cpu",kernelFunc:Ez};function zc(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mt({inputs:{x:r},backend:a,attrs:{shape:o}})}var Mz={kernelName:$l,backendName:"cpu",kernelFunc:zc},$z=Lt((e,t)=>e/t),y3=Yt(ci,$z),g2={kernelName:ci,backendName:"cpu",kernelFunc:y3};function z7(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&yMath.floor(e/t)),Vz=Yt(yi,Wz,null,"int32"),Uz={kernelName:yi,backendName:"cpu",kernelFunc:Vz};function Gz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=O7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d}});if(i){let g=m;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=mt({inputs:{x:i},backend:a,attrs:{shape:[i.shape[0],1,1]}});m=fl({inputs:{a:m,b:x},backend:a}),a.disposeIntermediateTensorInfo(x)}else m=fl({inputs:{a:m,b:i},backend:a});a.disposeIntermediateTensorInfo(g)}if(h){let g=m;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let x=mt({inputs:{x:o},backend:a,attrs:{shape:[o.shape[0],1,1]}});m=Dc(a,m,h,x,f),a.disposeIntermediateTensorInfo(x)}else m=Dc(a,m,h,o,f);a.disposeIntermediateTensorInfo(g)}return m}var Hz={kernelName:qr,backendName:"cpu",kernelFunc:Gz};function jz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=D7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d}});if(i){let g=m;m=fl({inputs:{a:m,b:i},backend:a}),a.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Dc(a,m,h,o,f),a.disposeIntermediateTensorInfo(g)}return m}var qz={kernelName:Xr,backendName:"cpu",kernelFunc:jz};function Xz(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=v.sizeFromShape(n.shape),i=r.shape,o=i[i.length-1],[l,u,p,c]=T.prepareAndValidate(n,r);if(u===0)return a.makeTensorInfo(l,n.dtype,[]);let d=a.data.get(r.dataId).values,h=a.bufferSync(n),f=e7(d,h,n.dtype,u,o,p,c,n.shape,s);return a.makeTensorInfo(l,n.dtype,f.values)}var Kz={kernelName:bi,backendName:"cpu",kernelFunc:Xz};function Zz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n;Ae([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=a.data.get(s.dataId).values,p=r.shape[l];for(let b=0;b=0,()=>`GatherV2: the index value ${k} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=T.segment_util.collectGatherOpShapeInfo(r,s,l,c),f=mt({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=mt({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],x=a.bufferSync(m),y=a.bufferSync(f),A=t7(y,x,g);return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var Yz={kernelName:Fl,backendName:"cpu",kernelFunc:Zz};function Jz(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=mt({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=z7(o,!0,a),u=mt({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var Qz={kernelName:Pd,backendName:"cpu",kernelFunc:Jz},eL=ot(Ol,e=>Number.isFinite(e)?1:0,"bool"),tL={kernelName:Ol,backendName:"cpu",kernelFunc:eL},aL=ot(Dl,e=>Math.abs(e)===1/0?1:0,"bool"),nL={kernelName:Dl,backendName:"cpu",kernelFunc:aL},rL=ot(Ii,e=>Number.isNaN(e)?1:0,"bool"),sL={kernelName:Ii,backendName:"cpu",kernelFunc:rL};function iL(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=i7(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var oL={kernelName:Od,backendName:"cpu",kernelFunc:iL},lL=ot(zl,e=>Math.log1p(e)),uL={kernelName:zl,backendName:"cpu",kernelFunc:lL},dL=Lt((e,t)=>e&&t),pL=Yt(Ei,dL,null,"bool"),cL={kernelName:Ei,backendName:"cpu",kernelFunc:pL},hL=ot(Ri,e=>e?0:1,"bool"),fL={kernelName:Ri,backendName:"cpu",kernelFunc:hL},mL=Lt((e,t)=>e||t),gL=Yt(Mi,mL,null,"bool"),xL={kernelName:Mi,backendName:"cpu",kernelFunc:gL};function yL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ae(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function f(m){let g=m%u,x=m-g+Math.max(0,g-s),y=m-g+Math.min(g+s,p),A=0;for(;x<=y;x++){let b=c[x];A+=b*b}return A}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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t.isPacked?(u=hV(t.logicalShape,i,a.enableShapeUniforms),p=xV(o)):(u=fV(t.logicalShape,i,a.enableShapeUniforms),p=gV(o)),a.packedInputs&&(c+=kV),[c,l,p,r,u,s,a.userCode].join(` +`)}function du(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return PV(e,t);case 1:return OV(e,t);case 2:return zV(e,t);case 3:return BV(e,t);case 4:return VV(e,t);case 5:return UV(e);case 6:return GV(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function x6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return _V(e);case 1:return FV(e,t);case 2:return DV(e,t);case 3:return LV(e,t);default:return WV(e,t)}}function cV(e,t,a=!1,n){let r="";a?r+=x6(e,n):r+=du(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=HV(e,t):r+=jV(e,t)),r}function hV(e,t,a){switch(e.length){case 0:return y6();case 1:return wV(e,t,a);case 2:return MV(e,t,a);case 3:return SV(e,t,a);default:return CV(e,t,a)}}function fV(e,t,a){switch(e.length){case 0:return y6();case 1:return IV(e,t,a);case 2:return $V(e,t,a);case 3:return TV(e,t,a);case 4:return NV(e,t,a);case 5:return EV(e,t);case 6:return RV(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function mV(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } @@ -162,7 +162,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram void setOutput(vec4 val) { ${e.output} = val; } - `}function AV(e){return`${e.version} + `}function yV(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -217,10 +217,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${yV} + ${AV} ${bV} ${vV} - `}var yV=` + `}var AV=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -249,7 +249,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,wV=` +`,kV=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -260,11 +260,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function b6(){return` +`;function y6(){return` int getOutputCoords() { return 0; } - `}function kV(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` + `}function wV(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -357,7 +357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Oh(["r","c","d"],e)} + ${Mh(["r","c","d"],e)} return ivec3(r, c, d); } `;let n=Ao(["r","c","d"],e);return` @@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Oh(["r","c","d","d2"],e)} + ${Mh(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } `;let n=Ao(["r","c","d","d2"],e);return` @@ -535,7 +535,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function yo(e){return`offset${e}`}function _V(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ca();return` + `}function bo(e){return`offset${e}`}function _V(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ca();return` vec4 ${a}() { return ${n.texture2D}(${t}, halfCR); } @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${n}() { return sampleTexture(${a}, halfCR); } - `;let i=yo(a);if(t)return` + `;let i=bo(a);if(t)return` float ${n}() { vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], ${i}); return sampleTexture(${a}, uv); @@ -574,7 +574,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${n}(int index) { return sampleTexture(${a}, halfCR); } - `;let o=yo(a);return i===1?t?` + `;let o=bo(a);return i===1?t?` float ${n}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${a}TexShape[0])); return sampleTexture(${a}, uv); @@ -648,7 +648,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int index = round(dot(vec2(row, col), vec2(${a[1]}, 1))); ${pu(e)} } - `;let u=s[0],p=s[1],c=yo(n);return p===1?t?` + `;let u=s[0],p=s[1],c=bo(n);return p===1?t?` float ${r}(int row, int col) { float index = dot(vec3(row, col, ${c}), vec3(${n}Shape[1], 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0])); @@ -687,7 +687,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return sampleTexture(${n}, uv); } `}function LV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(a[0]===1){let d=a.slice(1),h=[1,2],f=cu(e,d),m=["b","row","col"];return` - ${y6(f,t)} + ${x6(f,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${hu(m,h)}); } @@ -748,7 +748,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${c}.0); return sampleTexture(${n}, uv); } - `;let f=yo(n);return t?` + `;let f=bo(n);return t?` float ${r}(int row, int col, int depth) { // Explicitly use integer operations as dot() only works on floats. int stride0 = ${n}Shape[1] * ${n}Shape[2]; @@ -784,10 +784,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${u}); return ${r.texture2D}(${a}, uv); } - `}function VV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=a[3],i=a[2]*s,o=a[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(a);if(l.length=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` + `}function HV(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=g6(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,x)=>`coords.${c[x+u]}`).join(", ");let h="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)h=` return vec4(outputValue.xy, outputValue.xy); 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h=0;h{i[f]=e.getUniformLocation(a,h.name,d)}),{uniformLocations:n,customUniformLocations:i,infLoc:p,nanLoc:c,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function lx(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function XV(e,t,a,n,r){t.program.enableShapeUniforms||(lx(t.inShapeInfos,a),lx([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),B().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),a.forEach((l,u)=>{let p=t.program.variableNames[u],c=t.uniformLocations[p],d=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],f=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:m}=I3(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(c,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(c,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,c,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],c=r[u];if(l.type==="float")e.gl.uniform1fv(p,c);else if(l.type==="vec2")e.gl.uniform2fv(p,c);else if(l.type==="vec3")e.gl.uniform3fv(p,c);else if(l.type==="vec4")e.gl.uniform4fv(p,c);else if(l.type==="int")e.gl.uniform1iv(p,c);else if(l.type==="ivec2")e.gl.uniform2iv(p,c);else if(l.type==="ivec3")e.gl.uniform3iv(p,c);else if(l.type==="ivec4")e.gl.uniform4iv(p,c);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function KV(e,t,a){let n="";t.concat(a).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=I3(e.packedInputs,i.shape,l),d="",h="",f="";if(p.length===1&&e.packedInputs){let k=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let k=v.computeStrides(p);f=`${k[0]===l[1]}_${k[k.length-1]===l[1]}`}let m=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),x=v.sizeFromShape(i.shape)===1,y=T.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&m===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${A}_${u?c:""}_${p.length}_${x}_${y}_${g}_${d}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${B().getNumber("WEBGL_VERSION")}`,s}function Na(e){return B().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var ZV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ca();this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Oh(["r","c","d"],e):Ao(["r","c","d"],e)} + ${this.enableShapeUniforms?Mh(["r","c","d"],e):Ao(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},YV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ca();this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` + `}},YV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ca();this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Oh(["r","c","d"],e):Ao(["r","c","d"],e)} + ${this.enableShapeUniforms?Mh(["r","c","d"],e):Ao(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1017,25 +1017,25 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } `}},JV=class{constructor(e){this.variableNames=["A"],this.outTexUsage=pn.DOWNLOAD;let t=Ca();this.outputShape=e,this.userCode=` - ${x6} + ${m6} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}},QV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=pn.DOWNLOAD;let t=Ca();this.outputShape=e,this.userCode=` - ${x6} + ${m6} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},eU={R:0,G:1,B:2,A:3},hx=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Ca();this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iM6,createBufferFromOutputTexture:()=>P6,createFloat16MatrixTexture:()=>C6,createFloat16PackedMatrixTexture:()=>R6,createFloat32MatrixTexture:()=>T6,createIndexBuffer:()=>S6,createPackedMatrixTexture:()=>E6,createUnsignedBytesMatrixTexture:()=>N6,createVertexBuffer:()=>I6,createVertexShader:()=>k6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>O6,downloadFloat32MatrixFromBuffer:()=>F6,downloadMatrixFromPackedOutputTexture:()=>z6,downloadPackedMatrixFromBuffer:()=>D6,getInternalFormatForFloat16MatrixTexture:()=>S3,getInternalFormatForFloat16PackedMatrixTexture:()=>N3,getInternalFormatForFloat32MatrixTexture:()=>I3,getInternalFormatForPackedMatrixTexture:()=>C3,getInternalFormatForUnsignedBytesMatrixTexture:()=>T3,uploadDenseMatrixToTexture:()=>$6,uploadPixelDataToTexture:()=>_6});function k6(e){let t=Ca(),a=`${t.version} + 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if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",cn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(cn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=k6(this.gl),this.indexBuffer=w6(this.gl),this.framebuffer=t6(this.gl),this.textureConfig=b3(this.gl,this.textureHalfFloatExtension)}get debug(){return B().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(A2(this.gl,this.framebuffer),this.outputTexture=null),ue(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>P6(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return F6(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return _6(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=$6(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(B().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let 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successful."),this.debug&&bc(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ue(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&bc(this.gl,this.program)),ue(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?n6(this.gl,e,t):r6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ue(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),s6(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=lu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&bc(this.gl,this.program),Xu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=aU(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in B().platform&&(a=B().platform.setTimeoutCustom.bind(B().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),vc(this.gl,e,this.framebuffer),this.debug&&Xu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(vc(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xu(this.gl)):A2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;vc(n,e,this.framebuffer),this.debug&&Xu(n),this.outputTexture=e,ue(n,()=>n.viewport(0,0,t,a)),ue(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ue(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function aU(e){let t=0;for(;t`${e}.${a}`)}function va(e,t){return t===1?[e]:B6(e,t)}function jU(e,t){if(e===1)return"rc";let a="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},U6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},W6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` ${r} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n>0?"}":""} `}this.userCode=` ${XU(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?w3():v3(e)} + ${this.enableShapeUniforms?w3():k3(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1167,7 +1167,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t?dV(["r","c","d"],"inputShape"):Ao(["r","c","d"],e)} return ivec3(r, c, d); } - `}var KU=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,a){let n=mx(t,a),r=gx(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=fx(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return n===na.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===na.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===na.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===na.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===na.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=mx(a,n),s=gx(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=fx(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function ZU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function fx(e,t,a,n,r){let s=YU(t,n),i;if(r){let[l,u]=lu(e[0],e[1]);i=l*u}else{let[l,u]=up(e[0],e[1]);i=l*u}let o=ZU(a,s);return i*o}function YU(e,t){switch(e){case na.PACKED_2X2_FLOAT32:return C3(t);case na.PACKED_2X2_FLOAT16:return N3(t);case na.UNPACKED_FLOAT32:return I3(t);case na.UNPACKED_FLOAT16:return S3(t);case na.PACKED_4X1_UNSIGNED_BYTE:return T3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function JU(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?na.PACKED_2X2_FLOAT32:na.UNPACKED_FLOAT32:e?na.PACKED_2X2_FLOAT16:na.UNPACKED_FLOAT16}function mx(e,t){if(e===pn.UPLOAD)return na.PACKED_2X2_FLOAT32;if(e===pn.RENDER||e==null)return JU(t);if(e===pn.DOWNLOAD||e===pn.PIXELS)return na.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function gx(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var qn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` + `}var KU=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,a){let n=px(t,a),r=cx(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=dx(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return n===sa.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===sa.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===sa.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===sa.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===sa.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=px(a,n),s=cx(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=dx(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=B().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function ZU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function dx(e,t,a,n,r){let s=YU(t,n),i;if(r){let[l,u]=lu(e[0],e[1]);i=l*u}else{let[l,u]=cp(e[0],e[1]);i=l*u}let o=ZU(a,s);return i*o}function YU(e,t){switch(e){case sa.PACKED_2X2_FLOAT32:return N3(t);case sa.PACKED_2X2_FLOAT16:return E3(t);case sa.UNPACKED_FLOAT32:return S3(t);case sa.UNPACKED_FLOAT16:return T3(t);case sa.PACKED_4X1_UNSIGNED_BYTE:return C3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function JU(e){return B().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?sa.PACKED_2X2_FLOAT32:sa.UNPACKED_FLOAT32:e?sa.PACKED_2X2_FLOAT16:sa.UNPACKED_FLOAT16}function px(e,t){if(e===pn.UPLOAD)return sa.PACKED_2X2_FLOAT32;if(e===pn.RENDER||e==null)return JU(t);if(e===pn.DOWNLOAD||e===pn.PIXELS)return sa.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function cx(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var qn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,7 +1178,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Cn="if (isnan(x)) return x;",QU="return x;",xx="return abs(x);",eG="return (x >= 0.0) ? x : (exp(x) - 1.0);",tG=Cn+` + `}},Cn="if (isnan(x)) return x;",QU="return x;",hx="return abs(x);",eG="return (x >= 0.0) ? x : (exp(x) - 1.0);",tG=Cn+` return (x < 0.0) ? 0.0 : x; `,aG=Cn+` return (x < 0.0) ? 0.0 : min(6.0, x); @@ -1229,10 +1229,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(getChannel(packedInput, ${i})); } - `}},dG=Tn.whereImpl,pG=1e-7,cG=1e-4,zm={};function hG(e){return e in zm||(zm[e]={}),zm[e]}var fG=W().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),mG=600;function gG(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*mG/1024/1024}var fu=class extends bl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof il)t=e;else{let a=Dn(W().getNumber("WEBGL_VERSION"),e);t=new il(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Dn(W().getNumber("WEBGL_VERSION"));t=new il(a),this.binaryCache=hG(W().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new KU(this.gpgpu),this.numMBBeforeWarning=gG(),this.texData=new vd(this,kt())}nextDataId(){return fu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Ku(t),u=new hx(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:a,values:e,usage:pn.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,a,n,r){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:a,dtype:n,values:t,usage:pn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Vr(i,Dr):c=new qn(i,Dr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=T.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vr(n,Dr):h=new qn(n,Dr);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(W().getBool("DEBUG")&&!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...uc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=T.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;le(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Vr(r,Dr):d=new qn(r,Dr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=kt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return Me(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=fG){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return kt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new uG(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new qU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Hs(e.shape),...js(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Hs(t),...js(t)],s=new U6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Ku(r),o;n?o=new YV(i):o=new ZV(i);let l=!0,u=[t!=null?t:uc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===fd.DENSE){let g=s!=null?s:uc(e.outputShape);o.texShape=g.map(x=>x*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(g.dataId);if(x.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!x.isPacked!=!!e.packedInputs)g=x.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),x=this.texData.get(g.dataId);else if(x.isPacked&&!md(x.shape,g.shape)){let A=g,y=g.shape;g.shape=x.shape,g=this.packedReshape(g,y),l.push(g),x=this.texData.get(g.dataId),A.shape=y}return{shape:g.shape,texData:x,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=KV(e,u,p),d=this.getAndSaveBinary(c,()=>qV(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),W().get("ENGINE_COMPILE_ONLY")||XV(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=W().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=$e(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(Fe(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?pG:cG}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=d6(a,o),t.texShape=p),r!=null){let c=Ku(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=lu(p[0],p[1])),o?d=new tU(c,m):d=new hx(c,m);let g=m?[f,h]:p,x=this.makeTensorInfo(g,n),A=this.texData.get(x.dataId);m?A.usage=pn.PIXELS:A.usage=pn.UPLOAD,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),h,f,r);let y=[[f,h]],b=!0,w=this.runWebGLProgram(d,[x],n,y,b),S=this.texData.get(w.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,W().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(x),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=xG(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await b4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(b3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=v6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,a){let{texture:n,height:r,width:s,channels:i}=e,o=kt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return kt().makeTensorFromDataId(l,t,a,o)}};fu.nextDataId=0;function xG(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew fu,2);var yG={forceHalfFloat:G6},R3=` + `}},dG=Tn.whereImpl,pG=1e-7,cG=1e-4,Fm={};function hG(e){return e in Fm||(Fm[e]={}),Fm[e]}var fG=B().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),mG=600;function gG(){return B().global.screen==null?1024:B().global.screen.height*B().global.screen.width*window.devicePixelRatio*mG/1024/1024}var fu=class extends Al{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!B().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof sl)t=e;else{let a=Dn(B().getNumber("WEBGL_VERSION"),e);t=new sl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Dn(B().getNumber("WEBGL_VERSION"));t=new sl(a),this.binaryCache=hG(B().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new KU(this.gpgpu),this.numMBBeforeWarning=gG(),this.texData=new wd(this,vt())}nextDataId(){return fu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Ku(t),u=new ux(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((B().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||B().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:a,values:e,usage:pn.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,a,n,r){if(B().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:a,dtype:n,values:t,usage:pn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Vr(i,Dr):c=new qn(i,Dr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=T.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vr(n,Dr):h=new qn(n,Dr);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(B().getBool("DEBUG")&&!B().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&B().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&B().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...hc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=T.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ue(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Vr(r,Dr):d=new qn(r,Dr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=vt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=fG){return B().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return vt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new uG(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new qU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Hs(e.shape),...js(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Hs(t),...js(t)],s=new W6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Ku(r),o;n?o=new YV(i):o=new ZV(i);let l=!0,u=[t!=null?t:hc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===gd.DENSE){let g=s!=null?s:hc(e.outputShape);o.texShape=g.map(x=>x*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(g.dataId);if(x.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=B().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!x.isPacked!=!!e.packedInputs)g=x.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),x=this.texData.get(g.dataId);else if(x.isPacked&&!xd(x.shape,g.shape)){let y=g,A=g.shape;g.shape=x.shape,g=this.packedReshape(g,A),l.push(g),x=this.texData.get(g.dataId),y.shape=A}return{shape:g.shape,texData:x,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=KV(e,u,p),d=this.getAndSaveBinary(c,()=>qV(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),B().get("ENGINE_COMPILE_ONLY")||XV(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=B().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!B().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(B().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Fe(()=>{if(!B().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=B().getBool("DEBUG");B().set("DEBUG",!1);let t=this.abs(ze(1e-8)).dataSync()[0];if(B().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?pG:cG}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=l6(a,o),t.texShape=p),r!=null){let c=Ku(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=lu(p[0],p[1])),o?d=new tU(c,m):d=new ux(c,m);let g=m?[f,h]:p,x=this.makeTensorInfo(g,n),y=this.texData.get(x.dataId);m?y.usage=pn.PIXELS:y.usage=pn.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),h,f,r);let A=[[f,h]],b=!0,k=this.runWebGLProgram(d,[x],n,A,b),S=this.texData.get(k.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,B().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(x),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=xG(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await y4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(v3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=A6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=vt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return vt().makeTensorFromDataId(l,t,a,o)}};fu.nextDataId=0;function xG(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew fu,2);var AG={forceHalfFloat:V6},M3=` if (isnan(a)) return a; if (isnan(b)) return b; -`,Al=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` +`,xl=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},pp=` + `}},fp=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,cp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Na(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,mp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Na(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Za(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bG={kernelName:ki,backendName:"webgl",kernelFunc:Za};function ls(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Za({inputs:{x:n},backend:a}),l=Za({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var vG={kernelName:Sd,backendName:"webgl",kernelFunc:ls},H6="return (a < 0.) ? b * a : a;",j6=` + `}};function Za(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bG={kernelName:wi,backendName:"webgl",kernelFunc:Za};function us(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Za({inputs:{x:n},backend:a}),l=Za({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var vG={kernelName:Cd,backendName:"webgl",kernelFunc:us},U6="return (a < 0.) ? b * a : a;",G6=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function wG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cp(j6,r.shape,i.shape):new Al(H6,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var kG={kernelName:Si,backendName:"webgl",kernelFunc:wG},q6="return (a < 0.) ? b * a : a;",X6=` +`;function kG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(G6,r.shape,i.shape):new xl(U6,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var wG={kernelName:Si,backendName:"webgl",kernelFunc:kG},H6="return (a < 0.) ? b * a : a;",j6=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function IG(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cp(X6,n.shape,r.shape):new Al(q6,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var SG={kernelName:Hi,backendName:"webgl",kernelFunc:IG},mu="if (isnan(x)) return x;";function Qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Vr(i.shape,t):p=new qn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function oa({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(y=>{let[b,w]=y,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Al(e,l.shape,u.shape);return p.runWebGLProgram(E,[S,C],ca(b.dtype,w.dtype))}),A=ls({inputs:{real:g,imag:x},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(x),A}let c=s||ca(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,x=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[A,y]=r(l.shape,u.shape,g,x,c),b=p.makeTensorInfo(y,c),w=p.texData.get(b.dataId);return w.values=A,b}let d=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new cp(t,l.shape,u.shape,a):h=new Al(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function gd(e,t=!1){if(e==="linear")return t?rG:QU;if(e==="relu")return t?iG:tG;if(e==="elu")return t?sG:eG;if(e==="relu6")return t?oG:aG;if(e==="prelu")return t?X6:q6;if(e==="leakyrelu")return t?j6:H6;if(e==="sigmoid")return t?lG:nG;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var K6=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=Na(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) { +`;function IG(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(j6,n.shape,r.shape):new xl(H6,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var SG={kernelName:ji,backendName:"webgl",kernelFunc:IG},mu="if (isnan(x)) return x;";function Qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=B().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Vr(i.shape,t):p=new qn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ua({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,k]=A,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:k.dataId,dtype:k.dtype,shape:u.shape},E=new xl(e,l.shape,u.shape);return p.runWebGLProgram(E,[S,C],ha(b.dtype,k.dtype))}),y=us({inputs:{real:g,imag:x},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(x),y}let c=s||ha(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,x=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[y,A]=r(l.shape,u.shape,g,x,c),b=p.makeTensorInfo(A,c),k=p.texData.get(b.dataId);return k.values=y,b}let d=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new mp(t,l.shape,u.shape,a):h=new xl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function yd(e,t=!1){if(e==="linear")return t?rG:QU;if(e==="relu")return t?iG:tG;if(e==="elu")return t?sG:eG;if(e==="relu6")return t?oG:aG;if(e==="prelu")return t?j6:H6;if(e==="leakyrelu")return t?G6:U6;if(e==="sigmoid")return t?lG:nG;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var q6=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=Na(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?m=`vec4 activation(vec4 a) { @@ -1305,16 +1305,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i} }`:m=`vec4 activation(vec4 x) { ${i} - }`,g="result = activation(result);");let x=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",y="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!md(r.shape,l)&&!(p.texture!==null&&md(p.shape,l))?CG(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var NG={kernelName:ql,backendName:"webgl",kernelFunc:ce},vx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` + `}},gx="return a * b;";function $3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=T.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new mx(fx.REAL,n.shape,r.shape),p=new mx(fx.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=wU(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new mp(gx,n.shape,r.shape):i=new xl(gx,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var TG={kernelName:Li,backendName:"webgl",kernelFunc:$3};function CG(e,t,a){let n=[Hs(e.shape),...js(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Hs(t),...js(t)],i=new W6(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!xd(r.shape,l)&&!(p.texture!==null&&xd(p.shape,l))?CG(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var NG={kernelName:Hl,backendName:"webgl",kernelFunc:pe},xx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function RG(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=T.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function bo(e,t,a,n){let r=RG(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),r=V6("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),r=B6("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[x,c,h]:[x,h,c],w=n?[A,f,d]:[A,d,f],S=ce({inputs:{x:e},backend:r,attrs:{shape:b}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[S,C],_=Math.max(x,A),$=a?S.shape[1]:S.shape[2],M=s!=null,I=i!=null,N=l==="leakyrelu",O=l!=null?gd(l,!0):null,L=M||I||N||O!=null,B;if((h===1||f===1)&&$>Z6&&L===!1){let j=S,U=C;a&&(j=Ia({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),E.push(j)),n&&(U=Ia({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(U));let H=f!==1,V=f===1,Q=j;H&&(Q=ce({inputs:{x:j},backend:r,attrs:{shape:[_,$,1]}}),E.push(Q));let Z=f===1?2:1,re=U;V&&(re=ce({inputs:{x:U},backend:r,attrs:{shape:[_,1,$]}}),E.push(re));let ee=M3({inputs:{a:Q,b:re},backend:r});B=zh({inputs:{x:ee},backend:r,attrs:{axis:Z,keepDims:!0}}),E.push(ee)}else{let j=ca(e.dtype,t.dtype),U=new K6(b,w,[_,h,f],a,n,M,O,I,N),H=[S,C];if(s!=null&&H.push(s),I&&H.push(i),N){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),E.push(V)}B=r.runWebGLProgram(U,H,j)}let G=ce({inputs:{x:B},backend:r,attrs:{shape:y}});E.push(B);for(let j of E)r.disposeIntermediateTensorInfo(j);return G}function DG(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Dc({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var zG={kernelName:jr,backendName:"webgl",kernelFunc:DG},wx="return abs(x);";function LG(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=B6(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vr(n.shape,wx):r=new qn(n.shape,wx),a.runWebGLProgram(r,[n],n.dtype)}var BG={kernelName:wl,backendName:"webgl",kernelFunc:LG},WG=Cn+` + `}};function $h(e,t,a){let n=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _G(e.shape,t):new MG(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function PG(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=$h(e,l,n),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=T.expandShapeToKeepDim(c,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,g=pe({inputs:{x:p},attrs:{shape:[m,f]},backend:n}),x=Kd(e.dtype),y=vo(g,x,"sum",n),A=pe({inputs:{x:y},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(y),u&&n.disposeIntermediateTensorInfo(p),A}function _h(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return PG(r,s,i,a)}var FG={kernelName:io,backendName:"webgl",kernelFunc:_h};function Ia(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[x,c,h]:[x,h,c],k=n?[y,f,d]:[y,d,f],S=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),C=pe({inputs:{x:t},backend:r,attrs:{shape:k}}),E=[S,C],_=Math.max(x,y),$=a?S.shape[1]:S.shape[2],M=s!=null,I=i!=null,N=l==="leakyrelu",O=l!=null?yd(l,!0):null,L=M||I||N||O!=null,W;if((h===1||f===1)&&$>X6&&L===!1){let H=S,U=C;a&&(H=Ia({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),E.push(H)),n&&(U=Ia({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(U));let j=f!==1,V=f===1,Q=H;j&&(Q=pe({inputs:{x:H},backend:r,attrs:{shape:[_,$,1]}}),E.push(Q));let Z=f===1?2:1,re=U;V&&(re=pe({inputs:{x:U},backend:r,attrs:{shape:[_,1,$]}}),E.push(re));let ee=$3({inputs:{a:Q,b:re},backend:r});W=_h({inputs:{x:ee},backend:r,attrs:{axis:Z,keepDims:!0}}),E.push(ee)}else{let H=ha(e.dtype,t.dtype),U=new q6(b,k,[_,h,f],a,n,M,O,I,N),j=[S,C];if(s!=null&&j.push(s),I&&j.push(i),N){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));j.push(V),E.push(V)}W=r.runWebGLProgram(U,j,H)}let G=pe({inputs:{x:W},backend:r,attrs:{shape:A}});E.push(W);for(let H of E)r.disposeIntermediateTensorInfo(H);return G}function DG(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Bc({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var zG={kernelName:jr,backendName:"webgl",kernelFunc:DG},yx="return abs(x);";function LG(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=z6(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vr(n.shape,yx):r=new qn(n.shape,yx),a.runWebGLProgram(r,[n],n.dtype)}var BG={kernelName:vl,backendName:"webgl",kernelFunc:LG},WG=Cn+` if (abs(x) > 1.) { return NAN; } return acos(x); `,VG=Qe({opSnippet:WG}),UG={kernelName:kl,backendName:"webgl",kernelFunc:VG},GG=Cn+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:Il,backendName:"webgl",kernelFunc:HG},kx="return a + b;",qG=oa({opSnippet:kx,packedOpSnippet:kx,supportsComplex:!0,cpuKernelImpl:nU}),XG={kernelName:ts,backendName:"webgl",kernelFunc:qG},KG=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:wl,backendName:"webgl",kernelFunc:HG},Ax="return a + b;",qG=ua({opSnippet:Ax,packedOpSnippet:Ax,supportsComplex:!0,cpuKernelImpl:nU}),XG={kernelName:as,backendName:"webgl",kernelFunc:qG},KG=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:Il,back vec4 result = ${n}; setOutput(result); } - `}};function bc(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Za({inputs:{x:n[0]},backend:a});if(n.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=bc({inputs:n.slice(0,o),backend:a}),u=bc({inputs:n.slice(o),backend:a});return bc({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=W().getBool("WEBGL_PACK")?new ZG(n[0].shape,s):new KG(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var YG={kernelName:Ks,backendName:"webgl",kernelFunc:bc};function JG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ia({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=ce({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=bo(m,m.dtype,"all",a),x;if(i){let A=T.expandShapeToKeepDim(d,l);x=ce({inputs:{x:g},backend:a,attrs:{shape:A}})}else x=ce({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),x}var QG={kernelName:Zs,backendName:"webgl",kernelFunc:JG};function eH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ia({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=ce({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=bo(m,m.dtype,"any",a),x;if(i){let A=T.expandShapeToKeepDim(d,l);x=ce({inputs:{x:g},backend:a,attrs:{shape:A}})}else x=ce({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),x}var tH={kernelName:Ys,backendName:"webgl",kernelFunc:eH},aH=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Ic(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Za({inputs:{x:n[0]},backend:a});if(n.length>B().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Ic({inputs:n.slice(0,o),backend:a}),u=Ic({inputs:n.slice(o),backend:a});return Ic({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>ha(o,l)),s=n.map(o=>o.shape),i=B().getBool("WEBGL_PACK")?new ZG(n[0].shape,s):new KG(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var YG={kernelName:Ks,backendName:"webgl",kernelFunc:Ic};function JG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ia({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"all",a),x;if(i){let y=T.expandShapeToKeepDim(d,l);x=pe({inputs:{x:g},backend:a,attrs:{shape:y}})}else x=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),x}var QG={kernelName:Zs,backendName:"webgl",kernelFunc:JG};function eH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ia({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"any",a),x;if(i){let y=T.expandShapeToKeepDim(d,l);x=pe({inputs:{x:g},backend:a,attrs:{shape:y}})}else x=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),x}var tH={kernelName:Ys,backendName:"webgl",kernelFunc:eH},aH=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1575,15 +1575,15 @@ return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:Il,back ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; - --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],f=d.map(C=>"int "+C),m=va("sourceLocR",c-1).concat("inIdx.r"),g=va("sourceLocG",c-1).concat("inIdx.g"),x=va("sourceLocB",c-1).concat("inIdx.b"),A=va("sourceLocA",c-1).concat("inIdx.a"),y=a==="max"?"greaterThan":"lessThan",b=n?"":` + --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],f=d.map(C=>"int "+C),m=va("sourceLocR",c-1).concat("inIdx.r"),g=va("sourceLocG",c-1).concat("inIdx.g"),x=va("sourceLocB",c-1).concat("inIdx.b"),y=va("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${m.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), - getBestIndicesAChannel(${A.join()})));`,w=`vec4( + getBestIndicesAChannel(${y.join()})));`,k=`vec4( getAChannel(${m.join()}), hasNextCol ? getAChannel(${g.join()}) : 0., hasNextRow ? getAChannel(${x.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,S=n?"":` + hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,S=n?"":` float getBestIndicesAChannel(${f.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); @@ -1602,15 +1602,15 @@ return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:Il,back sourceLocB${h}, sourceLocA${h}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); - vec4 bestValue = ${w}; + vec4 bestValue = ${k}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${b} - vec4 candidate = ${w}; + vec4 candidate = ${k}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( - vec4(${y}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + vec4(${A}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,HG=Qe({opSnippet:GG}),jG={kernelName:Il,back } setOutput(bestIndex); } - `}};function Y6(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new aH(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=Y6(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function J6(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new nH(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=J6(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function Q6(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=Y6(e,d,n);s.push(h);let f=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return J6(e,t,n)}function rH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=Q6(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var sH={kernelName:Js,backendName:"webgl",kernelFunc:rH};function iH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Q6(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var oH={kernelName:kd,backendName:"webgl",kernelFunc:iH},lH=Cn+` + `}};function K6(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new aH(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=K6(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function Z6(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new nH(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=Z6(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function Y6(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!B().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=K6(e,d,n);s.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Z6(e,t,n)}function rH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=Y6(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var sH={kernelName:Js,backendName:"webgl",kernelFunc:rH};function iH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Y6(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var oH={kernelName:Sd,backendName:"webgl",kernelFunc:iH},lH=Cn+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,uH=Qe({opSnippet:lH}),dH={kernelName:Sl,backendName:"webgl",kernelFunc:uH},pH=Cn+"return log(x + sqrt(x * x + 1.0));",cH=Qe({opSnippet:pH}),hH={kernelName:Tl,backendName:"webgl",kernelFunc:cH},fH=Cn+` +`,uH=Qe({opSnippet:lH}),dH={kernelName:Il,backendName:"webgl",kernelFunc:uH},pH=Cn+"return log(x + sqrt(x * x + 1.0));",cH=Qe({opSnippet:pH}),hH={kernelName:Sl,backendName:"webgl",kernelFunc:cH},fH=Cn+` return atan(x); -`,mH=Qe({opSnippet:fH}),gH={kernelName:Cl,backendName:"webgl",kernelFunc:mH},xH=R3+` +`,mH=Qe({opSnippet:fH}),gH={kernelName:Tl,backendName:"webgl",kernelFunc:mH},xH=M3+` return atan(a, b); -`,AH=` +`,yH=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+pp+` + `+fp+` return result; -`,yH=oa({opSnippet:xH,packedOpSnippet:AH}),bH={kernelName:El,backendName:"webgl",kernelFunc:yH},vH=Cn+` +`,AH=ua({opSnippet:xH,packedOpSnippet:yH}),bH={kernelName:Nl,backendName:"webgl",kernelFunc:AH},vH=Cn+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelName:Nl,backendName:"webgl",kernelFunc:wH},xd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,x="0.0";if(f||(x="-1.0 / 1e-20"),a){let C=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,kH=Qe({opSnippet:vH}),wH={kernelName:Cl,backendName:"webgl",kernelFunc:kH},Ad=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,x="0.0";if(f||(x="-1.0 / 1e-20"),a){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${h}); @@ -1690,11 +1690,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let A="max",y=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(y="avgValue / count");let b=Math.floor(s/4)*4,w=s%4,S=` + `;return}let y="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / max(count, 1.0)");let b=Math.floor(s/4)*4,k=s%4,S=` if (${f}) { avgValue += dot(values, ones); } else { - minMaxValue = ${A}(values, minMaxValue); + minMaxValue = ${y}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); @@ -1749,7 +1749,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } int xC = xCCorner + ${b}; - if (${w===1}) { + if (${k===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, @@ -1758,7 +1758,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ); ${S} - } else if (${w===2}) { + } else if (${k===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), @@ -1767,7 +1767,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ); ${S} - } else if (${w===3}) { + } else if (${k===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), @@ -1778,9 +1778,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${S} } } - setOutput(${y}); + setOutput(${A}); } - `}},$3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,x=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",y="0.0";if(A||(y="-1.0 / 1e-20"),a){let _=">=";this.userCode=` + `}},_3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,x=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",A="0.0";if(y||(A="-1.0 / 1e-20"),a){let _=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${x}); @@ -1842,8 +1842,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let S=Math.floor(s/4)*4,C=s%4,E=` - if (${A}) { + `;return}let b="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / max(count, 1.0)");let S=Math.floor(s/4)*4,C=s%4,E=` + if (${y}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); @@ -1852,7 +1852,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${x}); - const float initializationValue = ${y}; + const float initializationValue = ${A}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; @@ -1877,7 +1877,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined - vec4 minMaxValue = vec4(${y}); + vec4 minMaxValue = vec4(${A}); float avgValue = 0.0; count = 0.0; @@ -1940,10 +1940,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${E} } } - setOutput(${w}); } + setOutput(${k}); } - `}};function IH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Za({inputs:{x:r},backend:a});let c=new xd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var SH={kernelName:Qs,backendName:"webgl",kernelFunc:IH};function TH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new $3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var CH={kernelName:Hc,backendName:"webgl",kernelFunc:TH},NH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` + `}};function IH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Za({inputs:{x:r},backend:a});let c=new Ad(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var SH={kernelName:Qs,backendName:"webgl",kernelFunc:IH};function TH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new _3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var CH={kernelName:Zc,backendName:"webgl",kernelFunc:TH},NH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${c}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}};function RH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new EH(d);return a.runWebGLProgram(h,[r],i.dtype)}var MH={kernelName:G1,backendName:"webgl",kernelFunc:RH};function $H(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;uu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new NH(p);return a.runWebGLProgram(c,[r],i.dtype)}var _H={kernelName:U1,backendName:"webgl",kernelFunc:$H};function PH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Dc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var FH={kernelName:ei,backendName:"webgl",kernelFunc:PH},OH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function RH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new EH(d);return a.runWebGLProgram(h,[r],i.dtype)}var MH={kernelName:L2,backendName:"webgl",kernelFunc:RH};function $H(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;uu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new NH(p);return a.runWebGLProgram(c,[r],i.dtype)}var _H={kernelName:Kc,backendName:"webgl",kernelFunc:$H};function PH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Bc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var FH={kernelName:ei,backendName:"webgl",kernelFunc:PH},OH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam setOutput((x - mean) * inv + offset); } - `}},zH=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=W().getBool("WEBGL_PACK_NORMALIZATION")?new DH(n.shape,r.shape,s.shape,p,c,l):new OH(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},LH={kernelName:yi,backendName:"webgl",kernelFunc:zH},BH=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=WH(this.rank),n,r=e.map((s,i)=>`sourceLoc.${T1[i]} = start[${i}] + coords.${T1[i]};`);n=` + `}},zH=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=B().getBool("WEBGL_PACK_NORMALIZATION")?new DH(n.shape,r.shape,s.shape,p,c,l):new OH(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},LH={kernelName:Ai,backendName:"webgl",kernelFunc:zH},BH=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=WH(this.rank),n,r=e.map((s,i)=>`sourceLoc.${k2[i]} = start[${i}] + coords.${k2[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${n} setOutput(getSource(${a})); } - `}},T1=["x","y","z","w","u","v"];function WH(e){if(e===1)return"sourceLoc";if(e<=6)return T1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var VH=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),a=va("coords",this.rank),n=va("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` + `}},k2=["x","y","z","w","u","v"];function WH(e){if(e===1)return"sourceLoc";if(e<=6)return k2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var VH=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),a=va("coords",this.rank),n=va("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` result.x = ${s}; if (++${a[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${o} setOutput(result); } - `}};function UH(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=It.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function gu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=It.parseSliceParams(r,s,i);if(It.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=PU(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=It.isSliceContinous(r.shape,o,l);if(u||!p){let c=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VH(l):new BH(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),UH(r,o,l,a)}var GH={kernelName:Zl,backendName:"webgl",kernelFunc:gu},HH=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,y)=>A*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=ce({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ia({inputs:{x:f},backend:a,attrs:{perm:u}}),g=ce({inputs:{x:m},backend:a,attrs:{shape:p}}),x=gu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>a.disposeIntermediateTensorInfo(A)),x},jH={kernelName:Rl,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=L6(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var XH={kernelName:Id,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var ZH={kernelName:jc,backendName:"webgl",kernelFunc:KH},YH="return float(a != b);",ev=oa({opSnippet:YH,cpuKernelImpl:SU,dtype:"bool"}),JH={kernelName:Li,backendName:"webgl",kernelFunc:ev};function hp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Za({inputs:{x:r.complexTensorInfos.real},backend:a})}var QH={kernelName:Md,backendName:"webgl",kernelFunc:hp},ej="return float(int(x));";function tj(e,t){let a=new qn(e.shape,ej),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function C1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Za({inputs:{x:r},backend:a});let i=fn(r.shape),o=C1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ls({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=hp({inputs:{input:r},backend:a}),o=C1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Za({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=sU(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return tj(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=ev({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var aj={kernelName:ti,backendName:"webgl",kernelFunc:C1},Ix="return ceil(x);",nj=Qe({opSnippet:Ix,packedOpSnippet:Ix,cpuKernelImpl:iU}),rj={kernelName:ai,backendName:"webgl",kernelFunc:nj},sj=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function UH(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=St.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function gu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=PU(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=St.isSliceContinous(r.shape,o,l);if(u||!p){let c=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VH(l):new BH(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),UH(r,o,l,a)}var GH={kernelName:Xl,backendName:"webgl",kernelFunc:gu},HH=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ia({inputs:{x:f},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:m},backend:a,attrs:{shape:p}}),x=gu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>a.disposeIntermediateTensorInfo(y)),x},jH={kernelName:El,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=D6(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var XH={kernelName:Td,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var ZH={kernelName:Yc,backendName:"webgl",kernelFunc:KH},YH="return float(a != b);",J6=ua({opSnippet:YH,cpuKernelImpl:SU,dtype:"bool"}),JH={kernelName:Bi,backendName:"webgl",kernelFunc:J6};function gp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Za({inputs:{x:r.complexTensorInfos.real},backend:a})}var QH={kernelName:zd,backendName:"webgl",kernelFunc:gp},ej="return float(int(x));";function tj(e,t){let a=new qn(e.shape,ej),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function w2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Za({inputs:{x:r},backend:a});let i=fn(r.shape),o=w2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=us({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=gp({inputs:{input:r},backend:a}),o=w2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Za({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=sU(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return tj(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=J6({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var aj={kernelName:ti,backendName:"webgl",kernelFunc:w2},bx="return ceil(x);",nj=Qe({opSnippet:bx,packedOpSnippet:bx,cpuKernelImpl:iU}),rj={kernelName:ai,backendName:"webgl",kernelFunc:nj},sj=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2125,7 +2125,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function oj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;W().getBool("WEBGL_PACK_CLIP")?o=new ij(r.shape):o=new sj(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var lj={kernelName:as,backendName:"webgl",kernelFunc:oj},uj=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function oj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;B().getBool("WEBGL_PACK_CLIP")?o=new ij(r.shape):o=new sj(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var lj={kernelName:ns,backendName:"webgl",kernelFunc:oj},uj=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2138,7 +2138,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function Sx(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function dj(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new uj(n.shape),i=[Sx(n,r.complexTensorInfos.real),Sx(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var pj={kernelName:qc,backendName:"webgl",kernelFunc:dj},cj=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s= ${o[f-1]}) { return getChannel( - getT${f}(${pc(i,l,m)}), - vec2(${pc(u,l,m)})); + getT${f}(${mc(i,l,m)}), + vec2(${mc(u,l,m)})); }`}let d=o.length,h=o[o.length-1];c+=` return getChannel( - getT${d}(${pc(i,l,h)}), - vec2(${pc(u,l,h)}));`,this.userCode=` + getT${d}(${mc(i,l,h)}), + vec2(${mc(u,l,h)}));`,this.userCode=` float getValue(${i.map(f=>"int "+f)}) { ${c} } @@ -2184,18 +2184,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(result); } - `}};function pc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - 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h.forEach(b=>a.disposeIntermediateTensorInfo(b)),y}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new qn(e[0].shape,Dr):new Vr(e[0].shape,Dr);return a.runWebGLProgram(h,e,n)}let o=W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let m=0;mf.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=mj(s,t,a),p=new cj(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=ce({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function mj(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function tv(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Za({inputs:{x:l[0]},backend:a}):Zu(l,s,a)}var gj={kernelName:Ml,backendName:"webgl",kernelFunc:tv},av=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,x=m?2:3,A=m?3:1,y="",b="";a&&(n?y=`float activation(float a) { + `}};function mc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function Ph(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Za({inputs:{x:r.complexTensorInfos.imag},backend:a})}var fj={kernelName:Fd,backendName:"webgl",kernelFunc:Ph};function Zu(e,t,a){let 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gj={kernelName:Rl,backendName:"webgl",kernelFunc:Q6},ev=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,x=m?2:3,y=m?3:1,A="",b="";a&&(n?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} - }`:r?y=`float activation(float a) { + }`:r?A=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${a} - }`:y=` + }`:A=` float activation(float x) { ${a} } - `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` - ${y} + `,b="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + ${A} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); @@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d2 = coords[${A}]; + int d2 = coords[${y}]; ivec2 xRCCorner = ivec2(coords[${g}], coords[${x}]) * strides - pads; @@ -2314,7 +2314,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } float result = dotProd; - ${w} + ${k} ${b} setOutput(result); } @@ -2406,7 +2406,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}},nv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` + `}},tv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function rv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,x=[];if(s!=null){let A=zc(s.shape,h);A!=null&&(s=ce({inputs:{x:s},backend:n,attrs:{shape:A}}),x.push(s))}if(r!=null){let A=zc(r.shape,h);A!=null&&(r=ce({inputs:{x:r},backend:n,attrs:{shape:A}}),x.push(r))}if(!((c===1||d===1)&&p>Z6)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),y={dataId:e.dataId,shape:[1,A,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(md(u.shape,y.shape),()=>`packed reshape ${u.shape} to ${y.shape} isn't free`);let w=ce({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});x.push(w);let S=Dc({a:y,b:w,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Za({inputs:{x:S},backend:n}),g.shape=a.outShape,x.push(S)}else{let A=a.outHeight*a.outWidth,y=ce({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,A,a.inChannels]:[a.batchSize,a.inChannels,A]}}),b=ce({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=Dc({a:h?y:b,b:h?b:y,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),x.push(y),x.push(b),x.push(w)}for(let A of x)n.disposeIntermediateTensorInfo(A);return g}function sv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,x=[a.batchSize,m,g],A=!0,y=!1,b=[];if(s!=null){let j=zc(s.shape,f);j!=null&&(s=ce({inputs:{x:s},backend:n,attrs:{shape:j}}),b.push(s))}if(r!=null){let j=zc(r.shape,f);j!=null&&(r=ce({inputs:{x:r},backend:n,attrs:{shape:j}}),b.push(r))}let w=ce({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w);let S=new Aj(x,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],E=n.runWebGLProgram(S,[e],"float32",C),_=ce({inputs:{x:E},backend:n,attrs:{shape:x}});b.push(E),b.push(_);let $=r!=null,M=s!=null,I=o==="leakyrelu",N=o?gd(o,!0):null,O=new K6(f?_.shape:w.shape,f?w.shape:_.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],A,y,$,N,M,I),L=f?[_,w]:[w,_];if(r&&L.push(r),M&&L.push(s),I){let j=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));L.push(j),b.push(j)}let B=n.runWebGLProgram(O,L,"float32"),G=ce({inputs:{x:B},backend:n,attrs:{shape:a.outShape}});b.push(B);for(let j of b)n.disposeIntermediateTensorInfo(j);return G}function yj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=rv({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&W().getBool("WEBGL_EXP_CONV")){let m=new nv(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(W().getBool("WEBGL_CONV_IM2COL"))h=sv({x:r,filter:s,convInfo:d,backend:a});else{let m=new av(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=ce({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var bj={kernelName:ni,backendName:"webgl",kernelFunc:yj},vj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function Wc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function av({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,x=[];if(s!=null){let y=Wc(s.shape,h);y!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:y}}),x.push(s))}if(r!=null){let y=Wc(r.shape,h);y!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:y}}),x.push(r))}if(!((c===1||d===1)&&p>X6)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,y,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(xd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});x.push(k);let S=Bc({a:A,b:k,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Za({inputs:{x:S},backend:n}),g.shape=a.outShape,x.push(S)}else{let y=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,y,a.inChannels]:[a.batchSize,a.inChannels,y]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),k=Bc({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:k},backend:n,attrs:{shape:a.outShape}}),x.push(A),x.push(b),x.push(k)}for(let y of x)n.disposeIntermediateTensorInfo(y);return g}function nv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,x=[a.batchSize,m,g],y=!0,A=!1,b=[];if(s!=null){let H=Wc(s.shape,f);H!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:H}}),b.push(s))}if(r!=null){let H=Wc(r.shape,f);H!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:H}}),b.push(r))}let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(k);let S=new yj(x,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],E=n.runWebGLProgram(S,[e],"float32",C),_=pe({inputs:{x:E},backend:n,attrs:{shape:x}});b.push(E),b.push(_);let $=r!=null,M=s!=null,I=o==="leakyrelu",N=o?yd(o,!0):null,O=new q6(f?_.shape:k.shape,f?k.shape:_.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],y,A,$,N,M,I),L=f?[_,k]:[k,_];if(r&&L.push(r),M&&L.push(s),I){let H=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));L.push(H),b.push(H)}let W=n.runWebGLProgram(O,L,"float32"),G=pe({inputs:{x:W},backend:n,attrs:{shape:a.outShape}});b.push(W);for(let H of b)n.disposeIntermediateTensorInfo(H);return G}function Aj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=av({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&B().getBool("WEBGL_EXP_CONV")){let m=new tv(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(B().getBool("WEBGL_CONV_IM2COL"))h=nv({x:r,filter:s,convInfo:d,backend:a});else{let m=new ev(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var bj={kernelName:ni,backendName:"webgl",kernelFunc:Aj},vj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}},wj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + `}},kj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2744,7 +2744,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}},kj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},wj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2843,14 +2843,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}};function Sj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new vj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Tj={kernelName:Xc,backendName:"webgl",kernelFunc:Sj};function Cj(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=new wj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Nj={kernelName:ri,backendName:"webgl",kernelFunc:Cj};function Ej(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new xj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Rj={kernelName:Kc,backendName:"webgl",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new kj(u);return a.runWebGLProgram(p,[r,s],"float32")}var $j={kernelName:H1,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Ij(u);return a.runWebGLProgram(p,[r,s],"float32")}var Pj={kernelName:Zc,backendName:"webgl",kernelFunc:_j},Fj=mu+` + `}};function Sj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new vj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Tj={kernelName:Nd,backendName:"webgl",kernelFunc:Sj};function Cj(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=new kj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Nj={kernelName:ri,backendName:"webgl",kernelFunc:Cj};function Ej(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new xj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Rj={kernelName:Qc,backendName:"webgl",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new wj(u);return a.runWebGLProgram(p,[r,s],"float32")}var $j={kernelName:B2,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Ij(u);return a.runWebGLProgram(p,[r,s],"float32")}var Pj={kernelName:eh,backendName:"webgl",kernelFunc:_j},Fj=mu+` return cos(x); `,Oj=Qe({opSnippet:Fj}),Dj={kernelName:si,backendName:"webgl",kernelFunc:Oj},zj=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Lj=Qe({opSnippet:zj}),Bj={kernelName:ii,backendName:"webgl",kernelFunc:Lj},Wj=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,x]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,y,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` +`,Lj=Qe({opSnippet:zj}),Bj={kernelName:ii,backendName:"webgl",kernelFunc:Lj},Wj=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,x]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); - const float width_ratio = float(${A}); + const float width_ratio = float(${y}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2871,7 +2871,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } float height_scale = ${g}; - float width_scale = ${y}; + float width_scale = ${A}; float in_y = ${x}; if( in_y < 0.0 || in_y > ${h} ) { @@ -2909,20 +2909,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam setOutput(newValue); } } - `}},Vj=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Wj(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},Uj={kernelName:ui,backendName:"webgl",kernelFunc:Vj},Ad;(function(e){e.Prod="*",e.Sum="+"})(Ad||(Ad={}));var Tx=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Ad.Prod?"1.0":"0.0",i=a?s:`getX(${Cx(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` + `}},Vj=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Wj(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},Uj={kernelName:ui,backendName:"webgl",kernelFunc:Vj},bd;(function(e){e.Prod="*",e.Sum="+"})(bd||(bd={}));var kx=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===bd.Prod?"1.0":"0.0",i=a?s:`getX(${wx(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${gt(r)} coords = getOutputCoords(); - int end = ${Nx(r,"coords",this.op)}; + int end = ${Ix(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${Nx(r,"coords",this.op)} = idx; - val ${this.op}= getX(${Cx(r,"coords",this.op)}); + ${Ix(r,"coords",this.op)} = idx; + val ${this.op}= getX(${wx(r,"coords",this.op)}); } setOutput(val); } - `}};function Cx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Nx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function iv(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Ia({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Za({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Tx(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new Tx(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=Ia({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function Gj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return iv(Ad.Prod,r,a,s,i,o)}var Hj={kernelName:oi,backendName:"webgl",kernelFunc:Gj};function jj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return iv(Ad.Sum,r,a,s,i,o)}var qj={kernelName:li,backendName:"webgl",kernelFunc:jj};function Xj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=L6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=rU(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Kj={kernelName:Td,backendName:"webgl",kernelFunc:Xj},Zj=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` + `}};function wx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Ix(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function rv(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Ia({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Za({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new kx(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new kx(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=Ia({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function Gj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return rv(bd.Prod,r,a,s,i,o)}var Hj={kernelName:oi,backendName:"webgl",kernelFunc:Gj};function jj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return rv(bd.Sum,r,a,s,i,o)}var qj={kernelName:li,backendName:"webgl",kernelFunc:jj};function Xj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=D6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=rU(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Kj={kernelName:Ed,backendName:"webgl",kernelFunc:Xj},Zj=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2941,7 +2941,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Yj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new Zj(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var Jj={kernelName:di,backendName:"webgl",kernelFunc:Yj},ov=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Yj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new Zj(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var Jj={kernelName:di,backendName:"webgl",kernelFunc:Yj},sv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?l=`float activation(float a) { @@ -2994,7 +2994,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${u} setOutput(result); } - `}},lv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` + `}},iv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Na(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); @@ -3076,10 +3076,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } `:d+=` xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy); - `):A===1?d+=` + `):y===1?d+=` xC${x+1} = xTexelC${x}; `:d+=` - xCOffset = xC + ${A}; + xCOffset = xC + ${y}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); @@ -3183,7 +3183,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${f} setOutput(result); } - `}};function Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;W().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new lv(c):d=new ov(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var eq={kernelName:pi,backendName:"webgl",kernelFunc:Qj},tq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;B().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new iv(c):d=new sv(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var eq={kernelName:pi,backendName:"webgl",kernelFunc:Qj},tq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3263,13 +3263,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}};function nq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new tq(c);return a.runWebGLProgram(d,[r,s],"float32")}var rq={kernelName:Yc,backendName:"webgl",kernelFunc:nq};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new aq(c);return a.runWebGLProgram(d,[r,s],"float32")}var iq={kernelName:Jc,backendName:"webgl",kernelFunc:sq},oq=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function nq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new tq(c);return a.runWebGLProgram(d,[r,s],"float32")}var rq={kernelName:th,backendName:"webgl",kernelFunc:nq};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new aq(c);return a.runWebGLProgram(d,[r,s],"float32")}var iq={kernelName:ah,backendName:"webgl",kernelFunc:sq},oq=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function lq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ce({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new oq(s),l=a.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var uq={kernelName:Qc,backendName:"webgl",kernelFunc:lq},dq=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` + `}};function lq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new oq(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var uq={kernelName:Rd,backendName:"webgl",kernelFunc:lq},dq=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${c}); const float neg_infinity = -3.4e38; @@ -3307,7 +3307,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam float result = curVal; setOutput(result); } - `}};function pq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new dq(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=ce({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var cq={kernelName:eh,backendName:"webgl",kernelFunc:pq};function hq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=zh({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var fq={kernelName:Cd,backendName:"webgl",kernelFunc:hq},mq="return (x >= 0.0) ? x : (exp(x) - 1.0);",gq=` + `}};function pq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new dq(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var cq={kernelName:Md,backendName:"webgl",kernelFunc:pq};function hq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=_h({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var fq={kernelName:$d,backendName:"webgl",kernelFunc:hq},mq="return (x >= 0.0) ? x : (exp(x) - 1.0);",gq=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3316,12 +3316,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,xq=Qe({opSnippet:mq,packedOpSnippet:gq}),Aq={kernelName:hi,backendName:"webgl",kernelFunc:xq},yq="return (b >= 1.0) ? a : a * (b + 1.0);",bq=` +`,xq=Qe({opSnippet:mq,packedOpSnippet:gq}),yq={kernelName:hi,backendName:"webgl",kernelFunc:xq},Aq="return (b >= 1.0) ? a : a * (b + 1.0);",bq=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,vq=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cp(bq,n.shape,r.shape):new Al(yq,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},wq={kernelName:j1,backendName:"webgl",kernelFunc:vq},kq=` +`,vq=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(bq,n.shape,r.shape):new xl(Aq,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},kq={kernelName:W2,backendName:"webgl",kernelFunc:vq},wq=` return vec4(equal(a, b)); -`,Iq="return float(a == b);",Sq=oa({opSnippet:Iq,packedOpSnippet:kq,dtype:"bool",cpuKernelImpl:lU}),Tq={kernelName:fi,backendName:"webgl",kernelFunc:Sq},Cq=` +`,Iq="return float(a == b);",Sq=ua({opSnippet:Iq,packedOpSnippet:wq,dtype:"bool",cpuKernelImpl:lU}),Tq={kernelName:fi,backendName:"webgl",kernelFunc:Sq},Cq=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3336,7 +3336,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,Nq=Qe({opSnippet:Cq}),Eq={kernelName:$l,backendName:"webgl",kernelFunc:Nq},Rq=mu+` +`,Nq=Qe({opSnippet:Cq}),Eq={kernelName:Ml,backendName:"webgl",kernelFunc:Nq},Rq=mu+` return exp(x); `,Mq=` vec4 result = exp(x); @@ -3347,7 +3347,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,uv=Qe({opSnippet:Rq,packedOpSnippet:Mq,cpuKernelImpl:uU,dtype:"float32"}),$q={kernelName:mi,backendName:"webgl",kernelFunc:uv};function N1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:n,attrs:{shape:o}})}var _q={kernelName:_l,backendName:"webgl",kernelFunc:N1},Ex="return exp(x) - 1.0;",Pq=Qe({opSnippet:Ex,packedOpSnippet:Ex,cpuKernelImpl:dU}),Fq={kernelName:Pl,backendName:"webgl",kernelFunc:Pq},Rx=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,ov=Qe({opSnippet:Rq,packedOpSnippet:Mq,cpuKernelImpl:uU,dtype:"float32"}),$q={kernelName:mi,backendName:"webgl",kernelFunc:ov};function I2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var _q={kernelName:$l,backendName:"webgl",kernelFunc:I2},Sx="return exp(x) - 1.0;",Pq=Qe({opSnippet:Sx,packedOpSnippet:Sx,cpuKernelImpl:dU}),Fq={kernelName:_l,backendName:"webgl",kernelFunc:Pq},Tx=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3380,12 +3380,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function dv(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Rx("real",l,t),p=new Rx("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=ls({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=ce({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function Oq(e){let{inputs:t,backend:a}=e,{input:n}=t;return dv(n,!1,a)}var Dq={kernelName:Nd,backendName:"webgl",kernelFunc:Oq},zq=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function lv(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Tx("real",l,t),p=new Tx("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function Oq(e){let{inputs:t,backend:a}=e,{input:n}=t;return lv(n,!1,a)}var Dq={kernelName:_d,backendName:"webgl",kernelFunc:Oq},zq=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function fp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new zq(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Lq={kernelName:Fl,backendName:"webgl",kernelFunc:fp},Bq=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function xp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new zq(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Lq={kernelName:Pl,backendName:"webgl",kernelFunc:xp},Bq=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3399,7 +3399,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(outputValue); } - `}},Wq={kernelName:gi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Bq(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},Mx="return floor(x);",Vq=Qe({opSnippet:Mx,packedOpSnippet:Mx,cpuKernelImpl:pU}),Uq={kernelName:xi,backendName:"webgl",kernelFunc:Vq},Gq=` + `}},Wq={kernelName:gi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Bq(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},Cx="return floor(x);",Vq=Qe({opSnippet:Cx,packedOpSnippet:Cx,cpuKernelImpl:pU}),Uq={kernelName:xi,backendName:"webgl",kernelFunc:Vq},Gq=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3430,7 +3430,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,jq=oa({opSnippet:Gq,packedOpSnippet:Hq,dtype:"int32"}),qq={kernelName:Ai,backendName:"webgl",kernelFunc:jq},Xq=class{constructor(e){this.variableNames=["A"];let t=Ca(),[a,n]=e;this.outputShape=e,this.userCode=` +`,jq=ua({opSnippet:Gq,packedOpSnippet:Hq,dtype:"int32"}),qq={kernelName:yi,backendName:"webgl",kernelFunc:jq},Xq=class{constructor(e){this.variableNames=["A"];let t=Ca(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,EX=oa({opSnippet:CX,packedOpSnippet:NX,cpuKernelImpl:gU,dtype:"bool"}),RX={kernelName:Ti,backendName:"webgl",kernelFunc:EX},MX="return float(a <= b);",$X=` +`,EX=ua({opSnippet:CX,packedOpSnippet:NX,cpuKernelImpl:gU,dtype:"bool"}),RX={kernelName:Ti,backendName:"webgl",kernelFunc:EX},MX="return float(a <= b);",$X=` return vec4(lessThanEqual(a, b)); -`,_X=oa({opSnippet:MX,packedOpSnippet:$X,cpuKernelImpl:xU,dtype:"bool"}),PX={kernelName:Ci,backendName:"webgl",kernelFunc:_X};function FX(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=AU(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var OX={kernelName:th,backendName:"webgl",kernelFunc:FX},DX=mu+` +`,_X=ua({opSnippet:MX,packedOpSnippet:$X,cpuKernelImpl:xU,dtype:"bool"}),PX={kernelName:Ci,backendName:"webgl",kernelFunc:_X};function FX(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=yU(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var OX={kernelName:Od,backendName:"webgl",kernelFunc:FX},DX=mu+` return x < 0.0 ? 0./0. : log(x); `,zX=` vec4 result = log(x); @@ -3526,18 +3526,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,LX=Qe({opSnippet:DX,packedOpSnippet:zX,cpuKernelImpl:yU}),BX={kernelName:Ni,backendName:"webgl",kernelFunc:LX},WX=mu+` +`,LX=Qe({opSnippet:DX,packedOpSnippet:zX,cpuKernelImpl:AU}),BX={kernelName:Ni,backendName:"webgl",kernelFunc:LX},WX=mu+` return log(1.0 + x); -`,VX=Qe({opSnippet:WX}),UX={kernelName:Ll,backendName:"webgl",kernelFunc:VX},GX="return float(a >= 1.0 && b >= 1.0);",HX=` +`,VX=Qe({opSnippet:WX}),UX={kernelName:zl,backendName:"webgl",kernelFunc:VX},GX="return float(a >= 1.0 && b >= 1.0);",HX=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,jX=oa({opSnippet:GX,packedOpSnippet:HX,dtype:"bool"}),qX={kernelName:Ei,backendName:"webgl",kernelFunc:jX},XX="return float(!(x >= 1.0));",KX=Qe({opSnippet:XX}),ZX={kernelName:Ri,backendName:"webgl",kernelFunc:KX},YX="return float(a >= 1.0 || b >= 1.0);",JX=` +`,jX=ua({opSnippet:GX,packedOpSnippet:HX,dtype:"bool"}),qX={kernelName:Ei,backendName:"webgl",kernelFunc:jX},XX="return float(!(x >= 1.0));",KX=Qe({opSnippet:XX}),ZX={kernelName:Ri,backendName:"webgl",kernelFunc:KX},YX="return float(a >= 1.0 || b >= 1.0);",JX=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,QX=oa({opSnippet:YX,packedOpSnippet:JX,dtype:"bool"}),eK={kernelName:Bl,backendName:"webgl",kernelFunc:QX},tK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,QX=ua({opSnippet:YX,packedOpSnippet:JX,dtype:"bool"}),eK={kernelName:Mi,backendName:"webgl",kernelFunc:QX},tK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3618,7 +3618,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},nK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new aK(r.shape,s,i,o,l):new tK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},rK={kernelName:ah,backendName:"webgl",kernelFunc:nK},sK=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` + `}},nK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=B().getBool("WEBGL_PACK_NORMALIZATION")?new aK(r.shape,s,i,o,l):new tK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},rK={kernelName:Dd,backendName:"webgl",kernelFunc:nK},sK=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3656,7 +3656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${n}) * float(${r}) - * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); @@ -3673,16 +3673,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(result); } - `}},iK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new sK(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},oK={kernelName:q1,backendName:"webgl",kernelFunc:iK};function lK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=bo(i,e.dtype,"max",n),l=ce({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function cv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let A=a.texData.get(h.dataId).values,y=new Array(o);for(let S=0;S{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new sK(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},oK={kernelName:V2,backendName:"webgl",kernelFunc:iK};function lK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function dv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let y=a.texData.get(h.dataId).values,A=new Array(o);for(let S=0;S`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Za({inputs:{x:r},backend:a});let c=new xd(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var mK={kernelName:_i,backendName:"webgl",kernelFunc:fK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new $3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var xK={kernelName:nh,backendName:"webgl",kernelFunc:gK},AK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` +`,cK=ua({opSnippet:dK,packedOpSnippet:pK,cpuKernelImpl:vU}),hK={kernelName:_i,backendName:"webgl",kernelFunc:cK};function fK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Za({inputs:{x:r},backend:a});let c=new Ad(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var mK={kernelName:Pi,backendName:"webgl",kernelFunc:fK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new _3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var xK={kernelName:nh,backendName:"webgl",kernelFunc:gK},yK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3728,7 +3728,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}},yK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},AK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${c}, ${d}); void main() { @@ -3792,16 +3792,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam } setOutput(dotProd); } - `}};function bK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new $3(d,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new yK(d),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var vK={kernelName:K1,backendName:"webgl",kernelFunc:bK};function wK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;uu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=T.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,f=new xd(d,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new AK(d),x=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),x}var kK={kernelName:X1,backendName:"webgl",kernelFunc:wK};function IK(e,t,a,n){let r=new xd(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new xd(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var SK={kernelName:rh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(n.shape,r,s,u,i),[c,d]=IK(n,o,p,l);return[c,d]}};function TK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=bo(i,"float32","mean",n),l=ce({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var CK={kernelName:Pi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(d){let y=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;C{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(n.shape,r,s,u,i),[c,d]=IK(n,o,p,l);return[c,d]}};function TK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var CK={kernelName:Fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(d){let A=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let n=e.length,r=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=` +`,$K=ua({opSnippet:RK,packedOpSnippet:MK,cpuKernelImpl:kU}),_K={kernelName:Di,backendName:"webgl",kernelFunc:$K},PK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let n=e.length,r=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3886,13 +3886,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wH=Qe({opSnippet:vH}),kH={kernelNam ${d} setOutput(result); } - `}},OK=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FK(n.shape,r,s):new PK(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},DK={kernelName:Di,backendName:"webgl",kernelFunc:OK},zK=`if (b == 0.0) return NAN; + `}},OK=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FK(n.shape,r,s):new PK(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},DK={kernelName:zi,backendName:"webgl",kernelFunc:OK},zK=`if (b == 0.0) return NAN; return mod(a, b);`,LK=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+pp+` + `+fp+` return result; -`,BK=oa({opSnippet:zK,packedOpSnippet:LK}),WK={kernelName:Wl,backendName:"webgl",kernelFunc:BK},VK=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` +`,BK=ua({opSnippet:zK,packedOpSnippet:LK}),WK={kernelName:Ll,backendName:"webgl",kernelFunc:BK},VK=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3934,7 +3934,7 @@ return a / b;`,GK=` } return result; -`,hv=oa({opSnippet:UK,packedOpSnippet:GK,checkOutOfBounds:!0}),HK={kernelName:ci,backendName:"webgl",kernelFunc:hv},$x="return a - b;",fv=oa({opSnippet:$x,packedOpSnippet:$x,supportsComplex:!0,cpuKernelImpl:VU}),jK={kernelName:lo,backendName:"webgl",kernelFunc:fv};function mv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=cv({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:a,attrs:{shape:l}}),p=fv({inputs:{a:r,b:u},backend:a}),c=uv({inputs:{x:p},backend:a}),d=zh({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:d},backend:a,attrs:{shape:l}}),f=hv({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var qK={kernelName:so,backendName:"webgl",kernelFunc:mv};function XK(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:mv({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new VK(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var KK={kernelName:sh,backendName:"webgl",kernelFunc:XK},ZK=Cn+` +`,pv=ua({opSnippet:UK,packedOpSnippet:GK,checkOutOfBounds:!0}),HK={kernelName:ci,backendName:"webgl",kernelFunc:pv},Nx="return a - b;",cv=ua({opSnippet:Nx,packedOpSnippet:Nx,supportsComplex:!0,cpuKernelImpl:VU}),jK={kernelName:po,backendName:"webgl",kernelFunc:cv};function hv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=dv({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=cv({inputs:{a:r,b:u},backend:a}),c=ov({inputs:{x:p},backend:a}),d=_h({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),f=pv({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var qK={kernelName:oo,backendName:"webgl",kernelFunc:hv};function XK(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:hv({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new VK(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var KK={kernelName:sh,backendName:"webgl",kernelFunc:XK},ZK=Cn+` return -x; `,YK=` vec4 result = -x; @@ -3946,14 +3946,14 @@ return a / b;`,GK=` result.a = isNaN.a ? x.a : result.a; return result; -`;function JK(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=IU(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vr(n.shape,YK):r=new qn(n.shape,ZK),a.runWebGLProgram(r,[n],n.dtype)}var QK={kernelName:Vl,backendName:"webgl",kernelFunc:JK},eZ=Tn.nonMaxSuppressionV3Impl;function tZ(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. 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n=e.length,r=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4002,7 +4002,7 @@ return a / b;`,GK=` ${h} setOutput(result); } - `}},xv=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return fp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xZ(r.shape,s,i):new gZ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},AZ={kernelName:Ui,backendName:"webgl",kernelFunc:xv},yZ=` + `}},mv=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return xp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xZ(r.shape,s,i):new 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l.forEach(f=>a.disposeIntermediateTensorInfo(f)),h}var IZ={kernelName:qi,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(x=>a.readSync(x.dataId)),u=r.map(x=>x.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,f]=CU(l,u,p,s.shape,s.dtype,c,i.shape,o),m=d.map(x=>a.makeTensorInfo([x.length],"int32",x)),g=a.makeTensorInfo(f,s.dtype,h);return m.concat([g])}var TZ={kernelName:ih,backendName:"webgl",kernelFunc:SZ};function CZ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=NU(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var NZ={kernelName:oh,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[f,m]=EU(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(f,s.dtype,m)}var RZ={kernelName:lh,backendName:"webgl",kernelFunc:EZ},gv=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=RU(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},MZ={kernelName:Gl,backendName:"webgl",kernelFunc:gv},$Z="return 1.0 / x;",_Z=Qe({opSnippet:$Z}),PZ={kernelName:Xi,backendName:"webgl",kernelFunc:_Z},FZ=Cn+` return (x < 0.0) ? 0.0 : x; `,OZ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4041,7 +4041,7 @@ return a / b;`,GK=` result.a = isNaN.a ? x.a : result.a; return result; -`,DZ=Qe({opSnippet:FZ,packedOpSnippet:OZ}),zZ={kernelName:Xi,backendName:"webgl",kernelFunc:DZ},LZ=Cn+` +`,DZ=Qe({opSnippet:FZ,packedOpSnippet:OZ}),zZ={kernelName:Ki,backendName:"webgl",kernelFunc:DZ},LZ=Cn+` return (x < 0.0) ? 0.0 : min(6.0, x); `,BZ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4053,7 +4053,7 @@ return a / b;`,GK=` result.a = isNaN.a ? x.a : result.a; return result; -`,WZ=Qe({opSnippet:LZ,packedOpSnippet:BZ}),VZ={kernelName:Yi,backendName:"webgl",kernelFunc:WZ},UZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,WZ=Qe({opSnippet:LZ,packedOpSnippet:BZ}),VZ={kernelName:Ji,backendName:"webgl",kernelFunc:WZ},UZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4163,7 +4163,7 @@ return a / b;`,GK=` setOutput(newValue); } - `}};function HZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GZ(r.shape,l,u,s,i):new UZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var jZ={kernelName:Zi,backendName:"webgl",kernelFunc:HZ},qZ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function HZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GZ(r.shape,l,u,s,i):new UZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var jZ={kernelName:Yi,backendName:"webgl",kernelFunc:HZ},qZ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4244,7 +4244,7 @@ return a / b;`,GK=` setOutput(accumulator); } - `}};function XZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new qZ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var KZ={kernelName:Y1,backendName:"webgl",kernelFunc:XZ},ZZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function XZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new qZ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var KZ={kernelName:j2,backendName:"webgl",kernelFunc:XZ},ZZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4307,7 +4307,7 @@ return a / b;`,GK=` setOutput(newValue); } - `}};function JZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new YZ(r.shape,l,u,s,i):new ZZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var QZ={kernelName:Ki,backendName:"webgl",kernelFunc:JZ},eY=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` + `}};function JZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new YZ(r.shape,l,u,s,i):new ZZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var QZ={kernelName:Zi,backendName:"webgl",kernelFunc:JZ},eY=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4377,7 +4377,7 @@ return a / b;`,GK=` setOutput(accumulator); } - `}};function tY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new eY(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var aY={kernelName:Z1,backendName:"webgl",kernelFunc:tY},nY=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` + `}};function tY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new eY(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var aY={kernelName:H2,backendName:"webgl",kernelFunc:tY},nY=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4415,7 +4415,7 @@ return a / b;`,GK=` } setOutput(result); } - `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let f=e.map((x,A)=>d(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function sY(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return Za({inputs:{x:r},backend:a});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rY(r.shape,o):new nY(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var iY={kernelName:Ji,backendName:"webgl",kernelFunc:sY},oY=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let f=e.map((x,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function sY(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return Za({inputs:{x:r},backend:a});let l=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rY(r.shape,o):new nY(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var iY={kernelName:Qi,backendName:"webgl",kernelFunc:sY},oY=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4434,7 +4434,7 @@ return a / b;`,GK=` } setOutput(outputValue); } - `}},lY={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new oY(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},uY=` + `}},lY={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new oY(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},uY=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4449,7 +4449,7 @@ return a / b;`,GK=` return base + 1.0; } } -`,dY=Qe({opSnippet:uY}),pY={kernelName:Xl,backendName:"webgl",kernelFunc:dY},cY="return inversesqrt(x);",hY=Qe({opSnippet:cY,cpuKernelImpl:MU}),fY={kernelName:Qi,backendName:"webgl",kernelFunc:hY},yv=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let p=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let d=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=` +`,dY=Qe({opSnippet:uY}),pY={kernelName:eo,backendName:"webgl",kernelFunc:dY},cY="return inversesqrt(x);",hY=Qe({opSnippet:cY,cpuKernelImpl:MU}),fY={kernelName:to,backendName:"webgl",kernelFunc:hY},xv=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let p=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let d=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${r}); void main() { @@ -4469,7 +4469,7 @@ return a / b;`,GK=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function mY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=ce({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new yv(l,o,h.shape.length,f.shape.length,p,d),x=a.runWebGLProgram(g,[f,h,m],f.dtype),A=ce({inputs:{x},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(m),A}var gY={kernelName:eo,backendName:"webgl",kernelFunc:mY},xY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=W().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` + `}};function mY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new xv(l,o,h.shape.length,f.shape.length,p,d),x=a.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(m),y}var gY={kernelName:ao,backendName:"webgl",kernelFunc:mY},xY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=B().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4494,7 +4494,7 @@ return a / b;`,GK=` setOutput(float(findBound(batch, value))); } - `}};function AY(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new xY(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var yY={kernelName:$d,backendName:"webgl",kernelFunc:AY},bY=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,IY=Qe({opSnippet:kY}),SY={kernelName:_d,backendName:"webgl",kernelFunc:IY},TY=mu+` +`,IY=Qe({opSnippet:wY}),SY={kernelName:ql,backendName:"webgl",kernelFunc:IY},TY=mu+` return 1.0 / (1.0 + exp(-1.0 * x)); `,CY=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); @@ -4522,15 +4522,15 @@ return a / b;`,GK=` result.a = isNaN.a ? x.a : result.a; return result; -`,NY=Qe({opSnippet:TY,packedOpSnippet:CY,cpuKernelImpl:_U}),EY={kernelName:ao,backendName:"webgl",kernelFunc:NY},RY=` +`,NY=Qe({opSnippet:TY,packedOpSnippet:CY,cpuKernelImpl:_U}),EY={kernelName:ro,backendName:"webgl",kernelFunc:NY},RY=` if (isnan(x)) { return 0.0; } return sign(x); -`,MY=Qe({opSnippet:RY}),$Y={kernelName:Pd,backendName:"webgl",kernelFunc:MY},_Y=mu+` +`,MY=Qe({opSnippet:RY}),$Y={kernelName:Zl,backendName:"webgl",kernelFunc:MY},_Y=mu+` return sin(x); -`,PY=Qe({opSnippet:_Y}),FY={kernelName:to,backendName:"webgl",kernelFunc:PY},OY=` +`,PY=Qe({opSnippet:_Y}),FY={kernelName:no,backendName:"webgl",kernelFunc:PY},OY=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,DY=Qe({opSnippet:OY}),zY={kernelName:Yl,backendName:"webgl",kernelFunc:DY},LY=` +`,DY=Qe({opSnippet:OY}),zY={kernelName:Kl,backendName:"webgl",kernelFunc:DY},LY=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4550,17 +4550,17 @@ return a / b;`,GK=` result = log(exp_x + 1.0); } return result; -`,BY=Qe({opSnippet:LY}),WY={kernelName:Fd,backendName:"webgl",kernelFunc:BY},VY=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xa.disposeIntermediateTensorInfo(x)),g},UY={kernelName:Jl,backendName:"webgl",kernelFunc:VY};function GY(e){let{inputs:t,backend:a}=e,{indices:n,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: 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KY={kernelName:Dd,backendName:"webgl",kernelFunc:XY};function ZY(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=L6(i,n.shape,n.dtype,o,l,!0);return a.makeTensorInfo(p,n.dtype,u)}var KY={kernelName:Wd,backendName:"webgl",kernelFunc:XY};function ZY(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let 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JY(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let x=a.bufferSync(r),y=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=$U(x,y,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let f=new xv(u,l,r.shape.length,s.shape.length,c,[d,1],h),m=a.runWebGLProgram(f,[s,r,i],s.dtype),g=pe({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(m),g}var QY={kernelName:Ud,backendName:"webgl",kernelFunc:JY};function eJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=gu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var tJ={kernelName:Ql,backendName:"webgl",kernelFunc:eJ},Ex="return sqrt(x);",aJ=Qe({opSnippet:Ex,packedOpSnippet:Ex,cpuKernelImpl:DU}),nJ={kernelName:so,backendName:"webgl",kernelFunc:aJ},rJ="return x * x;",sJ=Qe({opSnippet:rJ}),iJ={kernelName:Gd,backendName:"webgl",kernelFunc:sJ},Rx="return (a - b) * (a - b);",oJ=ua({opSnippet:Rx,packedOpSnippet:Rx}),lJ={kernelName:lo,backendName:"webgl",kernelFunc:oJ};function uJ({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=Cn+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new qn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var dJ={kernelName:fo,backendName:"webgl",kernelFunc:uJ},pJ=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=gt(a.length),s=gt(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new qn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var dJ={kernelName:ss,backendName:"webgl",kernelFunc:uJ},pJ=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=gt(a.length),s=gt(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4568,15 +4568,15 @@ return a / b;`,GK=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function cJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:x,begin:A,end:y,strides:b}=It.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(m)w=ce({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||x){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: 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tan(x);",vJ=Qe({opSnippet:bJ}),wJ={kernelName:uo,backendName:"webgl",kernelFunc:vJ},kJ=` + `}};function cJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:x,begin:y,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=pe({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||x){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=St.computeOutShape(y,A,b),E=gu({inputs:{x:r},backend:a,attrs:{begin:y,size:C}});k=pe({inputs:{x:E},backend:a,attrs:{shape:f}}),a.disposeIntermediateTensorInfo(E)}else if(a.shouldExecuteOnCPU([r])){let C=a.readSync(r.dataId),E=_e(r.shape,r.dtype,C),_=zU(h,E,b,y);k=a.makeTensorInfo(f,r.dtype,_.values)}else{let C=new pJ(y,b,h);k=a.runWebGLProgram(C,[r],r.dtype)}let S=pe({inputs:{x:k},backend:a,attrs:{shape:f}});return a.disposeIntermediateTensorInfo(k),S}var hJ={kernelName:uo,backendName:"webgl",kernelFunc:cJ};function fJ(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=LU(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var mJ={kernelName:tu,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=BU(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new 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NJ={kernelName:rs,backendName:"webgl",kernelFunc:yv},EJ=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4650,7 +4650,7 @@ return a / b;`,GK=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Es(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Fx(e){let t=1;for(;tl){let _=a.readSync(r.dataId),[$,M]=GU(_,u,r.dtype,s,i);return[a.makeTensorInfo($.shape,$.dtype,$.values),a.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,fp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let 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JQ(e){Fv=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function QQ(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,x=h.padInfo.right,y=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,C=h.strideWidth,E=h.inChannels,_=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(h.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return Fv(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,x,y,A,$,b,k,S,C,E,_,I),M}var eee={kernelName:pi,backendName:"wasm",setupFunc:JQ,kernelFunc:QQ},tee=Bt(hi),aee=!1,nee=da(fi,aee,"bool"),ree=Bt(mi,"float32");function T2(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),za({inputs:{x:r},backend:n,attrs:{shape:o}})}var see={kernelName:$l,backendName:"wasm",kernelFunc:T2};function Ov(e){let{attrs:{shape:t,value:a,dtype:n},backend:r}=e,s=r.makeOutput(t,n);return r.typedArrayFromHeap(s).fill(a),s}var iee={kernelName:Pl,backendName:"wasm",kernelFunc:Ov},Dv;function oee(e){Dv=e.wasm.cwrap(gi,null,["number","number","number","number","number","number"])}function lee(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return Dv(s,o,l,u,p,i),r}var uee={kernelName:gi,backendName:"wasm",kernelFunc:lee,setupFunc:oee},dee=Bt(xi),pee=!1,cee=da(yi,pee),zv;function hee(e){zv=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number","number"])}function fee(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return zv(p,c,d,h,f,r,g),m}var mee={kernelName:Ai,backendName:"wasm",setupFunc:hee,kernelFunc:fee},Lv;function gee(e){Lv=e.wasm.cwrap(qr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xee(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=a,m=T.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=vd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(r.dataId).id,y=n.dataIdMap.get(s.dataId).id,A=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${A})`);b=Z.id}let k=m.filterHeight,S=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,_=m.padInfo.bottom,$=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,N=m.strideHeight,O=m.strideWidth,L=m.inChannels,W=m.padInfo.type==="SAME"?1:0,G=m.batchSize,H=m.inHeight,U=m.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. 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ote={kernelName:Oi,backendName:"wasm",setupFunc:ste,kernelFunc:ite},lte=!1,ute=da(Di,lte),C2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(C2||(C2={}));var Xv;function dte(e){Xv=e.wasm.cwrap(zi,null,["number","array","number","number","array","array","number","number"])}function pte(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(f=>f[0]),c=n.map(f=>f[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Xv(i,u,t.shape.length,Ct[t.dtype],d,h,C2[r],l),o}var cte={kernelName:zi,backendName:"wasm",kernelFunc:pte,setupFunc:dte},hte=!0,fte=da(Li,hte),mte=Bt(Bl);function P3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return 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Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var $x=yl(eS()),vne=yl(tS()),_x=yl(aS()),Px=$x.default||$x,kne=_x.default||_x,k8=class extends Al{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(w8),E2=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new wd(this,vt())}write(e,t,a){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,a,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,a,n,r){let s=this.dataIdNextNumber++;if(n==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:a,dtype:n,memoryOffset:null,refCount:r});return}let i=v.sizeFromShape(a),o=i*v.bytesPerElement(n),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:a,dtype:n,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e,t,a){let{memoryOffset:n,dtype:r,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(a==null||a>=i.length)?i:i.slice(t,a);t=t||0,a=a||v.sizeFromShape(s);let o=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(n+t*o,n+a*o);return Sne(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let a=this.dataIdMap.get(e);if(a.refCount--,!t&&a.refCount>0)return!1;this.wasm._free(a.memoryOffset),this.wasm.tfjs.disposeData(a.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,a){let n;if(a==null)n=this.write(null,e,t);else{let r=this.dataIdNextNumber++;n={id:r},this.dataIdMap.set(n,{id:r,memoryOffset:a,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,a)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:a}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(a),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,r,s);case"int32":return new Int32Array(n,r,s);case"bool":return new Uint8Array(n,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function wne(e){return(t,a)=>(v.fetch(e,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${e}'`),n.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{a(s.instance,s.module)})})}),{})}function Fx(e,t,a){if(Uc!=null)return Uc;let n="tfjs-backend-wasm.wasm";return e&&t?n="tfjs-backend-wasm-threaded-simd.wasm":e&&(n="tfjs-backend-wasm-simd.wasm"),ed!=null&&ed[n]!=null?ed[n]:a+n}async function Ine(){let[e,t]=await Promise.all([B().getAsync("WASM_HAS_SIMD_SUPPORT"),B().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((a,n)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=vne.wasmWorkerContents.replace(/\n/g,"\\n"),p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return o.endsWith(".wasm")?Fx(e,t,Yu!=null?Yu:l):l+o},F3&&(r.instantiateWasm=wne(Fx(e,t,Yu!=null?Yu:"")));let s=!1;r.onAbort=()=>{s||td||(td=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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zn=B();zn.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zn.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zn.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);zn.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zn.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zn.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zn.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zn.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);zn.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);zn.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>0);zn.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);var $ne=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}},_ne=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,a=!1){let n=Ox(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let r=this.device.createBuffer({size:e,usage:t,mappedAtCreation:a});return this.usedBuffers.get(n).push(r),r}releaseBuffer(e,t,a){if(this.freeBuffers.size===0)return;let n=Ox(t,a);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(n),s=r.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,a){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,a)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Ox(e,t){return`${e}_${t}`}var Pne=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,a,n){let r=zx(a),s=e*t*r,i=Dx(e,t,a,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:a,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,a,n,r){if(this.freeTextures.size===0)return;let s=Dx(t,a,n,r);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=zx(n),u=t*a*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Dx(e,t,a,n){return`${e}_${t}_${a}_${n}`}function zx(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}function Fne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}var O3=(e,t,a)=>a==="int32"?`atomicAdd(${e}, bitcast(${t}));`:` + { + var oldValue = 0; + loop { + let newValueF32 = bitcast(oldValue) + (${t}); + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(${e}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`,One=(e,t,a,n)=>{let r={dtype:n.dtype,shape:n.shape},s=zne(a,r,t),i=e.createShaderModule({code:s,label:t.constructor.name});return e.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})};function ia(e){if(e<=1)return"i32";if(e===2)return"vec2";if(e===3)return"vec3";if(e===4)return"vec4";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ar(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function ke(...e){let t;switch(e.length){case 0:t=` fn main() `;break;case 1:t=` fn main(${e[0]} : i32) - `;break;default:throw Error("Unreachable")}return t}function Ux(e){let t;return t=` - ${Dne()} + `;break;default:throw Error("Unreachable")}return t}function Lx(e,t){let a;return a=` + ${Dne(t)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @@ -4888,12 +4900,9 @@ return a / b;`,GK=` workgroupId = WorkgroupId; ${e?"main(getGlobalIndex());":"main();"}; } - `,t}function Dne(){return` - @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ) + `,a}function Dne(e){return` + @compute @workgroup_size(${e.workgroupSize[0]}, ${e.workgroupSize[1]}, ${e.workgroupSize[2]}) `}function zne(e,t,a){let n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(n.push(` - const workgroupSizeX = ${a.workgroupSize[0]}u; - const workgroupSizeY = ${a.workgroupSize[1]}u; - const workgroupSizeZ = ${a.workgroupSize[2]}u; var localId: vec3; var localIndex: u32; @@ -4903,8 +4912,8 @@ return a / b;`,GK=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${T8(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + - workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r} + + ${I8(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u + localIndex); `} } @@ -4917,9 +4926,9 @@ return a / b;`,GK=` @group(0) @binding(0) var result: array<${ad(t.dtype,a.isVec4)}>; @group(0) @binding(2) var uniforms: Uniform; - `);let h=jx(a);return[Gx,n.join(` -`),Hx(t.shape),a.getUserCode(),Ux(h)].join(` -`)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,f)=>{let m=ra(e[f].shape.length);s+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${m}, `});let i=ra(t.shape.length);s+=`outShape : ${i}, `;let o=t.shape.length-1,l=ra(o);s+=` + `);let h=Vx(a);return[Bx,n.join(` +`),Wx(t.shape),a.getUserCode(),Lx(h,a)].join(` +`)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,f)=>{let m=ia(e[f].shape.length);s+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${m}, `});let i=ia(t.shape.length);s+=`outShape : ${i}, `;let o=t.shape.length-1,l=ia(o);s+=` outShapeStrides: ${l}, `,a.size&&(s+="size : i32, "),a.uniforms&&(s+=a.uniforms),s+="};",s=qne(s),n.push(s),a.atomic?n.push(` @group(0) @binding(0) var result: array>; `):n.push(` @@ -4928,10 +4937,10 @@ return a / b;`,GK=` @group(0) @binding(${1+f}) var ${h}: array<${a.variableTypes?a.variableTypes[f]:ad(e[f].dtype,a.isVec4)}>; `)}),s!==""&&n.push(` @group(0) @binding(${1+a.variableNames.length}) var uniforms: Uniforms; - `);let u=Gne(t.shape,a.dispatchLayout),p=[Gx+Bne,n.join(` -`),Hx(t.shape),u,Hne(t.shape.length)];a.atomic||p.push(jne(t.shape,t.dtype,a.isVec4));let c=e.map((h,f)=>Une(h,t.shape,a.variableTypes?a.variableTypes[f]==="vec4":a.isVec4,a.dispatchLayout.x.length===t.shape.length)).join(` -`);p.push(c),p.push(a.getUserCode());let d=jx(a);return p.push(Ux(d)),p.join(` -`)}function Lne(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>T.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=T8(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Gx=` + `);let u=Gne(t.shape,a.dispatchLayout),p=[Bx,n.join(` +`)+Bne,Wx(t.shape),u,Hne(t.shape.length)];a.atomic||p.push(jne(t.shape,t.dtype,a.isVec4));let c=e.map((h,f)=>Une(h,t.shape,a.variableTypes?a.variableTypes[f]==="vec4":a.isVec4,a.dispatchLayout.x.length===t.shape.length)).join(` +`);p.push(c),p.push(a.getUserCode());let d=Vx(a);return p.push(Lx(d,a)),p.join(` +`)}function Lne(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>T.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=I8(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Bx=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -4987,21 +4996,22 @@ return a / b;`,GK=` return (floatToUint & 0x7fffffffu) > 0x7f800000u; } fn isnanVec4(val : vec4) -> vec4 { - return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3])); + let floatToUint: vec4 = bitcast>(val); + return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } `,Bne=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function Hx(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let a=v.computeStrides(e),n=ra(t),r=[];for(let i=0;i vec2 { +`;function Wx(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let a=v.computeStrides(e),n=ia(t),r=[];for(let i=0;i vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); - }`;let s;return s="var index2 = index;"+a.map((i,o)=>{let l=`let ${r[o]} = index2 / uniforms.outShapeStrides.${yr(o)}`,u=o===a.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${yr(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${yr(o)}`;return`${l}; ${u};`}).join(""),` + }`;let s;return s="var index2 = index;"+a.map((i,o)=>{let l=`let ${r[o]} = index2 / uniforms.outShapeStrides.${Ar(o)}`,u=o===a.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${Ar(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${Ar(o)}`;return`${l}; ${u};`}).join(""),` fn getCoordsFromIndex(index : i32) -> ${n} { ${s} return ${n}(${r.join(",")}); } - `}function Wne(e,t){let a=e.name,n=e.shape.length,r=ra(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return t?` + `}function Wne(e,t){let a=e.name,n=e.shape.length,r=ia(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return t?` fn ${s}() -> vec4 { return vec4(${a}[0]); } @@ -5019,7 +5029,7 @@ return a / b;`,GK=` return f32(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}), ${l})]); } - `}function Vne(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=ra(l);if(v.arraysEqual(e.shape,t)&&n)return a?` + `}function Vne(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=ia(l);if(v.arraysEqual(e.shape,t)&&n)return a?` fn ${i}Index(globalIndex : i32) -> vec4 { return vec4(${r}[globalIndex]); } @@ -5051,8 +5061,8 @@ return a / b;`,GK=` fn ${i}Coords(coords : ${u}) -> f32{ return get${s}(); } - `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${yr(g+c)} = 0;`).join(` -`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=ra(o),x=e.shape.map((A,y)=>`coords.${yr(y+c)}`).join(", ");h=`${g}(${x})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return a?` + `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${Ar(g+c)} = 0;`).join(` +`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=ia(o),x=e.shape.map((y,A)=>`coords.${Ar(A+c)}`).join(", ");h=`${g}(${x})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return a?` fn ${i}Index(globalIndex : i32) -> vec4 { var coords = getCoordsFromIndex(globalIndex); ${d} @@ -5076,11 +5086,11 @@ return a / b;`,GK=` ${d} return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]); } -`}function Une(e,t,a,n){let r=Wne(e,a);return e.shape.length<=t.length&&(r+=Vne(e,t,a,n)),r}function Gne(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${ra(s)}{ +`}function Une(e,t,a,n){let r=Wne(e,a);return e.shape.length<=t.length&&(r+=Vne(e,t,a,n)),r}function Gne(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${ia(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let o="",l=[a,n,r];for(let d=0;d ${p} { + `;let o="",l=[a,n,r];for(let d=0;d ${p} { ${o} `;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function Hne(e){let t="";switch(e){case 0:case 1:t+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { @@ -5116,7 +5126,7 @@ return a / b;`,GK=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function T8(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function ad(e,t){return e==="float32"?t?"vec4":"f32":e==="int32"||e==="bool"?t?"vec4":"i32":e}function jne(e,t,a){let n=e.length,r=ad(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { + `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function I8(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function ad(e,t){return e==="float32"?t?"vec4":"f32":e==="int32"||e==="bool"?t?"vec4":"i32":e}function jne(e,t,a){let n=e.length,r=ad(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { result[flatIndex] = ${r}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : vec4) { @@ -5126,7 +5136,7 @@ return a / b;`,GK=` } fn setOutputAtIndexI32(flatIndex : i32, value : i32) { result[flatIndex] = ${r}(value); - }`,n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=ra(n);a?s+=` + }`,n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=ia(n);a?s+=` fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndex(flatIndex / 4, value); @@ -5144,25 +5154,13 @@ return a / b;`,GK=` let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndexI32(flatIndex, value); } - `}return s}function qne(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function jx(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var C8={};Xe(C8,{ArrayBufferToTypedArray:()=>R8,GPUBytesPerElement:()=>E8,MatMulProgramType:()=>Pn,computeDispatch:()=>Ne,computeWorkPerThreadForConv2d:()=>O3,computeWorkgroupInfoForMatMul:()=>N8,computeWorkgroupSizeForConv2d:()=>F3,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>D3,tilesFitEvenlyIntoShape:()=>Xne});var Ds=e=>{let t=1;for(let a=0;aa%e[n]===0)}function Ne(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Ds(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Ds(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Ds(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function N8(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function F3(e,t,a=!1){if(a)return[8,8,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function O3(e,t,a=!1){if(a)return[4,4,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,a)=>a)}}function E8(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function R8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function D3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Kne=W().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Zne=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Vh=class extends bl{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!D3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new $ne(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new _ne(this.device),this.textureManager=new Pne(this.device),this.tensorMap=new vd(this,kt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Vh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=R8(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:a,dtype:n,shape:r,resourceInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=s.size,o=this.bufferManager.acquireBuffer(i,s.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(r,n),u=kt().makeTensorFromTensorInfo(l),p=this.tensorMap.get(l.dataId);return p.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:o},{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return Me(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Me(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=E8(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(a===5||a===6)&&(l=16),t=Math.ceil(t/l)*l,a=o.data.length,n.push(t),t+=o.data.length*4});let r=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(r,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(r,u,o.data.length).set(o.data):new Float32Array(r,u,o.data.length).set(o.data)});let s=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(s,0,r,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:s};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:s}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=Zne(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=Lne(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=One(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),W().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Kne){return W().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape){W().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:W().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new Vh(r,s)},3);var De;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.INT_DIV=8]="INT_DIV",e[e.LESS=9]="LESS",e[e.LESS_EQUAL=10]="LESS_EQUAL",e[e.LOGICAL_AND=11]="LOGICAL_AND",e[e.MAX=12]="MAX",e[e.MIN=13]="MIN",e[e.MOD=14]="MOD",e[e.MUL=15]="MUL",e[e.NOT_EQUAL=16]="NOT_EQUAL",e[e.POW=17]="POW",e[e.PRELU=18]="PRELU",e[e.SQUARED_DIFFERENCE=19]="SQUARED_DIFFERENCE",e[e.SUB=20]="SUB"})(De||(De={}));var M8=` + `}return s}function qne(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function Vx(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var S8={};Ke(S8,{GPUBytesPerElement:()=>R2,MatMulProgramType:()=>Pn,assertNotComplex:()=>C8,computeDispatch:()=>we,computeWorkPerThreadForConv2d:()=>z3,computeWorkgroupInfoForMatMul:()=>T8,computeWorkgroupSizeForConv2d:()=>D3,flatDispatchLayout:()=>$e,isWebGPUSupported:()=>L3,tilesFitEvenlyIntoShape:()=>Xne});var Ds=e=>{let t=1;for(let a=0;aa%e[n]===0)}function we(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Ds(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Ds(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Ds(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function T8(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function D3(e,t,a=!1){if(a)return[8,8,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function z3(e,t,a=!1){if(a)return[4,4,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function $e(e){return{x:e.map((t,a)=>a)}}function R2(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function L3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function C8(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Kne=B().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Zne=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Dh=class extends Al{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!L3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new $ne(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new _ne(this.device),this.textureManager=new Pne(this.device),this.tensorMap=new wd(this,vt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Dh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=v.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,a){let n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=R2(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.sizev.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=R2(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(a===5||a===6)&&(u=16),u>r&&(r=u),t=Math.ceil(t/u)*u,a=l.data.length,n.push(t),t+=l.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((l,u)=>{let p=n[u];l.type==="int32"?new Int32Array(s,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(s,p,l.data.length).set(l.data):new Float32Array(s,p,l.data.length).set(l.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,s,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=Zne(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=Lne(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=One(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),B().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Kne){return B().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape){B().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:B().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new Dh(r,s)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.INT_DIV=8]="INT_DIV",e[e.LESS=9]="LESS",e[e.LESS_EQUAL=10]="LESS_EQUAL",e[e.LOGICAL_AND=11]="LOGICAL_AND",e[e.LOGICAL_OR=12]="LOGICAL_OR",e[e.MAX=13]="MAX",e[e.MIN=14]="MIN",e[e.MOD=15]="MOD",e[e.MUL=16]="MUL",e[e.NOT_EQUAL=17]="NOT_EQUAL",e[e.POW=18]="POW",e[e.PRELU=19]="PRELU",e[e.SQUARED_DIFFERENCE=20]="SQUARED_DIFFERENCE",e[e.SUB=21]="SUB"})(Pe||(Pe={}));var N8=` if (isnan(a)) { return a; } if (isnan(b)) { return b; } - `,$8=` - if (isNaN.r) { - resultTemp.r = valueForNaN; - } - if (isNaN.g) { - resultTemp.g = valueForNaN; - } - if (isNaN.b) { - resultTemp.b = valueForNaN; - } - if (isNaN.a) { - resultTemp.a = valueForNaN; - } - `,z3=` - let isNaN = isnanVec4(a) | isnanVec4(b); - ${$8} + `,zh=` + resultTemp = select( + resultTemp, vec4(valueForNaN), + vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); `,Yne="return a + b;",Jne="return areal * breal - aimag * bimag;",Qne="return areal * bimag + aimag * breal;",ere="return a / b;",tre="return f32(a == b);",are="return vec4(a == b);",nre="return f32(a > b);",rre="return vec4(a > b);",sre="return f32(a >= b);",ire="return vec4(a >= b);",ore=` let s = sign(a) * sign(b); let ia = i32(round(a)); @@ -5189,9 +5187,10 @@ return a / b;`,GK=` resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); -`,ure="return f32(a < b);",dre="return vec4(a < b);",pre="return f32(a <= b);",cre="return vec4(a <= b);",hre="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",fre=`return (vec4(a >= vec4(1.0)) * - vec4(b >= vec4(1.0)));`,mre=` - ${M8} +`,ure="return f32(a < b);",dre="return vec4(a < b);",pre="return f32(a <= b);",cre="return vec4(a <= b);",hre="return f32(a >= 1.0 && b >= 1.0);",fre=`return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`,mre="return f32(a >= 1.0 || b >= 1.0);",gre=`return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`,xre=` + ${N8} if (b == 0.) { return uniforms.NAN; } @@ -5201,23 +5200,11 @@ return a / b;`,GK=` } else { return (resultTemp + b) % b; } -`,gre=` +`,yre=` + let isNaN = !vec4(b); let valueForNaN = uniforms.NAN; var resultTemp = vec4(a % b); - ${z3} - - if (b[0] == 0.) { - resultTemp[0] = uniforms.NAN; - } - if (b[1] == 0.) { - resultTemp[1] = uniforms.NAN; - } - if (b[2] == 0.) { - resultTemp[2] = uniforms.NAN; - } - if (b[3] == 0.) { - resultTemp[3] = uniforms.NAN; - } + ${zh} if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { resultTemp[0] = (resultTemp[0] + b[0]) % b[0]; @@ -5233,18 +5220,18 @@ return a / b;`,GK=` } return resultTemp; -`,xre="return a * b;",Are=` +`,Are="return a * b;",bre=` if (isnan(a) || isnan(b)) { return 1.0; } return f32(a != b); -`,yre=` +`,vre=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; - ${z3} + ${zh} return resultTemp; -`,bre=` +`,kre=` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } @@ -5255,7 +5242,7 @@ return a / b;`,GK=` return pow(abs(a), b); } return sign(a) * pow(abs(a), b); -`,vre=` +`,wre=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -5277,39 +5264,39 @@ return a / b;`,GK=` } let isNaN = (a < vec4(0.0)) & (floor(b) < b); let valueForNaN = uniforms.NAN; - ${$8} + ${zh} return resultTemp; -`,wre="if (a < 0.0) { return b * a; } return a;",kre=` +`,Ire="if (a < 0.0) { return b * a; } return a;",Sre=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Ire="return (a - b) * (a - b);",Sre="return a - b;";function Bm(e,t,a="uniforms.NAN"){let n=t?z3:M8;return t?` +`,Tre="return (a - b) * (a - b);",Cre="return a - b;";function Dm(e,t,a="uniforms.NAN"){let n=t?zh:N8;return t?` let valueForNaN = ${a}; var resultTemp = vec4(${e}(a, b)); `+n+` return resultTemp; `:n+` return ${e}(a, b); - `}function L3(e,t){switch(e){case De.ADD:return Yne;case De.ATAN2:return Bm("atan2",t);case De.COMPLEX_MULTIPLY_IMAG:return Qne;case De.COMPLEX_MULTIPLY_REAL:return Jne;case De.DIV:return ere;case De.EQUAL:return t?are:tre;case De.GREATER:return t?rre:nre;case De.GREATER_EQUAL:return t?ire:sre;case De.INT_DIV:return t?lre:ore;case De.LESS:return t?dre:ure;case De.LESS_EQUAL:return t?cre:pre;case De.LOGICAL_AND:return t?fre:hre;case De.MAX:return Bm("max",t);case De.MIN:return Bm("min",t);case De.MOD:return t?gre:mre;case De.MUL:return xre;case De.NOT_EQUAL:return t?yre:Are;case De.POW:return t?vre:bre;case De.PRELU:return t?kre:wre;case De.SQUARED_DIFFERENCE:return Ire;case De.SUB:return Sre;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var de;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.RSQRT=27]="RSQRT",e[e.SIN=28]="SIN",e[e.SINH=29]="SINH",e[e.SIGMOID=30]="SIGMOID",e[e.SQRT=31]="SQRT",e[e.SQUARE=32]="SQUARE",e[e.TAN=33]="TAN",e[e.TANH=34]="TANH",e[e.TO_INT=35]="TO_INT"})(de||(de={}));var Tre="return abs(a);",Cre=` + `}function B3(e,t){switch(e){case Pe.ADD:return Yne;case Pe.ATAN2:return Dm("atan2",t);case Pe.COMPLEX_MULTIPLY_IMAG:return Qne;case Pe.COMPLEX_MULTIPLY_REAL:return Jne;case Pe.DIV:return ere;case Pe.EQUAL:return t?are:tre;case Pe.GREATER:return t?rre:nre;case Pe.GREATER_EQUAL:return t?ire:sre;case Pe.INT_DIV:return t?lre:ore;case Pe.LESS:return t?dre:ure;case Pe.LESS_EQUAL:return t?cre:pre;case Pe.LOGICAL_AND:return t?fre:hre;case Pe.LOGICAL_OR:return t?gre:mre;case Pe.MAX:return Dm("max",t);case Pe.MIN:return Dm("min",t);case Pe.MOD:return t?yre:xre;case Pe.MUL:return Are;case Pe.NOT_EQUAL:return t?vre:bre;case Pe.POW:return t?wre:kre;case Pe.PRELU:return t?Sre:Ire;case Pe.SQUARED_DIFFERENCE:return Tre;case Pe.SUB:return Cre;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var Nre="return abs(a);",Ere=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); -`,Nre=` +`,Rre=` if (a < 1.) { return uniforms.NAN; } return acosh(a); -`,Ere=` +`,Mre=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); -`,Rre="return asinh(a);",Mre=` +`,$re="return asinh(a);",_re=` if (isnan(a)) { return uniforms.NAN; } return atan(a); -`,$re=` +`,Pre=` if (abs(a) > 1.) { return uniforms.NAN; } @@ -5320,10 +5307,10 @@ return a / b;`,GK=` return -uniforms.INFINITY; } return atanh(a); -`,_re="return ceil(a);",Pre="return cos(a);",Fre=` +`,Fre="return ceil(a);",Ore="return cos(a);",Dre=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; -`,Ore="return exp(a) - 1.0;",Dre="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",zre=` +`,zre="return exp(a) - 1.0;",Lre="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Bre=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -5338,7 +5325,7 @@ return a / b;`,GK=` resFloat.a = a.a; } return resFloat; -`,Lre=` +`,Wre=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -5353,195 +5340,217 @@ return a / b;`,GK=` let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); -`,Bre="return exp(a);",Wre="return floor(a);",Vre="return f32(!isnan(a) && !isinf(a));",Ure="return f32(isinf(a));",Gre="return f32(isnan(a));",Hre="return a;",jre=`if (a < 0.0) { return uniforms.NAN; } - return log(a);`,qre=` +`,Vre="return exp(a);",Ure="return floor(a);",Gre="return f32(!isnan(a) && !isinf(a));",Hre="return f32(isinf(a));",jre="return f32(isnan(a));",qre="return a;",Xre=`if (a < 0.0) { return uniforms.NAN; } + return log(a);`,Kre=` if (isnan(a)) { return a; } return log(1.0 + a); -`,Xre="return f32(!(a >= 1.0));",Kre="return -a;",Zre="if (a < 0.0) { return uniforms.alpha * a; } return a;",Yre=` +`,Zre="return f32(!(a >= 1.0));",Yre="return -a;",Jre="if (a < 0.0) { return uniforms.alpha * a; } return a;",Qre=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Jre="return 1.0 / a;",Qre="return select(a, 0.0, a < 0.0);",ese="return clamp(a, 0.0, 6.0);",tse="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",ase=` +`,ese="return 1.0 / a;",tse="return select(a, 0.0, a < 0.0);",ase="return clamp(a, 0.0, 6.0);",nse="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",rse=` return select(a, vec4(0.0), a < vec4(0.0)); -`,nse="return inverseSqrt(a);",rse="return 1.0 / (1.0 + exp(-1.0 * a));",sse="return sin(a);",ise=` +`,sse="return round(a);",ise="return inverseSqrt(a);",ose=` + if (a >= 0.0) { + return ${T.SELU_SCALE} * a; + } else { + return ${T.SELU_SCALEALPHA} * (exp(a) - 1.0); + } +`,lse="return 1.0 / (1.0 + exp(-1.0 * a));",use="return sign(a);",dse="return sin(a);",pse=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; -`,ose="return sqrt(a);",lse="return a * a;",use="return tan(a);",dse=` +`,cse=` + let epsilon = 1.1920928955078125e-7; + let threshold = log(epsilon) + 2.0; + + let too_large = a > -threshold; + let too_small = a < threshold; + let exp_a = exp(a); + + if (too_large) { + return a; + } else if (too_small) { + return exp_a; + } else { + return log(exp_a + 1.0); + } +`,hse="return sqrt(a);",fse="return a * a;",mse=` + if (isnan(a)) { + return a; + } + + return select(uniforms.stepAlpha, 1.0, a > 0.0); +`,gse="return tan(a);",xse=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,pse="return f32(i32((a)));";function Ms(e,t){switch(e){case de.ABS:return Tre;case de.ACOS:return Cre;case de.ACOSH:return Nre;case de.ASIN:return Ere;case de.ASINH:return Rre;case de.ATAN:return Mre;case de.ATANH:return $re;case de.COS:return Pre;case de.COSH:return Fre;case de.CEIL:return _re;case de.ELU:return t?zre:Dre;case de.ERF:return Lre;case de.EXP:return Bre;case de.EXPM1:return Ore;case de.FLOOR:return Wre;case de.IS_FINITE:return Vre;case de.IS_INF:return Ure;case de.IS_NAN:return Gre;case de.LINEAR:return Hre;case de.LOG:return jre;case de.LOG1P:return qre;case de.LOGICAL_NOT:return Xre;case de.NEG:return Kre;case de.LEAKYRELU:return t?Yre:Zre;case de.RECIPROCAL:return Jre;case de.RELU:return t?ase:Qre;case de.RELU6:return t?tse:ese;case de.RSQRT:return nse;case de.SIGMOID:return rse;case de.SIN:return sse;case de.SINH:return ise;case de.SQRT:return ose;case de.SQUARE:return lse;case de.TAN:return use;case de.TANH:return dse;case de.TO_INT:return pse;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Mt=e=>{switch(e){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${e}-component is not supported.`)}};function Tr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ms(de.LINEAR);else if(e==="relu")r=Ms(de.RELU,a);else if(e==="elu")r=Ms(de.ELU,a);else if(e==="relu6")r=Ms(de.RELU6,a);else if(e==="prelu")r=L3(De.PRELU,a);else if(e==="sigmoid")r=Ms(de.SIGMOID,a);else if(e==="leakyrelu")r=Ms(de.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Mt(a?4:1),i="";return t?i=` +`,yse="return f32(i32((a)));";function $s(e,t){switch(e){case le.ABS:return Nre;case le.ACOS:return Ere;case le.ACOSH:return Rre;case le.ASIN:return Mre;case le.ASINH:return $re;case le.ATAN:return _re;case le.ATANH:return Pre;case le.COS:return Ore;case le.COSH:return Dre;case le.CEIL:return Fre;case le.ELU:return t?Bre:Lre;case le.ERF:return Wre;case le.EXP:return Vre;case le.EXPM1:return zre;case le.FLOOR:return Ure;case le.IS_FINITE:return Gre;case le.IS_INF:return Hre;case le.IS_NAN:return jre;case le.LINEAR:return qre;case le.LOG:return Xre;case le.LOG1P:return Kre;case le.LOGICAL_NOT:return Zre;case le.NEG:return Yre;case le.LEAKYRELU:return t?Qre:Jre;case le.RECIPROCAL:return ese;case le.RELU:return t?rse:tse;case le.RELU6:return t?nse:ase;case le.ROUND:return sse;case le.RSQRT:return ise;case le.SELU:return ose;case le.SIGMOID:return lse;case le.SIGN:return use;case le.SIN:return dse;case le.SINH:return pse;case le.SOFTPLUS:return cse;case le.SQRT:return hse;case le.SQUARE:return fse;case le.STEP:return mse;case le.TAN:return gse;case le.TANH:return xse;case le.TO_INT:return yse;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var $t=e=>{switch(e){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${e}-component is not supported.`)}};function Tr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=$s(le.LINEAR);else if(e==="relu")r=$s(le.RELU,a);else if(e==="elu")r=$s(le.ELU,a);else if(e==="relu6")r=$s(le.RELU6,a);else if(e==="prelu")r=B3(Pe.PRELU,a);else if(e==="sigmoid")r=$s(le.SIGMOID,a);else if(e==="leakyrelu")r=$s(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=$t(a?4:1),i="";return t?i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { let b = getPreluActivationWeightsByOutputCoords(coords); ${r} }`:i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { ${r} - }`,i}function vo(e,t){return` + }`,i}function ko(e,t){return` ${e?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} - `}function _8(e,t,a,n,r=!1,s=!1,i=!1,o=1){v.assert(a&&o===1||!a,()=>`transposeA ${a} is not compatible with component size ${o}`);let l=` - let batch = ${e?"0":"batchIn"}; - ${a?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} + `}function E8(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` + ${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} - `,u=n?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` - fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${Mt(o)} { - var value = ${Mt(o)}(0.0); - let col = colIn * ${o}; - ${r&&i?l:` - ${a?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} + `,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` + fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${$t(s)} { + var value = ${$t(s)}(0.0); + let col = colIn * ${s}; + ${a&&r?i:` + ${e?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { - ${l} + ${i} } `} return value; } - fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${Mt(o)} { - let col = colIn * ${o}; - let batch = ${t?"0":"batchIn"}; - var value = ${Mt(o)}(0.0); - ${u} + fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${$t(s)} { + let col = colIn * ${s}; + var value = ${$t(s)}(0.0); + ${o} return value; } - `}function B3(e,t,a,n,r,s,i=!1,o=!1,l=!1,u=1){return` - ${_8(a,n,r,s,i,o,l,u)} - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Mt(u)}) { - let col = colIn * ${u}; - ${i&&o?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} + `}function W3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` + ${E8(a,n,r,s,i,o)} + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${$t(o)}) { + let col = colIn * ${o}; + ${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${vo(e,t)} + ${ko(e,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } - `}var cse=e=>e?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + `}var Ase=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, - globalRowStart / innerElementSize + inputCol); + globalRowStart / ${t} + inputCol); `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, - kStart / innerElementSize + inputCol); - `,hse=(e,t)=>e?` - let ACached0 = mm_Asub[k * innerElementSize][localRow]; - let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; - let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; - ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} - for (var i = 0; i < rowPerThread; i = i + 1) { + kStart / ${t} + inputCol); + `,bse=(e,t,a)=>e?` + let ACached0 = mm_Asub[k * ${t}][localRow]; + let ACached1 = mm_Asub[k * ${t} + 1][localRow]; + let ACached2 = mm_Asub[k * ${t} + 2][localRow]; + ${t===3?"":`let ACached3 = mm_Asub[k * ${t} + 3][localRow];`} + for (var i = 0; i < ${a}; i++) { acc[i] = BCached0 * ACached0[i] + acc[i]; acc[i] = BCached1 * ACached1[i] + acc[i]; acc[i] = BCached2 * ACached2[i] + acc[i]; ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} }`:` - for (var i = 0; i < rowPerThread; i = i + 1) { + for (var i = 0; i < ${a}; i++) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached0 * ACached.x + acc[i]; acc[i] = BCached1 * ACached.y + acc[i]; acc[i] = BCached2 * ACached.z + acc[i]; ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} - }`;function Uh(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. - Otherwise, innerElementSize ${c} must be 3 or 4. - tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),` - var mm_Asub : array, ${u/c}>, ${p}>; - var mm_Bsub : array, ${l/e[0]}>, ${n}>; + }`;function Lh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=t[1]*e[1],u=t[0]*e[0],p=a?l:n,c=a?n:l,d=p/t[0],h=n/t[1],f=e[1];return v.assert((a&&d===4&&e[1]===4||!a&&(d===3||d===4))&&p%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${d} must be 3 or 4. + tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),` + var mm_Asub : array, ${p/d}>, ${c}>; + var mm_Bsub : array, ${u/e[0]}>, ${n}>; - const rowPerThread = ${e[1]}; - const colPerThread = ${e[0]}; - const innerElementSize = ${c}; - const tileInner = ${n}; - - ${Ce()} { + ${ke()} { let localRow = i32(localId.y); - let tileRow = ${i?"0":"localRow * rowPerThread"}; + let tileRow = ${i?"0":`localRow * ${f}`}; let tileCol = i32(localId.x); - let globalRow = ${i?"0":"i32(globalId.y) * rowPerThread"}; + let globalRow = ${i?"0":`i32(globalId.y) * ${f}`}; let globalCol = i32(globalId.x); let batch = ${r?"0":"i32(globalId.z)"}; - let globalRowStart = i32(workgroupId.y) * ${o}; + let batchA = ${r||!o?"batch":"batch % uniforms.aShape[0]"}; + let batchB = ${r||!o?"batch":"batch % uniforms.bShape[0]"}; + let globalRowStart = i32(workgroupId.y) * ${l}; - let numTiles = ${r?`${Math.ceil(s/n)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; + let numTiles = ${r?`${Math.ceil(s/n)}`:`(uniforms.dimInner - 1) / ${n} + 1`}; var kStart = ${r?`i32(globalId.z) * ${s}`:"0"}; - var acc: array, rowPerThread>; + var acc: array, ${f}>; // Loop over shared dimension. - let tileRowB = localRow * ${d}; - for (var t = 0; t < numTiles; t = t + 1) { + let tileRowB = localRow * ${h}; + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${cse(a)} + ${Ase(a,d)} } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol); + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol); } - kStart = kStart + tileInner; + kStart = kStart + ${n}; workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { - let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; - let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; - let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; - ${c===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + for (var k = 0; k < ${n/d}; k++) { + let BCached0 = mm_Bsub[k * ${d}][tileCol]; + let BCached1 = mm_Bsub[k * ${d} + 1][tileCol]; + let BCached2 = mm_Bsub[k * ${d} + 2][tileCol]; + ${d===3?"":`let BCached3 = mm_Bsub[k * ${d} + 3][tileCol];`} - ${hse(a,c)} + ${bse(a,d,f)} } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var qx=e=>e?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + }`}var Ux=e=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,fse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Gh(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=e[1]*t[1],l=e[0]*t[0],u=a?o:n,p=a?n:o;v.assert(p%t[1]===0&&u%t[0]===0&&n%t[1]===0,()=>`tileAHight ${p} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let c=p/t[1],d=u/t[0],h=n/t[1],f=i?` + `,vse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Bh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],f=n/t[1],m=e[1],g=e[0],x=i?` let localRow = i32(localId.y); let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${o}; - let globalColStart = i32(workgroupId.x) * ${l}; + let globalRowStart = i32(workgroupId.y) * ${l}; + let globalColStart = i32(workgroupId.x) * ${u}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var inputRow = localRow; inputRow < ${p}; inputRow = inputRow + ${t[1]}) { - for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) { - ${qx(a)} + for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) { + ${Ux(a)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${t[1]}) { - for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${t[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalColStart + inputCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${n}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${n}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } @@ -5549,58 +5558,58 @@ return a / b;`,GK=` } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { let gRow = globalRowStart + localRow + innerRow * ${t[1]}; - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${t[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } `:` - let tileRow = i32(localId.y) * rowPerThread; - let tileCol = i32(localId.x) * colPerThread; + let tileRow = i32(localId.y) * ${m}; + let tileCol = i32(localId.x) * ${g}; - let globalRow = i32(globalId.y) * rowPerThread; - let globalCol = i32(globalId.x) * colPerThread; - let globalRowStart = i32(workgroupId.y) * ${o}; + let globalRow = i32(globalId.y) * ${m}; + let globalCol = i32(globalId.x) * ${g}; + let globalRowStart = i32(workgroupId.y) * ${l}; - let tileRowA = i32(localId.y) * ${c}; - let tileColA = i32(localId.x) * ${d}; - let tileRowB = i32(localId.y) * ${h}; + let tileRowA = i32(localId.y) * ${d}; + let tileColA = i32(localId.x) * ${h}; + let tileRowB = i32(localId.y) * ${f}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${d}; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${d}; innerRow++) { + for (var innerCol = 0; innerCol < ${h}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${qx(a)} + ${Ux(a)} } } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol + innerCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${n}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${n}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - ${fse(a)} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { + ${vse(a)} + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } @@ -5609,72 +5618,72 @@ return a / b;`,GK=` workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `;return` - var mm_Asub : array, ${p}>; - var mm_Bsub : array, ${n}>; - const rowPerThread = ${e[1]}; - const colPerThread = ${e[0]}; - const tileInner = ${n}; + var mm_Asub : array, ${c}>; + var mm_Bsub : array, ${n}>; - ${Ce()} { + ${ke()} { let batch = ${r?"0":"i32(globalId.z)"}; - let numTiles = ${r?`${Math.ceil(s/n)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; + let batchA = ${r||!o?"batch":"batch % uniforms.aShape[0]"}; + let batchB = ${r||!o?"batch":"batch % uniforms.bShape[0]"}; + let numTiles = ${r?`${Math.ceil(s/n)}`:`(uniforms.dimInner - 1) / ${n} + 1`}; var kStart = ${r?`i32(globalId.z) * ${s}`:"0"}; - var acc : array, rowPerThread>; + var acc : array, ${m}>; // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = 0.0; } } - ${f} + ${x} } - `}var mse=e=>e?` - mm_readA(batch, colA, globalRow), - mm_readA(batch, colA + 1, globalRow), - mm_readA(batch, colA + 2, globalRow), - mm_readA(batch, colA + 3, globalRow) + `}var kse=e=>e?` + mm_readA(batchA, colA, globalRow), + mm_readA(batchA, colA + 1, globalRow), + mm_readA(batchA, colA + 2, globalRow), + mm_readA(batchA, colA + 3, globalRow) `:` - mm_readA(batch, globalRow, colA), - mm_readA(batch, globalRow, colA + 1), - mm_readA(batch, globalRow, colA + 2), - mm_readA(batch, globalRow, colA + 3) - `;function gse(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),` - const tileSize = ${e[0]*4}; + mm_readA(batchA, globalRow, colA), + mm_readA(batchA, globalRow, colA + 1), + mm_readA(batchA, globalRow, colA + 2), + mm_readA(batchA, globalRow, colA + 3) + `;function wse(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return` var mm_Asub : array, ${e[0]}>; - ${Ce()} { + ${ke()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); - let numTiles = (uniforms.dimInner - 1) / tileSize + 1; + let numTiles = (uniforms.dimInner - 1) / ${a} + 1; let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - let colA = t * tileSize + tileCol * 4; - mm_Asub[tileCol] = vec4(${mse(t)}); + let colA = t * ${a} + tileCol * 4; + mm_Asub[tileCol] = vec4(${kse(t)}); workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileSize / 4; k = k + 1) { - let rowB = t * tileSize + k * 4; - let BCached = vec4(mm_readB(batch, rowB, globalCol), - mm_readB(batch, rowB + 1, globalCol), - mm_readB(batch, rowB + 2, globalCol), - mm_readB(batch, rowB + 3, globalCol)); + for (var k = 0; k < ${a/4}; k++) { + let rowB = t * ${a} + k * 4; + let BCached = vec4(mm_readB(batchB, rowB, globalCol), + mm_readB(batchB, rowB + 1, globalCol), + mm_readB(batchB, rowB + 2, globalCol), + mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); @@ -5685,28 +5694,30 @@ return a / b;`,GK=` mm_write(batch, globalRow, globalCol, acc); } - `}var xse=class{constructor(e,t,a,n,r=!1,s=!1,i=null,o=null,l=null,u=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let p=r?e[1]:e[2];if(this.isVec4=(p%4===0&&!r||t[1]%4===0&&r)&&t[2]%4===0&&!s,this.isVectorA=t[1]===1&&!r,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let h=N8(t[1],p,t[2],r);this.workgroupSize=h.workgroupSize,this.elementsPerThread=h.elementsPerThread}this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=i!=null,d=l!=null;c&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=u,this.transposeA=r,this.transposeB=s,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=d,this.batchAEqualOne=a,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],p),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${r}_${s}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` + `}var Ise=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=T8(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` ${Tr(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${B3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?Uh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?gse(this.workgroupSize,this.transposeA):Gh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)} - `}};function Ase(){return` - var sumValues : array; - ${Ce()} { + ${W3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?wse(this.workgroupSize,this.transposeA):Bh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}};function Sse(e){return` + var sumValues : array; + ${ke()} { let coords = getOutputCoords(); let batch = coords[0]; + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; - for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) { - let dataA = mm_readA(batch, row, k); - let dataB = mm_readB(batch, k, col); + for (var k = i32(localId.x); k < Length; k = k + ${e}) { + let dataA = mm_readA(batchA, row, k); + let dataB = mm_readB(batchB, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); - for(var currentSize = workgroupSizeX / 2u; currentSize > 1u; + for(var currentSize = ${e/2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { @@ -5720,11 +5731,11 @@ return a / b;`,GK=` mm_write(batch, row, col, sum); } } - `}var yse=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=a,this.shaderKey=`matMulReduce_${this.activation}_${n}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` + `}var Tse=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` ${Tr(this.activation,this.hasPreluActivationWeights)} - ${B3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} - ${Ase()} - `}};function bse(e){let t=e[1],a=e[0],n=t>a?t:a;return` + ${W3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Sse(this.workgroupSize[0])} + `}};function Cse(e){let t=e[1],a=e[0],n=t>a?t:a;return` var mm_Asub : array, ${t}>; var mm_Bsub : array, ${n}>; @@ -5734,12 +5745,14 @@ return a / b;`,GK=` // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${Ce()} { + ${ke()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${n} + 1; @@ -5747,9 +5760,9 @@ return a / b;`,GK=` var globalColA = tileCol; var globalRowB = 0; - var regA = mm_readA(batch, globalRow, globalColA); - var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + var regA = mm_readA(batchA, globalRow, globalColA); + var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${n}; globalRowB = globalRowB + ${n}; @@ -5760,9 +5773,9 @@ return a / b;`,GK=` workgroupBarrier(); - regA = mm_readA(batch, globalRow, globalColA); - regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + regA = mm_readA(batchA, globalRow, globalColA); + regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${n}; globalRowB = globalRowB + ${n}; @@ -5774,59 +5787,48 @@ return a / b;`,GK=` mm_write(batch, globalRow, globalCol, acc); } - `}var vse=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` + `}var Nse=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` ${Tr(this.activation,this.hasPreluActivationWeights)} - ${B3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} - ${bse(this.workgroupSize)} - `}},wse=class{constructor(e,t,a,n,r=!1,s=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(r&&this.outputShape[1]%4===0||!r&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Ne(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=r,this.transposeB=s,this.batchAEqualOne=a,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${r}_${s}_${a}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=a=>` - for (var i = 0; i < ${a}; i = i + 1) - { - var oldValue = atomicLoad(&(result[flatIndex + i])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + ${a>1?"value[i]":"value"}; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } - } - `,t=this.isVec4?4:1;return` - ${_8(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)} - fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Mt(t)}) { - let col = colIn * ${t}; + ${W3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Cse(this.workgroupSize)} + `}},Ese=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=we(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return` + ${E8(!1,this.transposeB,!1,!1,!1,e)} + fn mm_write(batch: i32, row : i32, colIn : i32, value : ${$t(e)}) { + let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. - ${e(t)} + for (var i = 0; i < ${e}; i = i + 1) { + ${O3("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + } } } - ${this.isVec4?Uh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Gh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},kse=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` + ${this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Bh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},Rse=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` ${Tr(this.activation,this.hasPreluActivationWeights)} - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${vo(this.addBias,this.activation)} + ${ko(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}},Ise=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` - ${Ce("index")} { + `}},Mse=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function Cr(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Ise(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Sse={kernelName:Fl,backendName:"webgpu",kernelFunc:Cr};function Ie(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Tse={kernelName:ql,backendName:"webgpu",kernelFunc:Ie};function Hh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),x=v.sizeFromShape(m),A=v.sizeFromShape(g),y=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[x,c,h]:[x,h,c],w=n?[A,f,d]:[A,d,f],S=Ie({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ie({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[S,C],_=Math.max(x,A),$=x===1,M=A===1,I=[S,C],N=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],O,L,B=[_,h,f],G=W().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(G<0){let U=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),H=U>0?U:r.thresholdToIncreaseWorkgroups,V=_*Math.ceil(h/32)*Math.ceil(f/32);V<=H||h<=8&&V<=H*2?_*h*f<=128?G=Pn.MatMulReduceProgram:_===1&&d>=2e3?G=Pn.MatMulSplitKProgram:G=Pn.MatMulSmallOutputSizeProgram:G=Pn.MatMulPackedProgram}switch(G){case Pn.MatMulReduceProgram:O=new yse(B,$,M,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(L=Cr({backend:r,attrs:{shape:B,value:0,dtype:e.dtype}}),O=new wse(B,d,$,M,a,n),s||l){L=r.runWebGPUProgram(O,I,e.dtype,N,L);let H=new kse(L.shape,s,l,i),V=null,Q=[L];s&&Q.push(s),i&&Q.push(i),l==="leakyrelu"&&(V=[{type:"float32",data:[o]}],H.uniforms+=" alpha : f32,");let Z=r.runWebGPUProgram(H,Q,L.dtype,V);E.push(L);let re=Ie({inputs:{x:Z},backend:r,attrs:{shape:y}});E.push(Z);for(let ee of E)r.disposeData(ee.dataId);return re}break}case Pn.MatMulSmallOutputSizeProgram:O=new vse(b,w,B,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();O=new xse(b,B,$,M,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${G}.`)}s&&I.push(s),i&&I.push(i),l==="leakyrelu"&&(N.push({type:"float32",data:[o]}),O.uniforms+=" alpha : f32,"),L=r.runWebGPUProgram(O,I,e.dtype,N,L);let j=Ie({inputs:{x:L},backend:r,attrs:{shape:y}});E.push(L);for(let U of E)r.disposeData(U.dataId);return j}function Cse(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Hh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Nse={kernelName:jr,backendName:"webgpu",kernelFunc:Cse},Xx=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function Cr(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Mse(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var $se={kernelName:Pl,backendName:"webgpu",kernelFunc:Cr};function Ie(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var _se={kernelName:Hl,backendName:"webgpu",kernelFunc:Ie};function Wh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),x=v.sizeFromShape(m),y=v.sizeFromShape(g),A=yo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[x,c,h]:[x,h,c],k=n?[y,f,d]:[y,d,f],S=Ie({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ie({inputs:{x:t},backend:r,attrs:{shape:k}}),E=[S,C],_=Math.max(x,y),$=[S,C],M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],I,N,O=[_,h,f],L=B().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(L<0){let G=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),H=G>0?G:r.thresholdToIncreaseWorkgroups,U=_*Math.ceil(h/32)*Math.ceil(f/32);U<=H||h<=8&&U<=H*2?_*h*f<=128?L=Pn.MatMulReduceProgram:_===1&&d>=2e3?L=Pn.MatMulSplitKProgram:L=Pn.MatMulSmallOutputSizeProgram:L=Pn.MatMulPackedProgram}switch(L){case Pn.MatMulReduceProgram:I=new Tse(O,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(N=Cr({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),I=new Ese(O,d,a,n),s||l){N=r.runWebGPUProgram(I,$,e.dtype,M,N);let H=new Rse(N.shape,s,l,i),U=null,j=[N];s&&j.push(s),i&&j.push(i),l==="leakyrelu"&&(U=[{type:"float32",data:[o]}],H.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(H,j,N.dtype,U);E.push(N);let Q=Ie({inputs:{x:V},backend:r,attrs:{shape:A}});E.push(V);for(let Z of E)r.disposeData(Z.dataId);return Q}break}case Pn.MatMulSmallOutputSizeProgram:I=new Nse(b,k,O,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let G=r.adapterInfo.isIntel();I=new Ise(b,O,a,n,s,l,i,G);break;default:throw new Error(`Unsupported MatMulProgramType ${L}.`)}s&&$.push(s),i&&$.push(i),l==="leakyrelu"&&(M.push({type:"float32",data:[o]}),I.uniforms+=" alpha : f32,"),N=r.runWebGPUProgram(I,$,e.dtype,M,N);let W=Ie({inputs:{x:N},backend:r,attrs:{shape:A}});E.push(N);for(let G of E)r.disposeData(G.dataId);return W}function Pse(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Wh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Fse={kernelName:jr,backendName:"webgpu",kernelFunc:Pse},Gx=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${L3(this.op,!1)} + ${B3(this.op,!1)} } - ${Ce("index")} { + ${ke("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -5835,16 +5837,19 @@ return a / b;`,GK=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}},P1=class{constructor(e,t,a){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=Ve(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(v.arraysEqual(t,a)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",a=` + `}},M2=class{constructor(e,t,a){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(v.arraysEqual(t,a)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",a=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${L3(this.op,this.isVec4)} + let isNaN = false; + { + ${B3(this.op,this.isVec4)} + } }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${a} var sharedBuf : array; - ${Ce("index")} { + ${ke("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -5860,25 +5865,25 @@ return a / b;`,GK=` } `}else e=` ${a} - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); let b = getBByOutputIndex(index); setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function Ya(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Ese={kernelName:ki,backendName:"webgpu",kernelFunc:Ya};function wo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=Ya({inputs:{x:n},backend:a}),l=Ya({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Rse={kernelName:Sd,backendName:"webgpu",kernelFunc:wo},mp=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let a=128;this.workgroupSize=[a,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function Ya(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Ose={kernelName:wi,backendName:"webgpu",kernelFunc:Ya};function wo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=Ya({inputs:{x:n},backend:a}),l=Ya({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Dse={kernelName:Cd,backendName:"webgpu",kernelFunc:wo},yu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${Ms(this.op,!1)} + ${$s(this.op,!1)} } - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } - `}};function it({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new mp(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ua({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==De.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[x,A]=g,y={dataId:x.dataId,dtype:x.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=new P1(e,i.shape,o.shape);return l.runWebGPUProgram(w,[y,b],ca(x.dtype,A.dtype))});else{let g=new Xx(De.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),x=new Xx(De.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),A=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(x,A,"float32")}let m=wo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||ca(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?T.fromUint8ToStringArray(c):c,f=i.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new P1(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Mse,castImpl:$se,ceilImpl:_se,concatImpl:Pse,equalImpl:Fse,expImpl:Ose,expm1Impl:Dse,floorImpl:zse,gatherNdImpl:Lse,gatherV2Impl:Bse,greaterEqualImpl:Wse,greaterImpl:Vse,lessEqualImpl:Use,lessImpl:Gse,logImpl:Hse,maxImpl:jse,maximumImpl:qse,minimumImpl:Xse,multiplyImpl:Kse,negImpl:Zse,notEqualImpl:Yse,prodImpl:Jse,rangeImpl:Qse,rsqrtImpl:eie,scatterImpl:tie,simpleAbsImpl:aie,sliceImpl:nie,stridedSliceImpl:rie,stringNGramsImpl:sie,subImpl:iie,tileImpl:oie,topKImpl:lie,transposeImpl:uie,uniqueImpl:I0e}=_h,die=it({opType:de.ABS,cpuKernelImpl:aie}),pie={kernelName:wl,backendName:"webgpu",kernelFunc:die},cie=it({opType:de.ACOS}),hie={kernelName:kl,backendName:"webgpu",kernelFunc:cie},fie=it({opType:de.ACOSH}),mie={kernelName:Il,backendName:"webgpu",kernelFunc:fie},gie=ua({opType:De.ADD,cpuKernelImpl:Mse,supportsComplex:!0}),xie={kernelName:ts,backendName:"webgpu",kernelFunc:gie},Aie=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` - ${Ce("index")} { + `}};function et({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new yu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Jt({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==Pe.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[x,y]=g,A={dataId:x.dataId,dtype:x.dtype,shape:i.shape},b={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(k,[A,b],ha(x.dtype,y.dtype))});else{let g=new Gx(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),x=new Gx(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),y=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,y,"float32"),f=l.runWebGPUProgram(x,y,"float32")}let m=wo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||ha(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?T.fromUint8ToStringArray(c):c,f=i.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:zse,castImpl:Lse,ceilImpl:Bse,concatImpl:Wse,equalImpl:Vse,expImpl:Use,expm1Impl:Gse,floorImpl:Hse,gatherNdImpl:jse,gatherV2Impl:qse,greaterEqualImpl:Xse,greaterImpl:Kse,lessEqualImpl:Zse,lessImpl:Yse,logImpl:Jse,maxImpl:Qse,maximumImpl:eie,minimumImpl:tie,multiplyImpl:aie,negImpl:nie,notEqualImpl:rie,prodImpl:sie,rangeImpl:iie,rsqrtImpl:oie,scatterImpl:lie,simpleAbsImpl:uie,sliceImpl:die,stridedSliceImpl:pie,stringNGramsImpl:cie,subImpl:hie,tileImpl:fie,topKImpl:mie,transposeImpl:gie,uniqueImpl:lfe}=Nh,xie=et({opType:le.ABS,cpuKernelImpl:uie}),yie={kernelName:vl,backendName:"webgpu",kernelFunc:xie},Aie=et({opType:le.ACOS}),bie={kernelName:kl,backendName:"webgpu",kernelFunc:Aie},vie=et({opType:le.ACOSH}),kie={kernelName:wl,backendName:"webgpu",kernelFunc:vie},wie=Jt({opType:Pe.ADD,cpuKernelImpl:zse,supportsComplex:!0}),Iie={kernelName:as,backendName:"webgpu",kernelFunc:wie},Sie=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` + ${ke("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { @@ -5889,12 +5894,11 @@ return a / b;`,GK=` } } } - `}};function yie(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Ya({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=new Aie(s);return a.runWebGPUProgram(i,n,r)}var bie={kernelName:Ks,backendName:"webgpu",kernelFunc:yie},vie=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),` - const tileSize = ${this.workgroupSize[0]}; + `}};function Tie(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Ya({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>ha(o,l)),s=n.map(o=>o.shape),i=new Sie(s);return a.runWebGPUProgram(i,n,r)}var Cie={kernelName:Ks,backendName:"webgpu",kernelFunc:Tie},Nie=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; - ${Ce()} { - var x = i32(workgroupId.x) * tileSize + i32(localId.x); - var y = i32(workgroupId.y) * tileSize + i32(localId.y); + ${ke()} { + var x = i32(workgroupId.x) * ${e} + i32(localId.x); + var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { @@ -5902,15 +5906,15 @@ return a / b;`,GK=` } workgroupBarrier(); - x = i32(workgroupId.y) * tileSize + i32(localId.x); - y = i32(workgroupId.x) * tileSize + i32(localId.y); + x = i32(workgroupId.y) * ${e} + i32(localId.x); + y = i32(workgroupId.x) * ${e} + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); } } - `}},wie=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n"} bestValue) - { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="0.0");let a=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` + { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="0.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` - var xBestValues : array; + var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${Ce("index")} { - let outputIndex = index / i32(workgroupSizeX); + ${ke("index")} { + let outputIndex = index / ${a}; let offset = getOffset(outputIndex); var bestValue = ${t}; let Length = uniforms.reduceSize; - let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX); + let WorkPerThread = DIV_CEIL(u32(Length), ${a}u); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { + k = k + ${a}) { let candidate = f32(x[offset + k]); ${e} } xBestValues[localId.x] = bestValue; workgroupBarrier(); - var reduceSize = min(u32(Length), workgroupSizeX); + var reduceSize = min(u32(Length), ${a}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -5965,29 +5969,29 @@ return a / b;`,GK=` } if (localId.x == 0u && outputIndex < uniforms.size) { - ${a} + ${n} } } - `}};function ko(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=T.getAxesPermutation(l,s),p=e;u!=null&&(p=vr({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,s),i.push(p)),T.assertAxesAreInnerMostDims(n,l,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=T.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let m=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=jse(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:x,outShape:A,outDtype:y}=Jse(p.shape,p.dtype,m,l);f=r.makeTensorInfo(A,y,x);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/m,x={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":Hd(e.dtype),y=[{type:"int32",data:[m]}],b=new Sie(x,n),w=r.runWebGPUProgram(b,[p],A,y);i.push(w),f=Ie({inputs:{x:w},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Tie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ko(r,i,s,"all",a)}var Cie={kernelName:Zs,backendName:"webgpu",kernelFunc:Tie};function Nie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ko(r,i,s,"any",a)}var Eie={kernelName:Ys,backendName:"webgpu",kernelFunc:Nie},P8=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=T.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=Ne(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${yr(this.inputShape.length-1)}`,t=()=>{let a="";if(this.outputShape.length===1)this.inputShape.length!==1&&(a+="outputCoords,");else for(let n=0;nr.disposeData(m.dataId)),f}function _ie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"all",a)}var Pie={kernelName:Zs,backendName:"webgpu",kernelFunc:_ie};function Fie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"any",a)}var Oie={kernelName:Ys,backendName:"webgpu",kernelFunc:Fie},R8=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=T.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=$e(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=we(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ar(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r u32 { return ((a - 1u) / b + 1u); } ${` - var xBestIndices : array; - var xBestValues : array; + var xBestIndices : array; + var xBestValues : array; `} - ${Ce("index")} { - let outputIndex = index / i32(workgroupSizeX); - let reduceLength = ${e()}; + ${ke("index")} { + let outputIndex = index / ${e}; + let reduceLength = ${t()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; let outputCoords = getCoordsFromIndex(outputIndex); for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { - let candidate = getX(${t()} k); + k = k + ${e}) { + let candidate = getX(${a()} k); if (!isnan(candidate) && candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = k; @@ -5997,7 +6001,7 @@ return a / b;`,GK=` xBestIndices[localId.x] = bestIndex; workgroupBarrier(); - var reduceSize = min(u32(reduceLength), workgroupSizeX); + var reduceSize = min(u32(reduceLength), ${e}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -6018,14 +6022,14 @@ return a / b;`,GK=` } } `:` - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; - var bestValue = getX(${t()} 0); - let reduceLength = ${e()}; + var bestValue = getX(${a()} 0); + let reduceLength = ${t()}; for (var i = 1; i < reduceLength; i++) { - let candidate = getX(${t()} i); + let candidate = getX(${a()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; @@ -6034,8 +6038,8 @@ return a / b;`,GK=` setOutputAtIndexI32(index, bestIndex); } } - `}};function Rie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=vr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new P8(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Mie={kernelName:Js,backendName:"webgpu",kernelFunc:Rie};function $ie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=vr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new P8(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var _ie={kernelName:kd,backendName:"webgpu",kernelFunc:$ie},Pie=it({opType:de.ASIN}),Fie={kernelName:Sl,backendName:"webgpu",kernelFunc:Pie},Oie=it({opType:de.ASINH}),Die={kernelName:Tl,backendName:"webgpu",kernelFunc:Oie},zie=it({opType:de.ATAN}),Lie={kernelName:Cl,backendName:"webgpu",kernelFunc:zie},Bie=ua({opType:De.ATAN2}),Wie={kernelName:El,backendName:"webgpu",kernelFunc:Bie},Vie=it({opType:de.ATANH}),Uie={kernelName:Nl,backendName:"webgpu",kernelFunc:Vie},Kx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),` - ${Ce("index")} { + `}};function Die(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=vr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new R8(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var zie={kernelName:Js,backendName:"webgpu",kernelFunc:Die};function Lie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=vr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new R8(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Bie={kernelName:Sd,backendName:"webgpu",kernelFunc:Lie},Wie=et({opType:le.ASIN}),Vie={kernelName:Il,backendName:"webgpu",kernelFunc:Wie},Uie=et({opType:le.ASINH}),Gie={kernelName:Sl,backendName:"webgpu",kernelFunc:Uie},Hie=et({opType:le.ATAN}),jie={kernelName:Tl,backendName:"webgpu",kernelFunc:Hie},qie=Jt({opType:Pe.ATAN2}),Xie={kernelName:Nl,backendName:"webgpu",kernelFunc:qie},Kie=et({opType:le.ATANH}),Zie={kernelName:Cl,backendName:"webgpu",kernelFunc:Kie},Hx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6067,8 +6071,8 @@ return a / b;`,GK=` setOutputAtIndex(index, ${t}); } } - `}},Gie=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` - ${Ce("index")} { + `}},Yie=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6082,8 +6086,47 @@ return a / b;`,GK=` setOutputAtIndex(index, value); } } - `}};function W3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return ko(r,s,i,"max",a)}var Hie={kernelName:Mi,backendName:"webgpu",kernelFunc:W3};function F8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ko(r,i,s,"mean",a)}var jie={kernelName:Pi,backendName:"webgpu",kernelFunc:F8};function O8(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return Ya({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Ie({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=F8({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=W3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ie({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Gie(t):(a==="avg"?r=new Kx(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new Kx(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function qie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return O8(r,p,"avg",a)}var Xie={kernelName:Qs,backendName:"webgpu",kernelFunc:qie};function Kie(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Hh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Zie={kernelName:ei,backendName:"webgpu",kernelFunc:Kie},Yie=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ra(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=ra(this.rank),t=Jie(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${F1[r]} = uniforms.start.${yr(r)} + coords.${F1[r]};`),` - ${Ce("index")} { + `}};function V3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return Io(r,s,i,"max",a)}var Jie={kernelName:$i,backendName:"webgpu",kernelFunc:V3};function M8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"mean",a)}var Qie={kernelName:Fi,backendName:"webgpu",kernelFunc:M8};function $8(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return Ya({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Ie({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=M8({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=V3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ie({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Yie(t):(a==="avg"?r=new Hx(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new Hx(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function eoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return $8(r,p,"avg",a)}var toe={kernelName:Qs,backendName:"webgpu",kernelFunc:eoe},aoe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`stride : vec2, pads : vec2, dilation : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avg_pool2d_backprop"}getUserCode(){return` + ${ke("index")} { + if (index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let batch = coords[0]; + let d = coords[3]; + + let dyRCCorner = vec2(coords.yz) - uniforms.pads; + let dyRCorner = dyRCCorner.x; + let dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) { + let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]); + + if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { + continue; + } + let idyR = i32(dyR); + + for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) { + let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]); + + if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { + continue; + } + let idyC = i32(dyC); + + let dyValue = getDy(batch, idyR, idyC, d); + + dotProd = dotProd + dyValue * uniforms.avgMultiplier; + } + } + setOutputAtIndex(index, dotProd); + } + } + `}};function noe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;C8([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new aoe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var roe={kernelName:Kc,backendName:"webgpu",kernelFunc:noe};function soe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Wh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var ioe={kernelName:ei,backendName:"webgpu",kernelFunc:soe},ooe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ia(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=ia(this.rank),t=loe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${$2[r]} = uniforms.start.${Ar(r)} + coords.${$2[r]};`),` + ${ke("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); @@ -6092,59 +6135,44 @@ return a / b;`,GK=` setOutputAtIndex(index, getSource(${t})); } } - `}},F1=["x","y","z","w","u","v"];function Jie(e){if(e===1)return"sourceLoc";if(e<=6)return F1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Au(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=It.parseSliceParams(r,s,i);if(It.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=nie(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new Yie(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Qie={kernelName:Zl,backendName:"webgpu",kernelFunc:Au},eoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,y)=>A*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),m=vr({inputs:{x:f},backend:a,attrs:{perm:u}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:p}}),x=Au({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>a.disposeData(A.dataId)),x},toe={kernelName:Rl,backendName:"webgpu",kernelFunc:eoe},aoe=` + `}},$2=["x","y","z","w","u","v"];function loe(e){if(e===1)return"sourceLoc";if(e<=6)return $2.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Au(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=die(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new ooe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var uoe={kernelName:Xl,backendName:"webgpu",kernelFunc:Au},doe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),m=vr({inputs:{x:f},backend:a,attrs:{perm:u}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:p}}),x=Au({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>a.disposeData(y.dataId)),x},poe={kernelName:El,backendName:"webgpu",kernelFunc:doe},coe=` fn bincount_write(index: i32, value: f32) { - var oldValue = atomicLoad(& (result[index])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + value; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak( - &(result[index]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } + ${O3("&result[index]","value","float32")} } -`,noe=` +`,hoe=` fn bincount_write(index: i32, value: f32) { - result[index] = value; + atomicStore(&result[index], bitcast(value)); } -`,D8=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` - ${this.binaryOutput?noe:aoe} - ${Ce("index")} { +`,_8=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` + ${this.binaryOutput?hoe:coe} + ${ke("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(index))":"1."}; + let value = ${this.binaryOutput?1:this.hasWeights?"getW(index)":"1."}; bincount_write(indexVal, value); } }`:`let coord = getCoordsFromIndex(index); if (coordsInBounds2D(coord, uniforms.xShape)) { let indexVal = i32(getX(coord[0], coord[1])); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(coord[0], coord[1]))":"1."}; + let value = ${this.binaryOutput?1:this.hasWeights?"getW(coord[0], coord[1])":"1."}; bincount_write(coord.x * uniforms.binCountSize + indexVal, value); } }`} } - `}};function roe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Cr({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new D8([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var soe={kernelName:Id,backendName:"webgpu",kernelFunc:roe},z8=ua({opType:De.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Yse}),ioe={kernelName:Li,backendName:"webgpu",kernelFunc:z8};function gp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.real},backend:a})}var ooe={kernelName:Md,backendName:"webgpu",kernelFunc:gp};function loe(e,t){let a=new mp(e.shape,de.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function O1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ya({inputs:{x:r},backend:a});let i=fn(r.shape),o=O1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=wo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=gp({inputs:{input:r},backend:a}),o=O1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ya({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=$se(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return loe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=z8({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var uoe={kernelName:ti,backendName:"webgpu",kernelFunc:O1},doe=it({opType:de.CEIL,cpuKernelImpl:_se}),poe={kernelName:ai,backendName:"webgpu",kernelFunc:doe},coe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` - ${Ce("index")} { + `}};function foe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Cr({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new _8([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var moe={kernelName:Td,backendName:"webgpu",kernelFunc:foe},P8=Jt({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:rie}),goe={kernelName:Bi,backendName:"webgpu",kernelFunc:P8};function yp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.real},backend:a})}var xoe={kernelName:zd,backendName:"webgpu",kernelFunc:yp};function yoe(e,t){let a=new yu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ya({inputs:{x:r},backend:a});let i=fn(r.shape),o=_2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=wo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=yp({inputs:{input:r},backend:a}),o=_2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ya({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Lse(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return yoe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=P8({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Aoe={kernelName:ti,backendName:"webgpu",kernelFunc:_2},boe=et({opType:le.CEIL,cpuKernelImpl:Bse}),voe={kernelName:ai,backendName:"webgpu",kernelFunc:boe},koe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + ${ke("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); - var clampedValue : vec4; - for (var i = 0; i < 4; i = i + 1) { - if (isnan(value[i])) { - clampedValue[i] = value[i]; - } else { - clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); - } - } - + var clampedValue = clamp( + value, vec4(uniforms.minVal), vec4(uniforms.maxVal)); + clampedValue = select(clampedValue, value, isnanVec4(value)); setOutputAtIndex(index, clampedValue); } } - `}},hoe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` - ${Ce("index")} { + `}},woe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + ${ke("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -6154,8 +6182,8 @@ return a / b;`,GK=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function foe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new coe(r.shape):o=new hoe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var moe={kernelName:as,backendName:"webgpu",kernelFunc:foe},goe=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;ngp({inputs:{input:y},backend:a})),m=e.map(y=>jh({inputs:{input:y},backend:a})),g=Ju(f,t,a),x=Ju(m,t,a),A=wo({inputs:{real:g,imag:x},backend:a});return f.forEach(y=>a.disposeData(y.dataId)),m.forEach(y=>a.disposeData(y.dataId)),a.disposeData(g.dataId),a.disposeData(x.dataId),A}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(w=>{let S=[-1,v.sizeFromShape(w.shape.slice(t))];return Ie({inputs:{x:w},backend:a,attrs:{shape:S}})}),m=f.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=T.computeOutShape(f.map(w=>w.shape),1),x=f[0].shape[0]===1,A=Pse(m,g,n,x),y=T.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(y,n,A);return f.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;gf.shape),u=new goe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;fa.disposeData(f.dataId));let h=Ie({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Aoe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ie({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function L8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ya({inputs:{x:l[0]},backend:a}):Ju(l,s,a)}var yoe={kernelName:Ml,backendName:"webgpu",kernelFunc:L8};function boe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=E=>{switch(E){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${E} is not supported.`)}},c=E=>{switch(E){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${E} is not supported.`)}},d=e?` + `}};function Vh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Coe={kernelName:Fd,backendName:"webgpu",kernelFunc:Vh};function Ju(e,t,a){let n=e[0].dtype;if(n==="complex64"){let f=e.map(A=>yp({inputs:{input:A},backend:a})),m=e.map(A=>Vh({inputs:{input:A},backend:a})),g=Ju(f,t,a),x=Ju(m,t,a),y=wo({inputs:{real:g,imag:x},backend:a});return f.forEach(A=>a.disposeData(A.dataId)),m.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(x.dataId),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(k=>{let S=[-1,v.sizeFromShape(k.shape.slice(t))];return Ie({inputs:{x:k},backend:a,attrs:{shape:S}})}),m=f.map(k=>({vals:a.readSync(k.dataId),shape:k.shape})),g=T.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,y=Wse(m,g,n,x),A=T.computeOutShape(e.map(k=>k.shape),t),b=a.makeTensorInfo(A,n,y);return f.forEach(k=>a.disposeData(k.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;gf.shape),u=new Toe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;fa.disposeData(f.dataId));let h=Ie({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Noe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ie({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function F8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ya({inputs:{x:l[0]},backend:a}):Ju(l,s,a)}var Eoe={kernelName:Rl,backendName:"webgpu",kernelFunc:F8};function Roe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=E=>{switch(E){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${E} is not supported.`)}},c=E=>{switch(E){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${E} is not supported.`)}},d=e?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6184,7 +6212,7 @@ return a / b;`,GK=` row, col / outWidth, col % outWidth); - `,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",x=e?"col":"row",A=` + `,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",x=e?"col":"row",y=` let inChannels = uniforms.wShape[2]; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; let outRow = ${g} / outWidth; @@ -6195,7 +6223,7 @@ return a / b;`,GK=` let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0]; let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1]; let xCh = ${x} % inChannels; - var resData = ${Mt(o)}(0.0); + var resData = ${$t(o)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) { @@ -6203,44 +6231,44 @@ return a / b;`,GK=` let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${p(o)} } - return resData;`,y=e?t&&n?` + return resData;`,A=e?t&&n?` let col = colIn * ${o}; - ${A}`:` + ${y}`:` let col = colIn * ${o}; if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${A} + ${y} } - return ${Mt(o)}(0.0);`:n&&a?` + return ${$t(o)}(0.0);`:n&&a?` let col = colIn * ${o}; - ${A}`:` + ${y}`:` let col = colIn * ${o}; if (row < uniforms.dimInner && col < uniforms.dimBOuter) { - ${A} + ${y} } - return ${Mt(o)}(0.0);`,b=`${c(l)}`,w=Mt(u),S=Mt(e?o:l),C=Mt(e?l:o);return` + return ${$t(o)}(0.0);`,b=`${c(l)}`,k=$t(u),S=$t(e?o:l),C=$t(e?l:o);return` ${Tr(s,i,u===4,4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${S} { - ${e?y:b} + ${e?A:b} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${C} { - ${e?b:y} + ${e?b:A} } - fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${w}) { + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) { let col = colIn * ${u}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueIn; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${h} - ${vo(r,s)} + ${ko(r,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var voe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=F3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=O3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Uh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Gh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` - ${boe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} + }`}var Moe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Bh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + ${Roe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} - `}},woe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + `}},$oe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${Tr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); @@ -6262,11 +6290,11 @@ return a / b;`,GK=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${vo(this.addBias,this.activation)} + ${ko(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${Ce("index")} { + ${ke("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} @@ -6286,9 +6314,9 @@ return a / b;`,GK=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}},koe=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` - ${Ce("index")} { + `}},_oe=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, + inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` + ${ke("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -6310,74 +6338,8 @@ return a / b;`,GK=` setOutputAtIndex(index, value); } } - `}};function Wc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Ioe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let x=a.inHeight*a.inWidth*a.inChannels;h=Ie({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,x]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,x,a.outChannels]}})}else h=Ie({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let x=Wc(s.shape,l);x!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:x}}),d.push(s))}if(r!=null){let x=Wc(r.shape,l);x!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:x}}),d.push(r))}let m=Hh({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ie({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let x of d)n.disposeData(x.dataId);return g}function Soe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:x,dataFormat:A}=a,y=A==="channelsLast",b=l*u*p,w=m*f,S=y?[a.batchSize,w,b]:[a.batchSize,b,w],C=new koe(S,y),E=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],_=n.runWebGPUProgram(C,[e],e.dtype,E),$=[];$.push(_);let M=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if($.push(M),s!=null){let O=Wc(s.shape,y);O!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:O}}),$.push(s))}if(r!=null){let O=Wc(r.shape,y);O!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:O}}),$.push(r))}let I=Hh({a:y?_:M,b:y?M:_,transposeA:!y,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=Ie({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});$.push(I);for(let O of $)n.disposeData(O.dataId);return N}function B8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=W().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Ioe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(W().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Soe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,x=[a.padInfo.top,a.padInfo.left],A=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new woe(a,l,o,u);else{let S=p?a.outHeight*a.outWidth:a.outChannels,C=p?a.outChannels:a.outHeight*a.outWidth,E=a.filterHeight*a.filterWidth*a.inChannels;A.push({type:"int32",data:[S]},{type:"int32",data:[C]},{type:"int32",data:[E]});let _=n.adapterInfo.isIntel();g=new voe(a,S,C,E,l,o,u,_)}let y=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),y.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),y.push(s)),b.push(s)),o==="leakyrelu"&&(A.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,A);for(let S of y)n.disposeData(S.dataId);return w}function Toe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return B8({x:r,filter:s,convInfo:d,backend:n})}var Coe={kernelName:ni,backendName:"webgpu",kernelFunc:Toe};function Noe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` - let coord1 = vec4(coordX, coordY, col + 1, rowInner); - let coord2 = vec4(coordX, coordY, col + 2, rowInner); - let coord3 = vec4(coordX, coordY, col + 3, rowInner); - let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)]; - let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)]; - let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)]; - let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)]; - return vec4(v0, v1, v2, v3); - `;default:throw new Error(`innerElementSize ${n} is not supported.`)}},a=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${` - let outRow = row / uniforms.outShape[2]; - let outCol = row % uniforms.outShape[2]; - - let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); - let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; - let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); - let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); - if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { - return ${Mt(e)}(0.0); - } - if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { - return ${Mt(e)}(0.0); - } - let coord = vec4( - batch, - i32(xR), - i32(xC), - col % uniforms.outBackprop[3]); - return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`} - } - return ${Mt(e)}(0.0);`;return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Mt(e)} { - let col = colIn * ${e}; - ${a} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Mt(e)} { - let col = colIn * ${e}; - let coordX = uniforms.filterDims.x - 1 - - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); - let coordY = uniforms.filterDims.y - 1 - - (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; - if (row < uniforms.dimInner && col < uniforms.dimBOuter && - coordX >= 0 && coordY >= 0) { - let rowInner = row % uniforms.outBackprop[3]; - let coord = vec4(coordX, coordY, col, rowInner); - ${t(e)} - } - return ${Mt(e)}(0.0); - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Mt(e)}) { - let col = colIn * ${e}; - if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) { - var value = valueInput; - let outCoord = vec4( - batch, - row / uniforms.outShape[2], - row % uniforms.outShape[2], - col); - result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; - } - }`}var Eoe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=F3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=O3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Uh(this.elementsPerThread,this.workgroupSize):Gh(this.elementsPerThread,this.workgroupSize);return` - ${Noe(this.isVec4?4:1)} - ${e} - `}},Roe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return` - ${Ce("index")} { + `}};function Gc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Poe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let x=a.inHeight*a.inWidth*a.inChannels;h=Ie({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,x]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,x,a.outChannels]}})}else h=Ie({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let x=Gc(s.shape,l);x!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:x}}),d.push(s))}if(r!=null){let x=Gc(r.shape,l);x!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:x}}),d.push(r))}let m=Wh({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ie({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let x of d)n.disposeData(x.dataId);return g}function Foe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:x,dataFormat:y}=a,A=y==="channelsLast",b=l*u*p,k=m*f,S=A?[a.batchSize,k,b]:[a.batchSize,b,k],C=new _oe(S,A),E=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],_=n.runWebGPUProgram(C,[e],e.dtype,E),$=[];$.push(_);let M=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if($.push(M),s!=null){let O=Gc(s.shape,A);O!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:O}}),$.push(s))}if(r!=null){let O=Gc(r.shape,A);O!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:O}}),$.push(r))}let I=Wh({a:A?_:M,b:A?M:_,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=Ie({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});$.push(I);for(let O of $)n.disposeData(O.dataId);return N}function O8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=B().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Poe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(B().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Foe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,x=[a.padInfo.top,a.padInfo.left],y=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new $oe(a,l,o,u);else{let S=p?a.outHeight*a.outWidth:a.outChannels,C=p?a.outChannels:a.outHeight*a.outWidth,E=a.filterHeight*a.filterWidth*a.inChannels;y.push({type:"int32",data:[S]},{type:"int32",data:[C]},{type:"int32",data:[E]});let _=n.adapterInfo.isIntel();g=new Moe(a,S,C,E,l,o,u,_)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(y.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let k=n.runWebGPUProgram(g,b,e.dtype,y);for(let S of A)n.disposeData(S.dataId);return k}function Ooe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return O8({x:r,filter:s,convInfo:d,backend:n})}var Doe={kernelName:ni,backendName:"webgpu",kernelFunc:Ooe},zoe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return` + ${ke("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6425,8 +6387,115 @@ return a / b;`,GK=` setOutputAtIndex(index, dotProd); } } - `}};function Moe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(W().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.filterHeight<=2&&d.filterWidth<=2&&d.outChannels<=16&&d.inChannels===1)f=new Roe(d);else{f=new Eoe(d);let m=d.inHeight*d.inWidth,g=d.inChannels,x=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var $oe={kernelName:ri,backendName:"webgpu",kernelFunc:Moe},_oe=it({opType:de.COS}),Poe={kernelName:si,backendName:"webgpu",kernelFunc:_oe},Foe=it({opType:de.COSH}),Ooe={kernelName:ii,backendName:"webgpu",kernelFunc:Foe},Doe=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` - ${Ce("index")} { + `}},Loe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pad : vec2, stride : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + ${ke("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let wR = coords[0]; + let wC = coords[1]; + let d1 = coords[2]; + let d2 = coords[3]; + + // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var b = 0; b < uniforms.batchSize; b = b + 1) { + for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) { + let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0]; + if (xR < 0 || xR >= uniforms.inHeight) { + continue; + } + + for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) { + let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1]; + + if (xC < 0 || xC >= uniforms.inWidth) { + continue; + } + + if (${this.isChannelsLast}) { + let dyValue = getDy(b, yR, yC, d2); + let xValue = getX(b, xR, xC, d1); + dotProd = dotProd + xValue * dyValue; + } else { + let dyValue = getDy(b, d2, yR, yC); + let xValue = getX(b, d1, xR, xC); + dotProd = dotProd + xValue * dyValue; + } + } + } + } + setOutputAtIndex(index, dotProd); + } + } + `}};function Boe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Loe(d),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,f)}var Woe={kernelName:Nd,backendName:"webgpu",kernelFunc:Boe};function Voe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)]; + let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)]; + let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)]; + let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)]; + return vec4(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${n} is not supported.`)}},a=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { + ${` + let outRow = row / uniforms.outShape[2]; + let outCol = row % uniforms.outShape[2]; + + let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); + let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; + let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); + let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); + if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { + return ${$t(e)}(0.0); + } + if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { + return ${$t(e)}(0.0); + } + let coord = vec4( + batch, + i32(xR), + i32(xC), + col % uniforms.outBackprop[3]); + return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`} + } + return ${$t(e)}(0.0);`;return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${$t(e)} { + let col = colIn * ${e}; + ${a} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$t(e)} { + let col = colIn * ${e}; + let coordX = uniforms.filterDims.x - 1 - + row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); + let coordY = uniforms.filterDims.y - 1 - + (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; + if (row < uniforms.dimInner && col < uniforms.dimBOuter && + coordX >= 0 && coordY >= 0) { + let rowInner = row % uniforms.outBackprop[3]; + let coord = vec4(coordX, coordY, col, rowInner); + ${t(e)} + } + return ${$t(e)}(0.0); + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${$t(e)}) { + let col = colIn * ${e}; + if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) { + var value = valueInput; + let outCoord = vec4( + batch, + row / uniforms.outShape[2], + row % uniforms.outShape[2], + col); + result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; + } + }`}var Uoe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Lh(this.elementsPerThread,this.workgroupSize):Bh(this.elementsPerThread,this.workgroupSize);return` + ${Voe(this.isVec4?4:1)} + ${e} + `}};function Goe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(B().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.filterHeight<=2&&d.filterWidth<=2&&d.outChannels<=16&&d.inChannels===1)f=new zoe(d);else{f=new Uoe(d);let m=d.inHeight*d.inWidth,g=d.inChannels,x=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var Hoe={kernelName:ri,backendName:"webgpu",kernelFunc:Goe},joe=et({opType:le.COS}),qoe={kernelName:si,backendName:"webgpu",kernelFunc:joe},Xoe=et({opType:le.COSH}),Koe={kernelName:ii,backendName:"webgpu",kernelFunc:Xoe},Zoe=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${a}); @@ -6481,24 +6550,24 @@ return a / b;`,GK=` } } } - `}},zoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Doe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Loe={kernelName:ui,backendName:"webgpu",kernelFunc:zoe},bd;(function(e){e.Prod="*",e.Sum="+"})(bd||(bd={}));var Zx=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===bd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Yx(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` - ${Ce("index")} { + `}},Yoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Zoe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Joe={kernelName:ui,backendName:"webgpu",kernelFunc:Yoe},kd;(function(e){e.Prod="*",e.Sum="+"})(kd||(kd={}));var jx=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===kd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${qx(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` + ${ke("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${Jx(e,"coords",this.op)}; + let end = ${Xx(e,"coords",this.op)}; var val = ${a}; let pow2 = i32(pow(2.0, uniforms.index)); if (${r}) { let idx = ${s}; - ${Jx(e,"coords",this.op)} = idx; - val ${this.op}= getX(${Yx(e,"coords",this.op)}); + ${Xx(e,"coords",this.op)} = idx; + val ${this.op}= getX(${qx(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function Yx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Jx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function W8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=vr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ya({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Zx(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new Zx(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=vr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Boe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return W8(bd.Prod,r,a,s,i,o)}var Woe={kernelName:oi,backendName:"webgpu",kernelFunc:Boe};function Voe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return W8(bd.Sum,r,a,s,i,o)}var Uoe={kernelName:li,backendName:"webgpu",kernelFunc:Voe};function Goe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Cr({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new D8(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var Hoe={kernelName:Td,backendName:"webgpu",kernelFunc:Goe},joe=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` - ${Ce("index")} { + `}};function qx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Xx(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function D8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=vr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ya({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new jx(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new jx(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=vr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Qoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return D8(kd.Prod,r,a,s,i,o)}var ele={kernelName:oi,backendName:"webgpu",kernelFunc:Qoe};function tle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return D8(kd.Sum,r,a,s,i,o)}var ale={kernelName:li,backendName:"webgpu",kernelFunc:tle};function nle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Cr({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new _8(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var rle={kernelName:Ed,backendName:"webgpu",kernelFunc:nle},sle=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6517,7 +6586,7 @@ return a / b;`,GK=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function qoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=[{type:"int32",data:[s]}],g=new joe(f,i);return a.runWebGPUProgram(g,[r],r.dtype,m)}var Xoe={kernelName:di,backendName:"webgpu",kernelFunc:qoe},Koe=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ile(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=[{type:"int32",data:[s]}],g=new sle(f,i);return a.runWebGPUProgram(g,[r],r.dtype,m)}var ole={kernelName:di,backendName:"webgpu",kernelFunc:ile},lle=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${Tr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${a}>; @@ -6531,7 +6600,7 @@ return a / b;`,GK=` return value; } - ${Ce()} { + ${ke()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pad; @@ -6574,12 +6643,12 @@ return a / b;`,GK=` value = fma(xVal, wVal, value); } } - ${vo(this.addBias,this.activation)} + ${ko(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}},V8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return` + `}},z8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` ${Tr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); @@ -6589,14 +6658,12 @@ return a / b;`,GK=` return value; } - const strideHeight = ${this.convInfo.strideHeight}; - const strideWidth = ${this.convInfo.strideWidth}; - ${Ce()} { + ${ke()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; - let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad; + let xRCCorner = vec2(r, c) * vec2(${t}, ${a}) - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; @@ -6616,7 +6683,7 @@ return a / b;`,GK=` for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { - dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]); + dotProd[i] = fma(xVals[i * ${a} + wC], wValue, dotProd[i]); } } } @@ -6626,16 +6693,16 @@ return a / b;`,GK=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${vo(this.addBias,this.activation)} + ${ko(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}},U8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` + `}},L8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, + filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${Tr(this.activation,this.hasPreluActivation,!1,4)} - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -6694,11 +6761,51 @@ return a / b;`,GK=` } } } - ${vo(this.addBias,this.activation)} + ${ko(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function Zoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new Koe(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new V8(h):(g=new U8(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var Yoe={kernelName:pi,backendName:"webgpu",kernelFunc:Zoe},G8=ua({opType:De.MUL,cpuKernelImpl:Kse,supportsComplex:!0}),Joe={kernelName:zi,backendName:"webgpu",kernelFunc:G8};function V3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ko(r,s,i,"sum",a)}var Qoe={kernelName:ro,backendName:"webgpu",kernelFunc:V3};function ele(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=V3({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var tle={kernelName:Cd,backendName:"webgpu",kernelFunc:ele},ale=it({opType:de.ELU}),nle={kernelName:hi,backendName:"webgpu",kernelFunc:ale},rle=ua({opType:De.EQUAL,dtype:"bool",cpuKernelImpl:Fse}),sle={kernelName:fi,backendName:"webgpu",kernelFunc:rle},ile=it({opType:de.ERF}),ole={kernelName:$l,backendName:"webgpu",kernelFunc:ile},H8=it({opType:de.EXP,cpuKernelImpl:Ose,dtype:"float32"}),lle={kernelName:mi,backendName:"webgpu",kernelFunc:H8};function D1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ie({inputs:{x:s},backend:n,attrs:{shape:o}})}var ule={kernelName:_l,backendName:"webgpu",kernelFunc:D1},dle=it({opType:de.EXPM1,cpuKernelImpl:Dse}),ple={kernelName:Pl,backendName:"webgpu",kernelFunc:dle},Qx=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function ule(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new lle(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new z8(h):(g=new L8(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var dle={kernelName:pi,backendName:"webgpu",kernelFunc:ule},ple=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + ${ke("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); + setOutputAtIndex(index, value); + } + } + `}};function cle(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ie({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new ple(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=Ie({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var hle={kernelName:Rd,backendName:"webgpu",kernelFunc:cle},fle=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + ${ke("index")} { + if (index < uniforms.size) { + let neg_infinity = -3.4e38; + let coords = getOutputCoords(); + let batch = coords.x; + let d1 = coords.w; + let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad; + let hBeg = outTopLeftCorner.x; + let wBeg = outTopLeftCorner.y; + + var curVal = neg_infinity; + for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) { + let hIn = hBeg + h * uniforms.dilation[0]; + + if (hIn >= 0 && hIn < uniforms.xShape[1]) { + for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) { + let wIn = wBeg + w * uniforms.dilation[1]; + + if (wIn >= 0 && wIn < uniforms.xShape[2]) { + let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1); + if (val > curVal) { + curVal = val; + } + } + } + } + } + + setOutputAtIndex(index, curVal); + } + } + `}};function mle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new fle(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var gle={kernelName:Md,backendName:"webgpu",kernelFunc:mle},B8=Jt({opType:Pe.MUL,cpuKernelImpl:aie,supportsComplex:!0}),xle={kernelName:Li,backendName:"webgpu",kernelFunc:B8};function U3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"sum",a)}var yle={kernelName:io,backendName:"webgpu",kernelFunc:U3};function Ale(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m=0&&(d=U3({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var ble={kernelName:$d,backendName:"webgpu",kernelFunc:Ale},vle=et({opType:le.ELU}),kle={kernelName:hi,backendName:"webgpu",kernelFunc:vle},wle=Jt({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:Vse}),Ile={kernelName:fi,backendName:"webgpu",kernelFunc:wle},Sle=et({opType:le.ERF}),Tle={kernelName:Ml,backendName:"webgpu",kernelFunc:Sle},W8=et({opType:le.EXP,cpuKernelImpl:Use,dtype:"float32"}),Cle={kernelName:mi,backendName:"webgpu",kernelFunc:W8};function P2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ie({inputs:{x:s},backend:n,attrs:{shape:o}})}var Nle={kernelName:$l,backendName:"webgpu",kernelFunc:P2},Ele=et({opType:le.EXPM1,cpuKernelImpl:Gse}),Rle={kernelName:_l,backendName:"webgpu",kernelFunc:Ele},Kx=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -6725,14 +6832,14 @@ return a / b;`,GK=` return result; } - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function j8(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=Ie({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Qx("real",u),c=new Qx("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let x=a.runWebGPUProgram(c,d,"float32",m);o.push(x);let A=wo({inputs:{real:g,imag:x},backend:a});o.push(A);let y=Ie({inputs:{x:A},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),y}function cle(e){let{inputs:t,backend:a}=e,{input:n}=t;return j8(n,!1,a)}var hle={kernelName:Nd,backendName:"webgpu",kernelFunc:cle},fle=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` - ${Ce("index")} { + `}};function V8(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=Ie({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Kx("real",u),c=new Kx("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let x=a.runWebGPUProgram(c,d,"float32",m);o.push(x);let y=wo({inputs:{real:g,imag:x},backend:a});o.push(y);let A=Ie({inputs:{x:y},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Mle(e){let{inputs:t,backend:a}=e,{input:n}=t;return V8(n,!1,a)}var $le={kernelName:_d,backendName:"webgpu",kernelFunc:Mle},_le=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -6740,9 +6847,9 @@ return a / b;`,GK=` setOutputAtIndex(index, outputValue); } } - `}},mle={kernelName:gi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new fle(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},gle=it({opType:de.FLOOR,cpuKernelImpl:zse}),xle={kernelName:xi,backendName:"webgpu",kernelFunc:gle},Ale=ua({opType:De.INT_DIV,dtype:"int32"}),yle={kernelName:Ai,backendName:"webgpu",kernelFunc:Ale},ble=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}},Ple={kernelName:gi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new _le(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Fle=et({opType:le.FLOOR,cpuKernelImpl:Hse}),Ole={kernelName:xi,backendName:"webgpu",kernelFunc:Fle},Dle=Jt({opType:Pe.INT_DIV,dtype:"int32"}),zle={kernelName:yi,backendName:"webgpu",kernelFunc:Dle},Lle=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; - ${Ce("index")} { + ${ke("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -6752,8 +6859,8 @@ return a / b;`,GK=` } } } - `}},vle={kernelName:rd,backendName:"webgpu",kernelFunc:wle},Yo,Wm=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),cc=new Map;function wle(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,f=i||o;if(u||l||f){let A;if(h){let $=r;if(!cc.has($)||cc.get($).expired){let M={source:$};cc.set($,a.device.importExternalTexture(M))}A={width:p,height:c,format:null,usage:null,texture:cc.get($)}}else{if(f){let N=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Yo==null||N!==Wm)&&(Wm=N,Yo=document.createElement("canvas").getContext("2d",{willReadFrequently:Wm})),Yo.canvas.width=p,Yo.canvas.height=c,Yo.drawImage(r,0,0,p,c),r=Yo.canvas}let $=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M="rgba8unorm",I=a.textureManager.acquireTexture(d[1],d[0],M,$);a.queue.copyExternalImageToTexture({source:r},{texture:I},[d[1],d[0]]),A={width:p,height:c,format:M,usage:$,texture:I}}let y=v.sizeFromShape(d),b=v.computeStrides(d),w=new ble(d,s,h),S=[{type:"uint32",data:[y]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],C=a.makeTensorInfo([c,p],"int32"),E=a.tensorMap.get(C.dataId);E.resourceInfo=A;let _=a.runWebGPUProgram(w,[C],"int32",S);return a.disposeData(C.dataId),_}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let A=m.length,y=0;for(let b=0;b(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}},Ile={kernelName:yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new kle(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function Sle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return B8({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Tle={kernelName:qr,backendName:"webgpu",kernelFunc:Sle};function Cle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],x=i!=null,A=o!=null;x&&g.push(i),A&&g.push(o);let y=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new V8(m,x,d,A):(b=new U8(m,x,d,A),y.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(y.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",y)}var Nle={kernelName:Xr,backendName:"webgpu",kernelFunc:Cle},Ele=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ra(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` - ${Ce("index")} { + `}},Ule={kernelName:Ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Vle(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function Gle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return O8({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Hle={kernelName:qr,backendName:"webgpu",kernelFunc:Gle};function jle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],x=i!=null,y=o!=null;x&&g.push(i),y&&g.push(o);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new z8(m,x,d,y):(b=new L8(m,x,d,y),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var qle={kernelName:Xr,backendName:"webgpu",kernelFunc:jle},Xle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ia(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -6779,8 +6886,8 @@ return a / b;`,GK=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function Rle(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=T.prepareAndValidate(n,r),d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=Ie({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let A=a.readSync(r.dataId),y=a.bufferSync(n),b=Lse(A,y,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let f=new Ele(i,[u,p]),m=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(f,[h,d],h.dtype,m),x=Ie({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),x}var Mle={kernelName:bi,backendName:"webgpu",kernelFunc:Rle},$le=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=_le(this.aShape);return` - ${Ce("index")} { + `}};function Kle(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=T.prepareAndValidate(n,r),d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=Ie({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let y=a.readSync(r.dataId),A=a.bufferSync(n),b=jse(y,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let f=new Xle(i,[u,p]),m=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(f,[h,d],h.dtype,m),x=Ie({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),x}var Zle={kernelName:bi,backendName:"webgpu",kernelFunc:Kle},Yle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=Jle(this.aShape);return` + ${ke("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -6788,8 +6895,79 @@ return a / b;`,GK=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function _le(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new $le(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let x=Ie({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(A=>a.disposeData(A.dataId)),x}var Ple={kernelName:Ol,backendName:"webgpu",kernelFunc:q8},Fle=ua({opType:De.GREATER,cpuKernelImpl:Vse,dtype:"bool"}),Ole={kernelName:vi,backendName:"webgpu",kernelFunc:Fle},Dle=ua({opType:De.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Wse}),zle={kernelName:wi,backendName:"webgpu",kernelFunc:Dle};function Lle(e){let{inputs:t,backend:a}=e,{input:n}=t;return j8(n,!0,a)}var Ble={kernelName:Ed,backendName:"webgpu",kernelFunc:Lle},Wle=it({opType:de.IS_FINITE,dtype:"bool"}),Vle={kernelName:Dl,backendName:"webgpu",kernelFunc:Wle},Ule=it({opType:de.IS_INF,dtype:"bool"}),Gle={kernelName:zl,backendName:"webgpu",kernelFunc:Ule},Hle=it({opType:de.IS_NAN,dtype:"bool"}),jle={kernelName:Ii,backendName:"webgpu",kernelFunc:Hle};function qle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new mp(r.shape,de.LEAKYRELU);return o.uniforms="alpha : f32,",a.runWebGPUProgram(o,[r],"float32",i)}var Xle={kernelName:Si,backendName:"webgpu",kernelFunc:qle},Kle=ua({opType:De.LESS,dtype:"bool",cpuKernelImpl:Gse}),Zle={kernelName:Ti,backendName:"webgpu",kernelFunc:Kle},Yle=ua({opType:De.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Use}),Jle={kernelName:Ci,backendName:"webgpu",kernelFunc:Yle},Qle=it({opType:de.LOG,cpuKernelImpl:Hse}),eue={kernelName:Ni,backendName:"webgpu",kernelFunc:Qle},tue=it({opType:de.LOG1P}),aue={kernelName:Ll,backendName:"webgpu",kernelFunc:tue},nue=ua({opType:De.LOGICAL_AND,dtype:"bool"}),rue={kernelName:Ei,backendName:"webgpu",kernelFunc:nue},sue=it({opType:de.LOGICAL_NOT}),iue={kernelName:Ri,backendName:"webgpu",kernelFunc:sue},oue=ua({opType:De.MAX,cpuKernelImpl:qse}),lue={kernelName:$i,backendName:"webgpu",kernelFunc:oue};function uue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return O8(r,p,"max",a)}var due={kernelName:_i,backendName:"webgpu",kernelFunc:uue};function pue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ko(r,s,i,"min",a)}var cue={kernelName:Fi,backendName:"webgpu",kernelFunc:pue},hue=ua({opType:De.MIN,cpuKernelImpl:Xse}),fue={kernelName:Oi,backendName:"webgpu",kernelFunc:hue},mue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=ra(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${Ce("index")} { + `}};function Jle(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Yle(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let x=Ie({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(y=>a.disposeData(y.dataId)),x}var Qle={kernelName:Fl,backendName:"webgpu",kernelFunc:U8},eue=Jt({opType:Pe.GREATER,cpuKernelImpl:Kse,dtype:"bool"}),tue={kernelName:vi,backendName:"webgpu",kernelFunc:eue},aue=Jt({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Xse}),nue={kernelName:ki,backendName:"webgpu",kernelFunc:aue};function rue(e){let{inputs:t,backend:a}=e,{input:n}=t;return V8(n,!0,a)}var sue={kernelName:Pd,backendName:"webgpu",kernelFunc:rue},iue=et({opType:le.IS_FINITE,dtype:"bool"}),oue={kernelName:Ol,backendName:"webgpu",kernelFunc:iue},lue=et({opType:le.IS_INF,dtype:"bool"}),uue={kernelName:Dl,backendName:"webgpu",kernelFunc:lue},due=et({opType:le.IS_NAN,dtype:"bool"}),pue={kernelName:Ii,backendName:"webgpu",kernelFunc:due};function cue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new yu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var hue={kernelName:Si,backendName:"webgpu",kernelFunc:cue},fue=Jt({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Yse}),mue={kernelName:Ti,backendName:"webgpu",kernelFunc:fue},gue=Jt({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Zse}),xue={kernelName:Ci,backendName:"webgpu",kernelFunc:gue},yue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + ${ke("index")} { + if (index < uniforms.size) { + setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); + } + } + `}};function Aue(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new yue(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var bue={kernelName:Od,backendName:"webgpu",kernelFunc:Aue},vue=et({opType:le.LOG,cpuKernelImpl:Jse}),kue={kernelName:Ni,backendName:"webgpu",kernelFunc:vue},wue=et({opType:le.LOG1P}),Iue={kernelName:zl,backendName:"webgpu",kernelFunc:wue},Sue=Jt({opType:Pe.LOGICAL_AND,dtype:"bool"}),Tue={kernelName:Ei,backendName:"webgpu",kernelFunc:Sue},Cue=et({opType:le.LOGICAL_NOT}),Nue={kernelName:Ri,backendName:"webgpu",kernelFunc:Cue},Eue=Jt({opType:Pe.LOGICAL_OR}),Rue={kernelName:Mi,backendName:"webgpu",kernelFunc:Eue},G8=` + var powValue = 0.0; + let basis = uniforms.bias + uniforms.alpha * sum; + if (uniforms.beta == 0.5) { + powValue = inverseSqrt(basis); + } else if (uniforms.beta == 1.0) { + powValue = 1.0 / basis; + } else { + powValue = exp(log(basis) * (-uniforms.beta)); + } +`,Mue=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` + ${ke("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let b = coords[0]; + let r = coords[1]; + let c = coords[2]; + let d = coords[3]; + + let x = getX(b, r, c, d); + var sum = 0.0; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let idx = d + i; + if (idx >= 0 && idx < uniforms.xShape[3]) { + let z = getX(b, r, c, idx); + sum = sum + z * z; + } + } + ${G8} + + setOutputAtIndex(index, x * powValue); + } + } + `}},$ue=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,v.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + var lrnSub: array; + const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; + const maxAllowRadius = ${this.maxAllowRadius}; + + ${ke()} { + let localDepth = i32(localId.x); + let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; + let xDepth = workgroupDepth + localDepth - maxAllowRadius; + let b = i32(globalId.z) / uniforms.xShape[1]; + let r = i32(globalId.z) - b * uniforms.xShape[1]; + let c = i32(globalId.y); + let d = workgroupDepth + localDepth; + + var x = 0.0; + if (xDepth >= 0 && xDepth < uniforms.xShape[3]) { + x = getX(b, r, c, xDepth); + } + lrnSub[localDepth] = x; + workgroupBarrier(); + + if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) { + var sum = 0.0; + let index = localDepth + maxAllowRadius; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let z = lrnSub[index + i]; + sum = sum + z * z; + } + ${G8} + + setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); + } + } `}};function _ue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new Mue(r.shape):u=new $ue(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Pue={kernelName:Dd,backendName:"webgpu",kernelFunc:_ue},Fue=Jt({opType:Pe.MAX,cpuKernelImpl:eie}),Oue={kernelName:_i,backendName:"webgpu",kernelFunc:Fue};function Due(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return $8(r,p,"max",a)}var zue={kernelName:Pi,backendName:"webgpu",kernelFunc:Due};function Lue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"min",a)}var Bue={kernelName:Oi,backendName:"webgpu",kernelFunc:Lue},Wue=Jt({opType:Pe.MIN,cpuKernelImpl:tie}),Vue={kernelName:Di,backendName:"webgpu",kernelFunc:Wue},Uue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=ia(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${ke("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${a}); @@ -6805,16 +6983,16 @@ return a / b;`,GK=` setOutputAtIndex(index, getX(${o})); } } - `}},gue={kernelName:Di,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new mue(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},xue=ua({opType:De.MOD}),Aue={kernelName:Wl,backendName:"webgpu",kernelFunc:xue};function yue(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=Zse(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new mp(n.shape,de.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var bue={kernelName:Vl,backendName:"webgpu",kernelFunc:yue};function vue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Tn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var wue={kernelName:Bi,backendName:"webgpu",kernelFunc:vue};function kue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:x}=Tn.nonMaxSuppressionV5Impl(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var Iue={kernelName:Wi,backendName:"webgpu",kernelFunc:kue},Sue=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` - ${Ce("index")} { + `}},Gue={kernelName:zi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Uue(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},Hue=Jt({opType:Pe.MOD}),jue={kernelName:Ll,backendName:"webgpu",kernelFunc:Hue};function que(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=nie(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new yu(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var Xue={kernelName:Bl,backendName:"webgpu",kernelFunc:que};function Kue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Tn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var Zue={kernelName:Wi,backendName:"webgpu",kernelFunc:Kue};function Yue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:x}=Tn.nonMaxSuppressionV5Impl(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var Jue={kernelName:Vi,backendName:"webgpu",kernelFunc:Yue},Que=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + ${ke("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function Tue(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new Sue(u,i),c=Ie({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=Ie({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var Cue={kernelName:Vi,backendName:"webgpu",kernelFunc:Tue};function Vc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=gp({inputs:{input:n},backend:a}),s=Vc({inputs:{x:r},backend:a}),i=jh({inputs:{input:n},backend:a}),o=Vc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Cr({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var Nue={kernelName:nu,backendName:"webgpu",kernelFunc:Vc};function X8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=gp({inputs:{input:n},backend:a}),s=X8({inputs:{x:r},backend:a}),i=jh({inputs:{input:n},backend:a}),o=Vc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Cr({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Eue={kernelName:Gl,backendName:"webgpu",kernelFunc:X8};function Rue(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return D1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=D1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=L8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Mue={kernelName:Hl,backendName:"webgpu",kernelFunc:Rue},$ue=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=ra(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${Ce("index")} { + `}};function ede(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new Que(u,i),c=Ie({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=Ie({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var tde={kernelName:Ui,backendName:"webgpu",kernelFunc:ede};function Hc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=Hc({inputs:{x:r},backend:a}),i=Vh({inputs:{input:n},backend:a}),o=Hc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Cr({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var ade={kernelName:nu,backendName:"webgpu",kernelFunc:Hc};function H8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=H8({inputs:{x:r},backend:a}),i=Vh({inputs:{input:n},backend:a}),o=Hc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Cr({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var nde={kernelName:Vl,backendName:"webgpu",kernelFunc:H8};function rde(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return P2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=P2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=F8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var sde={kernelName:Ul,backendName:"webgpu",kernelFunc:rde},ide=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=ia(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${ke("index")} { if (index < uniforms.size) { let start = ${r}; let end = ${s}; @@ -6828,8 +7006,8 @@ return a / b;`,GK=` } } } - `}},K8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Ya({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Cr({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new $ue(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},_ue={kernelName:Ui,backendName:"webgpu",kernelFunc:K8},Pue=ua({opType:De.POW}),Fue={kernelName:Gi,backendName:"webgpu",kernelFunc:Pue};function Oue(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new P1(De.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Due={kernelName:Hi,backendName:"webgpu",kernelFunc:Oue};function zue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ko(r,s,i,"prod",a)}var Lue={kernelName:ji,backendName:"webgpu",kernelFunc:zue},Bue=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=Qse(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Wue={kernelName:jl,backendName:"webgpu",kernelFunc:Bue},Z8=ua({opType:De.DIV}),Vue={kernelName:ci,backendName:"webgpu",kernelFunc:Z8},Uue=it({opType:de.RECIPROCAL}),Gue={kernelName:qi,backendName:"webgpu",kernelFunc:Uue},Hue=it({opType:de.RELU}),jue={kernelName:Xi,backendName:"webgpu",kernelFunc:Hue},que=it({opType:de.RELU6}),Xue={kernelName:Yi,backendName:"webgpu",kernelFunc:que},Kue=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` - ${Ce("index")} { + `}},j8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Ya({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Cr({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new ide(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},ode={kernelName:Gi,backendName:"webgpu",kernelFunc:j8},lde=Jt({opType:Pe.POW}),ude={kernelName:Hi,backendName:"webgpu",kernelFunc:lde};function dde(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new M2(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var pde={kernelName:ji,backendName:"webgpu",kernelFunc:dde};function cde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"prod",a)}var hde={kernelName:qi,backendName:"webgpu",kernelFunc:cde},fde=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=iie(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},mde={kernelName:Gl,backendName:"webgpu",kernelFunc:fde},q8=Jt({opType:Pe.DIV}),gde={kernelName:ci,backendName:"webgpu",kernelFunc:q8},xde=et({opType:le.RECIPROCAL}),yde={kernelName:Xi,backendName:"webgpu",kernelFunc:xde},Ade=et({opType:le.RELU}),bde={kernelName:Ki,backendName:"webgpu",kernelFunc:Ade},vde=et({opType:le.RELU6}),kde={kernelName:Ji,backendName:"webgpu",kernelFunc:vde},wde=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6871,8 +7049,8 @@ return a / b;`,GK=` setOutputAtIndex(index, newValue); } } - `}};function Zue(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new Kue(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var Yue={kernelName:Zi,backendName:"webgpu",kernelFunc:Zue},Jue=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` - ${Ce("index")} { + `}};function Ide(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new wde(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var Sde={kernelName:Yi,backendName:"webgpu",kernelFunc:Ide},Tde=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6902,7 +7080,7 @@ return a / b;`,GK=` setOutputAtIndex(index, newValue); } } - `}};function Que(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new Jue(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var ede={kernelName:Ki,backendName:"webgpu",kernelFunc:Que},tde=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function Cde(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new Tde(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var Nde={kernelName:Zi,backendName:"webgpu",kernelFunc:Cde},Ede=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -6924,7 +7102,7 @@ return a / b;`,GK=` return reverseCoords; } - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -6932,9 +7110,9 @@ return a / b;`,GK=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function ade(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return Ya({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,x)=>{let A=x+4-i;l[A]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let x=g+4-i;p[x]=1});let c=[{type:"int32",data:p}],d=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new tde(l),f=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let m=Ie({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),m}var nde={kernelName:Ji,backendName:"webgpu",kernelFunc:ade},rde=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function Rde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return Ya({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,x)=>{let y=x+4-i;l[y]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let x=g+4-i;p[x]=1});let c=[{type:"int32",data:p}],d=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Ede(l),f=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let m=Ie({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),m}var Mde={kernelName:Qi,backendName:"webgpu",kernelFunc:Rde},$de=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -6953,7 +7131,7 @@ return a / b;`,GK=` setOutputAtIndex(index, outputValue); } } - `}},sde={kernelName:mo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new rde(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},ide=it({opType:de.RSQRT,cpuKernelImpl:eie}),ode={kernelName:Qi,backendName:"webgpu",kernelFunc:ide},vc=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=Ve(e),this.dispatch=Ne(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=ra(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` + `}},_de={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new $de(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Pde=et({opType:le.ROUND}),Fde={kernelName:eo,backendName:"webgpu",kernelFunc:Pde},Ode=et({opType:le.RSQRT,cpuKernelImpl:oie}),Dde={kernelName:to,backendName:"webgpu",kernelFunc:Ode},Sc=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=$e(e),this.dispatch=we(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=ia(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -6968,25 +7146,9 @@ return a / b;`,GK=` let d1 = index - d0 * sliceSize; return vec2(d0, d1); } - `);let s=`getUpdates(${Array.from({length:this.updatesRank},(o,l)=>`coords[${l}]`).join(", ")})`,i=(o,l)=>{let u=`atomicAdd(${o}, bitcast(${l}))`;this.type==="float32"&&(u=` - { - var oldBits = 0; - var newBits = bitcast(${l}); - loop { - let info = atomicCompareExchangeWeak(${o}, oldBits, newBits); - if (info.exchanged) { - break; - } - oldBits = info.old_value; - let oldValue = bitcast(oldBits); - let newValue = oldValue + (${l}); - newBits = bitcast(newValue); - } - } - `);let p=`atomicStore(${o}, bitcast(${l}));`;return this.sumDupeIndices?u:p};return` + `);let s=`getUpdates(${Array.from({length:this.updatesRank},(i,o)=>`coords[${o}]`).join(", ")})`;return` ${r} - - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; @@ -6998,9 +7160,9 @@ return a / b;`,GK=` ${ad(this.type,!1)}(${s}); let flatIndex = getOutputIndexFromCoords(${n}); - ${i("&result[flatIndex]","updateValue")}; + ${this.sumDupeIndices?O3("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function lde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Ie({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Ie({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Cr({backend:a,attrs:{shape:d,value:0,dtype:m}}),x=v.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[x]}],y=new vc(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(y,[f,h],m,A,g),w=Ie({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),w}var ude={kernelName:eo,backendName:"webgpu",kernelFunc:lde},dde=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function zde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Ie({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Ie({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Cr({backend:a,attrs:{shape:d,value:0,dtype:m}}),x=v.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[x]}],A=new Sc(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(A,[f,h],m,y,g),k=Ie({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),k}var Lde={kernelName:ao,backendName:"webgpu",kernelFunc:zde},Bde=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -7015,15 +7177,15 @@ return a / b;`,GK=` return right; } - ${Ce("index")} { + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function pde(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new dde([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var cde={kernelName:$d,backendName:"webgpu",kernelFunc:pde},hde=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xa.disposeData(x.dataId)),g},Tde={kernelName:Jl,backendName:"webgpu",kernelFunc:Sde},Cde=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,y)=>x*y),l=[[0,0]];l.push(...i);for(let x=1+s.length;xa.disposeData(x.dataId)),g},lpe={kernelName:Jl,backendName:"webgpu",kernelFunc:ope},upe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=Me(r.shape,r.dtype,l),p=oie(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Cde(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var Ede={kernelName:ns,backendName:"webgpu",kernelFunc:J8};function Rde(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let E=a.bufferSync(r),_=a.bufferSync(s),$=v.decodeString(a.readSync(i.dataId)[0]),M=tie(E,_,o,d,p,u,l,c,$,h);return a.makeTensorInfo(o,M.dtype,M.values)}let f=[d/p,p],m=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Ie({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):Ya({inputs:{x:s},backend:a}),x=g.dtype,A=a.makeTensorInfo([],x,v.makeZerosTypedArray(1,x)),y=Ie({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=J8({inputs:{x:y},backend:a,attrs:{reps:f}}),w=v.sizeFromShape([u,p]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let E=new vc([u,p],l,m.shape.length,g.shape.length,c,f,x,h);a.runWebGPUProgram(E,[g,m],x,S,b)}break;default:{let E=new vc([u,p],l,m.shape.length,A.shape.length,c,f,x,h);a.runWebGPUProgram(E,[A,m],x,S,b)}{let E=new vc([u,p],l,m.shape.length,g.shape.length,c,f,x);a.runWebGPUProgram(E,[g,m],x,S,b)}}let C=Ie({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(y.dataId),a.disposeData(A.dataId),a.disposeData(b.dataId),C}var Mde={kernelName:Ld,backendName:"webgpu",kernelFunc:Rde};function $de(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=Au({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var _de={kernelName:Ql,backendName:"webgpu",kernelFunc:$de},Pde=it({opType:de.SQRT}),Fde={kernelName:no,backendName:"webgpu",kernelFunc:Pde},Ode={kernelName:Bd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new mp(a.shape,de.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},Dde=ua({opType:De.SQUARED_DIFFERENCE}),zde={kernelName:io,backendName:"webgpu",kernelFunc:Dde},Lde=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ra(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` - ${Ce("index")} { + `}};function dpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=fie(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new upe(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var ppe={kernelName:rs,backendName:"webgpu",kernelFunc:K8};function cpe(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let E=a.bufferSync(r),_=a.bufferSync(s),$=v.decodeString(a.readSync(i.dataId)[0]),M=lie(E,_,o,d,p,u,l,c,$,h);return a.makeTensorInfo(o,M.dtype,M.values)}let f=[d/p,p],m=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Ie({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):Ya({inputs:{x:s},backend:a}),x=g.dtype,y=a.makeTensorInfo([],x,v.makeZerosTypedArray(1,x)),A=Ie({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=K8({inputs:{x:A},backend:a,attrs:{reps:f}}),k=v.sizeFromShape([u,p]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let E=new Sc([u,p],l,m.shape.length,g.shape.length,c,f,x,h);a.runWebGPUProgram(E,[g,m],x,S,b)}break;default:{let E=new Sc([u,p],l,m.shape.length,y.shape.length,c,f,x,h);a.runWebGPUProgram(E,[y,m],x,S,b)}{let E=new Sc([u,p],l,m.shape.length,g.shape.length,c,f,x);a.runWebGPUProgram(E,[g,m],x,S,b)}}let C=Ie({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(y.dataId),a.disposeData(b.dataId),C}var hpe={kernelName:Ud,backendName:"webgpu",kernelFunc:cpe};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=Au({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var mpe={kernelName:Ql,backendName:"webgpu",kernelFunc:fpe},gpe=et({opType:le.SQRT}),xpe={kernelName:so,backendName:"webgpu",kernelFunc:gpe},ype={kernelName:Gd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new yu(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},Ape=Jt({opType:Pe.SQUARED_DIFFERENCE}),bpe={kernelName:lo,backendName:"webgpu",kernelFunc:Ape};function vpe({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new yu(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var kpe={kernelName:ss,backendName:"webgpu",kernelFunc:vpe},wpe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ia(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` + ${ke("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function Bde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:x,begin:A,end:y,strides:b}=It.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(m)w=Ie({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||x){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=It.computeOutShape(A,y,b),C=Au({inputs:{x:r},backend:a,attrs:{begin:A,size:S}});w=Ie({inputs:{x:C},backend:a,attrs:{shape:f}}),a.disposeData(C.dataId)}else if(a.shouldExecuteOnCPU([r])){let S=a.readSync(r.dataId),C=Me(r.shape,r.dtype,S),E=rie(h,C,b,A);w=a.makeTensorInfo(f,r.dtype,E.values)}else{let S=new Lde(h),C=[{type:"int32",data:A},{type:"int32",data:b}],E=a.runWebGPUProgram(S,[r],r.dtype,C);w=Ie({inputs:{x:E},backend:a,attrs:{shape:f}}),a.disposeData(E.dataId)}return w}var Wde={kernelName:oo,backendName:"webgpu",kernelFunc:Bde};function Vde(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=sie(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var Ude={kernelName:tu,backendName:"webgpu",kernelFunc:Vde},Gde=it({opType:de.TAN}),Hde={kernelName:uo,backendName:"webgpu",kernelFunc:Gde},jde=it({opType:de.TANH}),qde={kernelName:po,backendName:"webgpu",kernelFunc:jde},Xde=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function Ipe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:x,begin:y,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=Ie({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||x){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=St.computeOutShape(y,A,b),C=Au({inputs:{x:r},backend:a,attrs:{begin:y,size:S}});k=Ie({inputs:{x:C},backend:a,attrs:{shape:f}}),a.disposeData(C.dataId)}else if(a.shouldExecuteOnCPU([r])){let S=a.readSync(r.dataId),C=_e(r.shape,r.dtype,S),E=pie(h,C,b,y);k=a.makeTensorInfo(f,r.dtype,E.values)}else{let S=new wpe(h),C=[{type:"int32",data:y},{type:"int32",data:b}],E=a.runWebGPUProgram(S,[r],r.dtype,C);k=Ie({inputs:{x:E},backend:a,attrs:{shape:f}}),a.disposeData(E.dataId)}return k}var Spe={kernelName:uo,backendName:"webgpu",kernelFunc:Ipe};function 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getCoordsFromIndex(index); let batch = outC[0]; @@ -7116,8 +7278,8 @@ return a / b;`,GK=` } } } - `}},Kde=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` - ${Ce("index")} { + `}},_pe=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` + ${ke("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -7175,7 +7337,7 @@ return a / b;`,GK=` } } } - `}};function Jo(e,t){t!==null&&e.disposeData(t.dataId)}function eA(e){let t=1;for(;th===null?[p,p]:[p,h],m=(b,w,S)=>{let C=f(),E=new Xde(S),_=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[w]}],$=h;h=a.runWebGPUProgram(E,C,"int32",_),Jo(a,$)};for(let b=1;b=1;S/=2)m(w,S,[u,d])}for(let b=d;b>c;b/=2){let w=f(),S=new Kde([u,b/2]),C=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"int32",data:[c]}],E=h;h=a.runWebGPUProgram(S,w,"int32",C),Jo(a,E);let _=c/2,$=_*2;for(let M=_;M>=1;M/=2)m($,M,h.shape)}let g=h;h=Au({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),Jo(a,g);let x=q8({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});Jo(a,p);let A=o.slice(0,-1);A.push(s),g=h,h=Ie({inputs:{x:h},attrs:{shape:A},backend:a}),Jo(a,g);let y=x;return x=Ie({inputs:{x},attrs:{shape:A},backend:a}),Jo(a,y),[x,h]}var Yde={kernelName:co,backendName:"webgpu",kernelFunc:Zde},Jde=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=Ne(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` + `}};function Yo(e,t){t!==null&&e.disposeData(t.dataId)}function Zx(e){let t=1;for(;th===null?[p,p]:[p,h],m=(b,k,S)=>{let C=f(),E=new $pe(S),_=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[k]}],$=h;h=a.runWebGPUProgram(E,C,"int32",_),Yo(a,$)};for(let b=1;b=1;S/=2)m(k,S,[u,d])}for(let b=d;b>c;b/=2){let k=f(),S=new _pe([u,b/2]),C=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"int32",data:[c]}],E=h;h=a.runWebGPUProgram(S,k,"int32",C),Yo(a,E);let _=c/2,$=_*2;for(let M=_;M>=1;M/=2)m($,M,h.shape)}let g=h;h=Au({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),Yo(a,g);let x=U8({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});Yo(a,p);let y=o.slice(0,-1);y.push(s),g=h,h=Ie({inputs:{x:h},attrs:{shape:y},backend:a}),Yo(a,g);let A=x;return x=Ie({inputs:{x},attrs:{shape:y},backend:a}),Yo(a,A),[x,h]}var Fpe={kernelName:fo,backendName:"webgpu",kernelFunc:Ppe},Ope=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -7239,7 +7401,7 @@ return a / b;`,GK=` return outputValue; } - ${Ce("index")} { + ${ke("index")} { 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a=lt.faceLabels.slice();if(e.score&&(a=ct(a,"[score]",100*e.score)),e.gender&&(a=ct(a,"[gender]",e.gender)),e.genderScore&&(a=ct(a,"[genderScore]",100*e.genderScore)),e.age&&(a=ct(a,"[age]",e.age)),e.distance&&(a=ct(a,"[distance]",100*e.distance)),e.real&&(a=ct(a,"[real]",100*e.real)),e.live&&(a=ct(a,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let d=e.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),a=ct(a,"[emotions]",d.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(a=ct(a,"[roll]",To(e.rotation.angle.roll))),(o=(i=e.rotation)==null?void 0:i.angle)!=null&&o.yaw&&(a=ct(a,"[yaw]",To(e.rotation.angle.yaw))),(u=(l=e.rotation)==null?void 0:l.angle)!=null&&u.pitch&&(a=ct(a,"[pitch]",To(e.rotation.angle.pitch))),(c=(p=e.rotation)==null?void 0:p.gaze)!=null&&c.bearing&&(a=ct(a,"[gaze]",To(e.rotation.gaze.bearing))),En(t,a,e.box[0],e.box[1],lt)}function Nhe(e,t){var a,n,r,s;if(((a=e.annotations)==null?void 0:a.leftEyeIris)&&((n=e.annotations)==null?void 0:n.leftEyeIris[0])){t.strokeStyle=lt.useDepth?"rgba(255, 200, 255, 0.3)":lt.color,t.beginPath();let i=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,o=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),lt.fillPolygons&&(t.fillStyle=lt.useDepth?"rgba(255, 255, 200, 0.3)":lt.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=lt.useDepth?"rgba(255, 200, 255, 0.3)":lt.color,t.beginPath();let i=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,o=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),lt.fillPolygons&&(t.fillStyle=lt.useDepth?"rgba(255, 255, 200, 0.3)":lt.color,t.fill())}}function Ehe(e,t){var a;if(lt.drawGaze&&((a=e.rotation)==null?void 0:a.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*To(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*To(e.rotation.angle.pitch)/90,s=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C ${n} ${e.box[1]}, @@ -7396,7 +7558,7 @@ return a / b;`,GK=` ${e.box[0]} ${r}, ${e.box[0]+e.box[2]} ${r}, ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(i),t.stroke(s)}}function jce(e,t){var a;if(lt.drawGaze&&((a=e.rotation)==null?void 0:a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Z3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Z3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function qce(e,t){if(lt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);K3(t,n,lt)}Gce(e,t)}}function Xce(e,t){if(lt.drawPoints&&e.mesh.length>=468)for(let a=0;a0&&(Xce(r,n),qce(r,n),Hce(r,n),jce(r,n))}}function t0(e,t,a){var s,i;let n=Ct($t,a);if(!t||!e)return;let r=xn(e);if(!!r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ct(l,"[score]",100*t[o].score),En(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ct(u,"[label]",l.part),u=ct(u,"[score]",100*l.score),En(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)f9(r,u,n)}}}function a0(e,t,a){var s,i;let n=Ct($t,a);if(!t||!e)return;let r=xn(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,sr(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ct(l,"[label]",o.label),l=ct(l,"[score]",100*o.score),En(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=To(l[2],n),Nr(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ct(p,"[label]",l),En(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function n0(e,t,a){var s;let n=Ct($t,a);if(!t||!e)return;let r=xn(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,sr(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ct(o,"[label]",i.label),o=ct(o,"[score]",100*i.score),En(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function r0(e,t,a){var r;let n=Ct($t,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=xn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ct(c,"[where]",l[0]),c=ct(c,"[who]",p),c=ct(c,"[what]",u[1]),En(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var cs={face:`face + `);t.stroke(i),t.stroke(s)}}function Rhe(e,t){var a;if(lt.drawGaze&&((a=e.rotation)==null?void 0:a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let 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ne.initial&&(Mn=null),Mn?e.debug&&K("cached model:",Mn.modelUrl):Mn=await Ee((t=e.face.gear)==null?void 0:t.modelPath),Mn}async function $g(e,t,a,n){var i,o;if(!Mn)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Mg<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>te()-vw;return t.skipAllowed&&s&&r&&bw===n&&Rg[a]?(Mg++,Rg[a]):(Mg=0,new Promise(async l=>{var x,A;if(!(Mn!=null&&Mn.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];u.resize=ge.cropAndResize(e,p,[0],[Mn.inputs[0].shape[2],Mn.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(x=t.face.gear)!=null&&x.enabled&&([u.age,u.gender,u.race]=Mn.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let y=0;y(((A=t.face.gear)==null?void 0:A.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[y])/100,race:Ihe[y]});c.race.sort((y,b)=>b.score-y.score);let m=Array.from(await u.age.data()).map((y,b)=>[She[b],y]).sort((y,b)=>b[1]-y[1]),g=m[0][0];for(let y=1;yY(u[y])),Rg[a]=c,bw=n,vw=te(),l(c)}))}var en,v0=[],Iw=0,Sw=0,_g=Number.MAX_SAFE_INTEGER;async function Tw(e){return ne.initial&&(en=null),en?e.debug&&K("cached model:",en.modelUrl):en=await Ee(e.face.ssrnet.modelPathAge),en}async function Pg(e,t,a,n){var i,o,l,u;if(!en)return{age:0};let r=_g<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>te()-Sw;return t.skipAllowed&&r&&s&&Iw===n&&((l=v0[a])==null?void 0:l.age)&&((u=v0[a])==null?void 0:u.age)>0?(_g++,v0[a]):(_g=0,new Promise(async p=>{var h;if(!(en!=null&&en.inputs)||!en.inputs[0]||!en.inputs[0].shape)return;let c={};c.resize=ge.resizeBilinear(e,[en.inputs[0].shape[2],en.inputs[0].shape[1]],!1),c.enhance=ae(c.resize,ze.tf255);let d={age:0};if((h=t.face.ssrnet)!=null&&h.enabled&&(c.age=en.execute(c.enhance)),c.age){let f=await c.age.data();d.age=Math.trunc(10*f[0])/10}Object.keys(c).forEach(f=>Y(c[f])),v0[a]=d,Iw=n,Sw=te(),p(d)}))}var $n,w0=[],Nw=0,Ew=0,Fg=Number.MAX_SAFE_INTEGER,Og=[.2989,.587,.114];async function Rw(e){var t;return ne.initial&&($n=null),$n?e.debug&&K("cached model:",$n.modelUrl):$n=await Ee((t=e.face.ssrnet)==null?void 0:t.modelPathGender),$n}async function Dg(e,t,a,n){var i,o,l,u;if(!$n)return{gender:"unknown",genderScore:0};let r=Fg<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>te()-Ew;return t.skipAllowed&&r&&s&&Nw===n&&((l=w0[a])==null?void 0:l.gender)&&((u=w0[a])==null?void 0:u.genderScore)>0?(Fg++,w0[a]):(Fg=0,new Promise(async p=>{var f;if(!($n!=null&&$n.inputs[0].shape))return;let c={};c.resize=ge.resizeBilinear(e,[$n.inputs[0].shape[2],$n.inputs[0].shape[1]],!1),c.enhance=$e(()=>{let[m,g,x]=ka(c.resize,3,3),A=ae(m,Og[0]),y=ae(g,Og[1]),b=ae(x,Og[2]),w=ch([A,y,b]);return ae(fe(w,ze.tf05),2)});let d={gender:"unknown",genderScore:0};(f=t.face.ssrnet)!=null&&f.enabled&&(c.gender=$n.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(m=>Y(c[m])),w0[a]=d,Nw=n,Ew=te(),p(d)}))}var tn,zg=[],$w=0,_w=0,Pw=Number.MAX_SAFE_INTEGER;async function Fw(e){var t;return ne.initial&&(tn=null),tn?e.debug&&K("cached model:",tn.modelUrl):tn=await Ee((t=e.face.mobilefacenet)==null?void 0:t.modelPath),tn}async function Lg(e,t,a,n){var i,o;if(!(tn!=null&&tn.executor))return[];let r=Pw<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>te()-_w;return t.skipAllowed&&s&&r&&$w===n&&zg[a]?(Pw++,zg[a]):new Promise(async l=>{var p;let u=[];if(((p=t.face.mobilefacenet)==null?void 0:p.enabled)&&(tn==null?void 0:tn.inputs[0].shape)){let c={};c.crop=ge.resizeBilinear(e,[tn.inputs[0].shape[2],tn.inputs[0].shape[1]],!1),c.data=tn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>Y(c[h]))}zg[a]=u,$w=n,_w=te(),l(u)})}var an,Bg=[],Dw=0,zw=0,Lw=Number.MAX_SAFE_INTEGER;async function Bw(e){return ne.initial&&(an=null),an?e.debug&&K("cached model:",an.modelUrl):an=await Ee(e.face.insightface.modelPath),an}async function Wg(e,t,a,n){var i,o;if(!(an!=null&&an.executor))return[];let r=Lw<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>te()-zw;return t.skipAllowed&&s&&r&&Dw===n&&Bg[a]?(Lw++,Bg[a]):new Promise(async l=>{var p;let u=[];if(((p=t.face.insightface)==null?void 0:p.enabled)&&(an==null?void 0:an.inputs[0].shape)){let c={};c.crop=ge.resizeBilinear(e,[an.inputs[0].shape[2],an.inputs[0].shape[1]],!1),c.data=an.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>Y(c[h]))}Bg[a]=u,Dw=n,zw=te(),l(u)})}var The=e=>{let t=(c,d)=>Math.atan2(c[1]-d[1],c[0]-d[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let a=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-a[0],n*(s[1]-i[1])/o[1]-a[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Vw=(e,t)=>{let a=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return 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Ma={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Ma.nameMapping[e],getPoints:e=>Ma.pointsMapping[e]},As={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>As.nameMapping[e]},Et={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Et.nameMapping[e]},xs=class{constructor(t){ue(this,"name");ue(this,"curls");ue(this,"directions");ue(this,"weights");ue(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,a,n){typeof 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e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function ik(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ohe(a)}var ak=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ws(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Kn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Ht([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Ht([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=Pe(t,[0,0],[-1,2]),a.boxSizes=Pe(t,[0,2],[-1,2]),a.div=me(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=be(a.div,this.anchorsTensor),a.halfBoxSizes=me(a.boxSizes,this.doubleInputSizeTensor),a.sub=fe(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=ae(a.sub,this.inputSizeTensor),a.add=be(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=ae(a.add,this.inputSizeTensor);let n=ru([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>Y(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=J(t,[-1,7,2]),n.div=me(n.reshape,this.inputSizeTensor),n.landmarks=be(n.div,this.anchors[a]?this.anchors[a]:0);let r=ae(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>Y(n[s])),r}async predict(t,a){var o;let n={};n.resize=ge.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=me(n.resize,ze.tf127),n.image=fe(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=_e(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Da(n.slice),n.scores=_e(n.sigmoid);let r=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await ge.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Pe(n.norm,[l,0],[1,-1]),u.slice=Pe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=J(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=sk(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>Y(u[g]))}return Object.keys(n).forEach(l=>Y(n[l])),i}};var Bhe=5,dk=1.65,pk=[0,5,9,13,17,1,2],Whe=0,Vhe=2,ck=0,N0=class{constructor(t,a){ue(this,"handDetector");ue(this,"handPoseModel");ue(this,"inputSize");ue(this,"storedBoxes");ue(this,"skipped");ue(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>jg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return S0(T0(r),Bhe)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=S0(T0(a),dk);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Hg(n,[0,0]),u=o.map(h=>[...jg(h,l),h[2]]),p=ok(r),c=[...Tp(a),1],d=[ws(c,p[0]),ws(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>te()-ck,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=J(y,[-1,3]),S=await w.array();Y(y),Y(w);let C=this.transformRawCoords(S,m,p,f),E=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...E,confidence:b};let _={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(_)}else this.storedBoxes[l]=null;Y(y)}else{let p=S0(T0(u),dk),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var hk={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Do,zo,fk;async function qg(e,t){let a=await fk.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=k0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function mk(e){var a,n;ne.initial&&(Do=null,zo=null),!Do||!zo?[Do,zo]=await Promise.all([e.hand.enabled?Ee((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Ee((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",Do.modelUrl),e.debug&&K("cached model:",zo.modelUrl));let t=Do?new C0(Do):void 0;return t&&zo&&(fk=new N0(t,zo)),[Do,zo]}var Pt=[null,null],Ghe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ks=[[0,0],[0,0]],Hhe=["hand","fist","pinch","point","face","tip","pinchtip"],xk=4,Ak=1.6,jhe=512,qhe=1.4,E0=Number.MAX_SAFE_INTEGER,Xg=0,$r=[0,0],_t={boxes:[],hands:[]},yk={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function bk(e){var t;if(ne.initial&&(Pt[0]=null),Pt[0])e.debug&&K("cached model:",Pt[0].modelUrl);else{Qh(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Pt[0]=await Ee((t=e.hand.detector)==null?void 0:t.modelPath);let a=Pt[0].executor?Object.values(Pt[0].modelSignature.inputs):void 0;ks[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,ks[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Pt[0]}async function vk(e){var t;if(ne.initial&&(Pt[1]=null),Pt[1])e.debug&&K("cached model:",Pt[1].modelUrl);else{Pt[1]=await Ee((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Pt[1].executor?Object.values(Pt[1].modelSignature.inputs):void 0;ks[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,ks[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Pt[1]}async function Xhe(e,t){let a=[];if(!e||!Pt[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,jhe),i=Math.round(s*r/8)*8;n.resize=ge.resizeBilinear(e,[s,i]),n.cast=He(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Pt[0].executeAsync(n.cast,Ghe),n.boxes=_e(n.rawBoxes,[0,2]),n.scores=_e(n.rawScores,[0]);let o=Ta(n.scores,1);Y(o[xk]),o.splice(xk,1),n.filtered=sa(o,1),Y(o),n.max=pa(n.filtered,1),n.argmax=ar(n.filtered,1);let l=0;n.nms=await ge.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Pe(n.boxes,d,1),f=await h.data();Y(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=l0(m,qhe),x=[Math.trunc(m[0]*$r[0]),Math.trunc(m[1]*$r[1]),Math.trunc(m[2]*$r[0]),Math.trunc(m[3]*$r[1])],A=p[d],y=Hhe[c[d]],b={id:l++,score:A,box:x,boxRaw:g,label:y};a.push(b)}return Object.keys(n).forEach(d=>Y(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Kg(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Pt[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=ge.cropAndResize(e,[s],[0],[ks[1][0],ks[1][1]],"bilinear"),r.div=me(r.crop,ze.tf255),[r.score,r.keypoints]=Pt[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=J(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/ks[1][1],c[1]/ks[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=k0(n.keypoints);for(let c of Object.keys(yk))n.annotations[c]=yk[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>Y(r[l]))}return n}async function Zg(e,t){var r,s;if(!((r=Pt[0])!=null&&r.executor)||!((s=Pt[1])!=null&&s.executor)||!Pt[0].inputs[0].shape||!Pt[1].inputs[0].shape)return[];$r=[e.shape[2]||0,e.shape[1]||0],E0++;let a=(t.hand.skipTime||0)>te()-Xg,n=E0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?_t.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>te()-Xg,l=E0<3*(t.hand.skipFrames||0);t.skipAllowed&&_t.hands.length===t.hand.maxDetected?_t.hands=await Promise.all(_t.boxes.map(p=>Kg(e,p,t))):t.skipAllowed&&o&&l&&_t.hands.length>0?_t.hands=await Promise.all(_t.boxes.map(p=>Kg(e,p,t))):(_t.boxes=await Xhe(e,t),Xg=te(),_t.hands=await Promise.all(_t.boxes.map(p=>Kg(e,p,t))),E0=0);let u=[..._t.boxes];if(_t.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p<_t.hands.length;p++){let c=b9(_t.hands[p].keypoints,$r);if(c.box[2]/(e.shape[2]||1)>.05&&c.box[3]/(e.shape[1]||1)>.05&&_t.hands[p].fingerScore&&_t.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=l0(c.box,Ak),h=l0(c.boxRaw,Ak);_t.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p<_t.hands.length;p++){let c=Er(_t.hands[p].keypoints,$r);_t.hands[p].box=c.box,_t.hands[p].boxRaw=c.boxRaw}i(_t.hands)})}var or=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var Cp={};hr(Cp,{connected:()=>M0,horizontal:()=>Yg,kpt:()=>R0,relative:()=>Qg,vertical:()=>Jg});var R0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Yg=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Jg=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Qg=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],M0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var xe=or(),e5=0;function kk(e,t){var i,o,l,u,p,c,d,h,f,m,g,x,A,y,b,w,S,C,E,_,$,M,I;let a=te();if(!e)return or();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(xe.canvas=e.canvas),e.error&&(xe.error=e.error),!xe.body||e.body.length!==xe.body.length)xe.body=JSON.parse(JSON.stringify(e.body));else for(let N=0;N((r-1)*xe.body[N].box[H]+U)/r),L=e.body[N].boxRaw.map((U,H)=>((r-1)*xe.body[N].boxRaw[H]+U)/r),B=e.body[N].keypoints.map((U,H)=>{var V,Q,Z,re,ee,he,oe,Ae,we;return{score:U.score,part:U.part,position:[xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].position[0]||0)+(U.position[0]||0))/r:U.position[0],xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].position[1]||0)+(U.position[1]||0))/r:U.position[1],xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].position[2]||0)+(U.position[2]||0))/r:U.position[2]],positionRaw:[xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].positionRaw[0]||0)+(U.positionRaw[0]||0))/r:U.positionRaw[0],xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].positionRaw[1]||0)+(U.positionRaw[1]||0))/r:U.positionRaw[1],xe.body[N].keypoints[H]?((r-1)*(xe.body[N].keypoints[H].positionRaw[2]||0)+(U.positionRaw[2]||0))/r:U.positionRaw[2]],distance:[xe.body[N].keypoints[H]?((r-1)*(((V=xe.body[N].keypoints[H].distance)==null?void 0:V[0])||0)+(((Q=U.distance)==null?void 0:Q[0])||0))/r:(Z=U.distance)==null?void 0:Z[0],xe.body[N].keypoints[H]?((r-1)*(((re=xe.body[N].keypoints[H].distance)==null?void 0:re[1])||0)+(((ee=U.distance)==null?void 0:ee[1])||0))/r:(he=U.distance)==null?void 0:he[1],xe.body[N].keypoints[H]?((r-1)*(((oe=xe.body[N].keypoints[H].distance)==null?void 0:oe[2])||0)+(((Ae=U.distance)==null?void 0:Ae[2])||0))/r:(we=U.distance)==null?void 0:we[2]]}}),G={},j={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?j=p0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?j=i0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(j=Cp);for(let[U,H]of Object.entries(j.connected)){let V=[];for(let Q=0;Qee.part===H[Q]),re=B.find(ee=>ee.part===H[Q+1]);Z&&re&&V.push([Z.position,re.position])}G[U]=V}xe.body[N]={...e.body[N],box:O,boxRaw:L,keypoints:B,annotations:G}}if(!xe.hand||e.hand.length!==xe.hand.length)xe.hand=JSON.parse(JSON.stringify(e.hand));else for(let N=0;N((r-1)*xe.hand[N].box[U]+j)/r),L=e.hand[N].boxRaw.map((j,U)=>((r-1)*xe.hand[N].boxRaw[U]+j)/r);xe.hand[N].keypoints.length!==e.hand[N].keypoints.length&&(xe.hand[N].keypoints=e.hand[N].keypoints);let B=e.hand[N].keypoints&&e.hand[N].keypoints.length>0?e.hand[N].keypoints.map((j,U)=>j.map((H,V)=>((r-1)*(xe.hand[N].keypoints[U][V]||1)+(H||0))/r)):[],G={};if(Object.keys(xe.hand[N].annotations).length!==Object.keys(e.hand[N].annotations).length)xe.hand[N].annotations=e.hand[N].annotations,G=xe.hand[N].annotations;else if(e.hand[N].annotations)for(let j of Object.keys(e.hand[N].annotations))G[j]=(c=(p=(u=e.hand[N])==null?void 0:u.annotations)==null?void 0:p[j])!=null&&c[0]?e.hand[N].annotations[j].map((U,H)=>U.map((V,Q)=>((r-1)*xe.hand[N].annotations[j][H][Q]+V)/r)):null;xe.hand[N]={...e.hand[N],box:O,boxRaw:L,keypoints:B,annotations:G}}if(!xe.face||e.face.length!==xe.face.length)xe.face=JSON.parse(JSON.stringify(e.face));else for(let N=0;N((r-1)*xe.face[N].box[G]+B)/r),L=e.face[N].boxRaw.map((B,G)=>((r-1)*xe.face[N].boxRaw[G]+B)/r);if(e.face[N].rotation){let B={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};B.matrix=(d=e.face[N].rotation)==null?void 0:d.matrix,B.angle={roll:((r-1)*(((f=(h=xe.face[N].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[N].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((A=(x=xe.face[N].rotation)==null?void 0:x.angle)==null?void 0:A.yaw)||0)+(((b=(y=e.face[N].rotation)==null?void 0:y.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((S=(w=xe.face[N].rotation)==null?void 0:w.angle)==null?void 0:S.pitch)||0)+(((E=(C=e.face[N].rotation)==null?void 0:C.angle)==null?void 0:E.pitch)||0))/r},B.gaze={bearing:((r-1)*(((_=xe.face[N].rotation)==null?void 0:_.gaze.bearing)||0)+((($=e.face[N].rotation)==null?void 0:$.gaze.bearing)||0))/r,strength:((r-1)*(((M=xe.face[N].rotation)==null?void 0:M.gaze.strength)||0)+(((I=e.face[N].rotation)==null?void 0:I.gaze.strength)||0))/r},xe.face[N]={...e.face[N],rotation:B,box:O,boxRaw:L}}else xe.face[N]={...e.face[N],box:O,boxRaw:L}}if(!xe.object||e.object.length!==xe.object.length)xe.object=JSON.parse(JSON.stringify(e.object));else for(let N=0;N((r-1)*xe.object[N].box[G]+B)/r),L=e.object[N].boxRaw.map((B,G)=>((r-1)*xe.object[N].boxRaw[G]+B)/r);xe.object[N]={...e.object[N],box:O,boxRaw:L}}if(e.persons){let N=e.persons;if(!xe.persons||N.length!==xe.persons.length)xe.persons=JSON.parse(JSON.stringify(N));else for(let O=0;O((r-1)*xe.persons[O].box[B]+L)/r)}e.gesture&&(xe.gesture=e.gesture),xe.width=e.width,xe.height=e.height;let s=te();return e5=ne.perfadd?e5+Math.round(s-a):Math.round(s-a),e.performance&&(xe.performance={...e.performance,interpolate:e5}),xe}var ma;async function t5(e){return!ma||ne.initial?ma=await Ee(e.segmentation.modelPath):e.debug&&K("cached model:",ma.modelUrl),ma}async function Ik(e,t){var r;if(ma||(ma=await 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t.skipAllowed&&s&&r&&Ek===n&&Lg[a]?(Mk++,Lg[a]):new Promise(async l=>{var p;let u=[];if(((p=t.face.mobilefacenet)==null?void 0:p.enabled)&&(tn==null?void 0:tn.inputs[0].shape)){let c={};c.crop=ge.resizeBilinear(e,[tn.inputs[0].shape[2],tn.inputs[0].shape[1]],!1),c.data=tn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>Y(c[h]))}Lg[a]=u,Ek=n,Rk=te(),l(u)})}var an,Wg=[],Pk=0,Fk=0,Ok=Number.MAX_SAFE_INTEGER;async function Dk(e){return ne.initial&&(an=null),an?e.debug&&X("cached model:",an.modelUrl):an=await Me(e.face.insightface.modelPath),an}async function Vg(e,t,a,n){var i,o;if(!(an!=null&&an.executor))return[];let r=Ok<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>te()-Fk;return t.skipAllowed&&s&&r&&Pk===n&&Wg[a]?(Ok++,Wg[a]):new Promise(async l=>{var p;let u=[];if(((p=t.face.insightface)==null?void 0:p.enabled)&&(an==null?void 0:an.inputs[0].shape)){let c={};c.crop=ge.resizeBilinear(e,[an.inputs[0].shape[2],an.inputs[0].shape[1]],!1),c.data=an.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>Y(c[h]))}Wg[a]=u,Pk=n,Fk=te(),l(u)})}var d0e=e=>{let t=(c,d)=>Math.atan2(c[1]-d[1],c[0]-d[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let a=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-a[0],n*(s[1]-i[1])/o[1]-a[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Lk=(e,t)=>{let a=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return 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this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([a,n])}direction(t,a,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([a,n])}weight(t,a){this.weights[t]=a;let n=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/n)}matchAgainst(t,a){let n=0;for(let r in t){let s=t[r],i=this.curls[r];if(typeof i=="undefined"){n+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[r];break}}for(let r in a){let s=a[r],i=this.directions[r];if(typeof i=="undefined"){n+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[r];break}}return n/10}};var{thumb:Bn,index:Rr,middle:Mr,ring:Fo,pinky:Oo}=Ma,{none:Wn,half:c0e,full:Vn}=As,{verticalUp:Nu,verticalDown:N5e,horizontalLeft:Gg,horizontalRight:h0e,diagonalUpRight:f0e,diagonalUpLeft:Eu,diagonalDownRight:E5e,diagonalDownLeft:R5e}=Rt,bs=new ys("thumbs 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x=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],y=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(y<.01||x<.01||y>.022||x>.022)&&(h=!1),(y<.01||x<.01)&&t.push({iris:i,gesture:"looking down"}),(y>.022||x>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},Jk=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function v0(e,t=1.5){let a=Np(e),n=b0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k0(e){let t=Np(e),a=b0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function b0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function nw(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return b0e(a)}var Qk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ws(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Kn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Ht([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Ht([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=De(t,[0,0],[-1,2]),a.boxSizes=De(t,[0,2],[-1,2]),a.div=me(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=be(a.div,this.anchorsTensor),a.halfBoxSizes=me(a.boxSizes,this.doubleInputSizeTensor),a.sub=he(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=ae(a.sub,this.inputSizeTensor),a.add=be(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=ae(a.add,this.inputSizeTensor);let n=ru([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>Y(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=J(t,[-1,7,2]),n.div=me(n.reshape,this.inputSizeTensor),n.landmarks=be(n.div,this.anchors[a]?this.anchors[a]:0);let r=ae(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>Y(n[s])),r}async predict(t,a){var o;let n={};n.resize=ge.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=me(n.resize,Le.tf127),n.image=he(n.div,Le.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=De(n.predictions,[0,0],[-1,1]),n.sigmoid=Da(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=De(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await ge.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=De(n.norm,[l,0],[1,-1]),u.slice=De(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=J(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=aw(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>Y(u[g]))}return Object.keys(n).forEach(l=>Y(n[l])),i}};var I0e=5,ow=1.65,lw=[0,5,9,13,17,1,2],S0e=0,T0e=2,uw=0,I0=class{constructor(t,a){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>qg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return v0(k0(r),I0e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=v0(k0(a),ow);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=jg(n,[0,0]),u=o.map(h=>[...qg(h,l),h[2]]),p=rw(r),c=[...Np(a),1],d=[ws(c,p[0]),ws(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>te()-uw,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let k=J(A,[-1,3]),S=await k.array();Y(A),Y(k);let C=this.transformRawCoords(S,m,p,f),E=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...E,confidence:b};let _={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(_)}else this.storedBoxes[l]=null;Y(A)}else{let p=v0(k0(u),ow),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var dw={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},zo,Lo,pw;async function Xg(e,t){let a=await pw.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else 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o=Ta(n.scores,1);Y(o[fw]),o.splice(fw,1),n.filtered=oa(o,1),Y(o),n.max=ca(n.filtered,1),n.argmax=ar(n.filtered,1);let l=0;n.nms=await ge.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=De(n.boxes,d,1),f=await h.data();Y(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=r0(m,M0e),x=[Math.trunc(m[0]*$r[0]),Math.trunc(m[1]*$r[1]),Math.trunc(m[2]*$r[0]),Math.trunc(m[3]*$r[1])],y=p[d],A=E0e[c[d]],b={id:l++,score:y,box:x,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>Y(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Zg(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ft[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=ge.cropAndResize(e,[s],[0],[Is[1][0],Is[1][1]],"bilinear"),r.div=me(r.crop,Le.tf255),[r.score,r.keypoints]=Ft[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=J(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Is[1][1],c[1]/Is[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=A0(n.keypoints);for(let c of 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p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Pt.hands[p].fingerScore&&Pt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=r0(c.box,mw),h=r0(c.boxRaw,mw);Pt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var Ep={};hr(Ep,{connected:()=>C0,horizontal:()=>Jg,kpt:()=>T0,relative:()=>e5,vertical:()=>Qg});var 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N=0;N((r-1)*xe.hand[N].box[U]+H)/r),L=e.hand[N].boxRaw.map((H,U)=>((r-1)*xe.hand[N].boxRaw[U]+H)/r);xe.hand[N].keypoints.length!==e.hand[N].keypoints.length&&(xe.hand[N].keypoints=e.hand[N].keypoints);let W=e.hand[N].keypoints&&e.hand[N].keypoints.length>0?e.hand[N].keypoints.map((H,U)=>H.map((j,V)=>((r-1)*(xe.hand[N].keypoints[U][V]||1)+(j||0))/r)):[],G={};if(Object.keys(xe.hand[N].annotations).length!==Object.keys(e.hand[N].annotations).length)xe.hand[N].annotations=e.hand[N].annotations,G=xe.hand[N].annotations;else if(e.hand[N].annotations)for(let H of Object.keys(e.hand[N].annotations))G[H]=(c=(p=(u=e.hand[N])==null?void 0:u.annotations)==null?void 0:p[H])!=null&&c[0]?e.hand[N].annotations[H].map((U,j)=>U.map((V,Q)=>((r-1)*xe.hand[N].annotations[H][j][Q]+V)/r)):null;xe.hand[N]={...e.hand[N],box:O,boxRaw:L,keypoints:W,annotations:G}}if(!xe.face||e.face.length!==xe.face.length)xe.face=JSON.parse(JSON.stringify(e.face));else for(let N=0;N((r-1)*xe.face[N].box[G]+W)/r),L=e.face[N].boxRaw.map((W,G)=>((r-1)*xe.face[N].boxRaw[G]+W)/r);if(e.face[N].rotation){let W={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};W.matrix=(d=e.face[N].rotation)==null?void 0:d.matrix,W.angle={roll:((r-1)*(((f=(h=xe.face[N].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[N].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((y=(x=xe.face[N].rotation)==null?void 0:x.angle)==null?void 0:y.yaw)||0)+(((b=(A=e.face[N].rotation)==null?void 0:A.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((S=(k=xe.face[N].rotation)==null?void 0:k.angle)==null?void 0:S.pitch)||0)+(((E=(C=e.face[N].rotation)==null?void 0:C.angle)==null?void 0:E.pitch)||0))/r},W.gaze={bearing:((r-1)*(((_=xe.face[N].rotation)==null?void 0:_.gaze.bearing)||0)+((($=e.face[N].rotation)==null?void 0:$.gaze.bearing)||0))/r,strength:((r-1)*(((M=xe.face[N].rotation)==null?void 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o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=u}}}function Sw(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],a.pad=rr(e,nn.padding),a.resize=ge.resizeBilinear(a.pad,[t,t]);let n=qe(a.resize,"int32");return Object.keys(a).forEach(i=>Y(a[i])),n}function Cw(e,t){e.keypoints=e.keypoints.filter(n=>n==null?void 0:n.position);for(let n of 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e.detect(i.tensor,e.config):void 0;t(o)}},a?n.src=a:t(void 0)})}async function A0e(e){let t=r=>Buffer.from(r,"base64"),a;e.config.warmup==="face"?a=t(B0):a=t(W0);let n;if("node"in Ue&&ia()==="tensorflow"){let r=(void 0).decodeJpeg(a),s=Gt(r,0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&K("Warmup tfjs-node not loaded");return n}async function y0e(e){let t;return typeof createImageBitmap=="function"?t=await g0e(e):typeof Image!="undefined"||ne.Canvas!==void 0?t=await x0e(e):t=await A0e(e),t}async function b0e(e){var o,l,u,p;if(!W().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=ia(),a=tr();if(t!=="webgl"&&t!=="humangl"||!(a!=null&&a.checkCompileCompletion))return;W().set("ENGINE_COMPILE_ONLY",!0);let n=kt().state.numTensors,r=[];for(let[c,d]of Object.entries(e.models.models)){if(!d)continue;let h=(d==null?void 0:d.modelSignature)&&((l=(o=d==null?void 0:d.inputs)==null?void 0:o[0])==null?void 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Mu,Mp,$p,V0,Is,A5=class{constructor(t){ue(this,"version");ue(this,"config");ue(this,"result");ue(this,"state");ue(this,"process");ue(this,"tf");ue(this,"env",ne);ue(this,"draw",s0);ue(this,"match",$0);ue(this,"models");ue(this,"events");ue(this,"faceTriangulation");ue(this,"faceUVMap");ue(this,"performance");Gn(this,Mu,void 0);Gn(this,Mp,void 0);Gn(this,$p,void 0);ue(this,"analyze",(...t)=>{if(!Ga(this,Mp))return;let a=this.tf.engine().state.numTensors,n=Ga(this,Mu);fr(this,Mu,a);let r=a-n;r!==0&&K(...t,r)});Gn(this,V0,t=>{if(!Ga(this,$p))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof pt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});ue(this,"webcam",new Jh);ue(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Gn(this,Is,{});let a=(xp.tfjs||A2).replace(/-(.*)/,"");Io.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,Io.modelBasePath=ne.browser?"../models/":"file://models/",this.version=X3,Object.defineProperty(this,"version",{value:X3}),this.config=JSON.parse(JSON.stringify(Io)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Ct(this.config,t)),u9(this.config),this.tf=Ue,this.state="idle",fr(this,Mu,0),fr(this,Mp,!1),fr(this,$p,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Rp(this),tg(),this.result=or(),this.process={tensor:null,canvas:null},this.faceTriangulation=tw,this.faceUVMap=aw,L0(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Io)),this.config.backend=t,j3(),ne.initial=!0}validate(t){let a=U3(Io,t||this.config);return a.length===0&&(this.config=Ct(this.config,t)),a}now(){return te()}image(t,a=!1){return Zh(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Ct(this.config,a)),!this.config.segmentation.enabled)return null;let n=await Zh(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jk(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await Ik(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await Xk(n.tensor,this.config)),Y(n.tensor),r}compare(t,a){return l9(this.config,t,a)}async init(){await wp(this,!0),await this.tf.ready(),j3()}async load(t){this.state="load";let a=te(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Ct(this.config,t)),this.env.initial&&(await wp(this,!1)||K("error: backend check failed"),await Kd(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(te()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return kk(t,this.config)}async warmup(t){let a=te(),n=await Yk(this,t),r=te();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,x,A,y,b,w,S,C,E,_,$,M,I,N,O,L,B,G,j,U,H;this.state="config";let r;this.config=Ct(this.config,a),this.state="check";let s=Ga(this,V0).call(this,t);s&&(K(s,t),this.emit("error"),n(or(s)));let i=te();await this.load(),r=te(),this.state="image";let o=await Zh(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(te()-r):Math.trunc(te()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(or("could not convert input to tensor"));return}this.emit("image"),r=te(),this.config.skipAllowed=await o9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(te()-r):Math.trunc(te()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Vg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=te(),l=this.config.face.enabled?await Vg(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Ct(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?c5(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("blazepose")?u=this.config.body.enabled?ig(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?hg(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("movenet")&&(u=this.config.body.enabled?s5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=te(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await c5(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await ig(o.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await hg(o.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await s5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Ct(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?qg(o.tensor,h):[]:(M=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&M.includes("handtrack")&&(p=this.config.hand.enabled?Zg(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=te(),(N=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&N.includes("handdetect")?p=this.config.hand.enabled?await qg(o.tensor,h):[]:(L=(O=this.config.hand.detector)==null?void 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Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=te(),f=[...Qw(l),...Jw(u),...tk(p),...ek(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(te()-i):Math.trunc(te()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Zk(l,u,p,f,m)}},Y(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Ga(this,Is)[t.id]||(this.config.debug&&K("video 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I0=Z(H());var N=Z(H());var S1=` +"use strict"; +var __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; +var __commonJS = (cb, mod3) => function __require() { + return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; +}; +var __export = (target, all2) => { + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( + isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, + mod3 +)); +var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); +var __publicField = (obj, key, value) => { + __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); + return value; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all2) => { + for (var name in all2) + __defProp2(target, name, { get: all2[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); + var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); + var tf_node_gpu_exports = {}; + __export2(tf_node_gpu_exports, { + version: () => version7 + }); + module2.exports = __toCommonJS2(tf_node_gpu_exports); + __reExport(tf_node_gpu_exports, require("@tensorflow/tfjs-node-gpu"), module2.exports); + var version4 = "4.2.0"; + var version22 = "4.2.0"; + var version32 = "4.2.0"; + var version42 = "4.2.0"; + var version5 = "4.2.0"; + var version6 = "0.0.1-alpha.17"; + var version7 = { + tfjs: version4, + "tfjs-core": version4, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version42, + "tfjs-backend-wasm": version5, + "tfjs-backend-webgpu": version6 + }; + } +}); + +// src/human.ts +var human_exports = {}; +__export(human_exports, { + Env: () => Env, + Human: () => Human, + default: () => Human, + defaults: () => config, + draw: () => draw_exports, + empty: () => empty, + env: () => env, + match: () => match_exports, + models: () => models_exports2 +}); +module.exports = __toCommonJS(human_exports); +var tf38 = __toESM(require_tfjs_esm()); + +// src/util/util.ts +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`modelpath error: expecting json file: ${path}`); + return path; +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function validate(defaults, config3, parent = "config", msgs = []) { + for (const key of Object.keys(config3)) { + if (typeof config3[key] === "object") { + validate(defaults[key], config3[key], key, msgs); + } else { + const defined = defaults && typeof defaults[key] !== "undefined"; + if (!defined) + msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); + const same = defaults && typeof defaults[key] === typeof config3[key]; + if (defined && !same) + msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); + } + } + if (config3.debug && parent === "config" && msgs.length > 0) + log("invalid configuration", msgs); + return msgs; +} +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "", + modelBasePath: "", + cacheModels: true, + validateModels: true, + wasmPath: "", + wasmPlatformFetch: false, + debug: false, + async: true, + warmup: "full", + cacheSensitivity: 0.7, + skipAllowed: false, + deallocate: false, + flags: {}, + softwareKernels: false, + filter: { + enabled: true, + equalization: false, + width: 0, + height: 0, + flip: false, + return: true, + autoBrightness: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: false, + maxDetected: 1, + skipFrames: 99, + skipTime: 2500, + minConfidence: 0.2, + iouThreshold: 0.1, + mask: false, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json", + keepInvalid: false + }, + attention: { + enabled: false, + modelPath: "facemesh-attention.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 99, + skipTime: 1500, + modelPath: "emotion.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 99, + skipTime: 3e3, + minConfidence: 0.1 + }, + antispoof: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "antispoof.json" + }, + liveness: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "liveness.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: -1, + minConfidence: 0.3, + skipFrames: 1, + skipTime: 200 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 99, + skipTime: 1e3, + minConfidence: 0.5, + iouThreshold: 0.2, + maxDetected: -1, + landmarks: true, + detector: { + modelPath: "handtrack.json" + }, + skeleton: { + modelPath: "handlandmark-lite.json" + } + }, + object: { + enabled: false, + modelPath: "centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 99, + skipTime: 2e3 + }, + segmentation: { + enabled: false, + modelPath: "rvm.json", + ratio: 0.5, + mode: "default" + } +}; + +// src/util/env.ts +var tf3 = __toESM(require_tfjs_esm()); + +// src/image/image.ts +var tf2 = __toESM(require_tfjs_esm()); + +// src/image/imagefxshaders.ts +var vertexIdentity = ` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -14,7 +323,8 @@ vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var j1=` +`; +var colorMatrixWithAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -26,7 +336,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,N1=` +`; +var colorMatrixWithoutAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -38,7 +349,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,I1=` +`; +var pixelate = ` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -51,7 +363,8 @@ vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,O1=` +`; +var blur = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -74,7 +387,8 @@ gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`,L1=` +`; +var convolution = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -96,20 +410,6055 @@ c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`;var Yt=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,s)=>(n[s]=0,r))},Kt=class{constructor(t,n,o){k(this,"uniform",{});k(this,"attribute",{});k(this,"gl");k(this,"id");k(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(u(`filter: gl compile failed: 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C1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((p=e0.get())==null?void 0:p.length)>1?R0=e0.apply(v0):R0=e0.draw(v0)):(t.debug&&u("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,Z2(v0,R0))):(Z2(v0,R0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:R0};if(!R0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&N.browser)l=N.browser?N.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=N.tensor(f,[e.height,e.width,c],"int32")}else if((!c2||R0.width!==c2.width||R0.height!==c2.height)&&(c2=te(R0.width,R0.height)),N.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=N.browser.fromPixels(R0):(c2=Z2(R0),l=N.browser.fromPixels(c2));else{let M=Z2(R0).getContext("2d").getImageData(0,0,s,A);c=M.data.length/s/A;let T=new Uint8Array(M.data.buffer);l=N.tensor(T,[s,A,c])}if(c===4){let f=N.slice3d(l,[0,0,0],[-1,-1,3]);N.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let x=N.cast(l,"float32"),i=t.filter.equalization?await G2(x):N.expandDims(x,0);if(N.dispose([l,x]),t.filter.autoBrightness){let f=N.max(i),b=await 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O0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},E0={};async function os(e,t){return O0.debug&&u("load model fetch:",e,t),fetch(e,t)}function F1(e){O0.cacheModels=e.cacheModels,O0.verbose=e.debug,O0.modelBasePath=e.modelBasePath}async function O(e){var c,x,i,y;let t=z1(O0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;E0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:Qt[o],inCache:!1,url:""},O0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=O0.cacheSupported&&O0.cacheModels?await d2.io.listModels():{}}catch(d){O0.cacheSupported=!1}E0[o].inCache=O0.cacheSupported&&O0.cacheModels&&Object.keys(s).includes(r),E0[o].url=E0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,p)=>os(d,p)},a=new d2.GraphModel(E0[o].url,A),l=!1;try{a.findIOHandler(),O0.debug&&u("model load 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0.3)":K.color,t.fill())}}function Os(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ke(e.rotation.angle.pitch)/90,s=new Path2D(` - M ${e.box[0]+e.box[2]/2} ${e.box[1]} +`; + +// src/image/imagefx.ts +var collect = (source, prefix, collection) => { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match2, name) => { + collection[name] = 0; + return match2; + }); +}; +var GLProgram = class { + constructor(gl, vertexSource, fragmentSource) { + __publicField(this, "uniform", {}); + __publicField(this, "attribute", {}); + __publicField(this, "gl"); + __publicField(this, "id"); + __publicField(this, "compile", (source, type) => { + const shader = this.gl.createShader(type); + if (!shader) { + log("filter: could not create shader"); + return null; + } + this.gl.shaderSource(shader, source); + this.gl.compileShader(shader); + if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { + log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); + return null; + } + return shader; + }); + this.gl = gl; + const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); + const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); + this.id = this.gl.createProgram(); + if (!vertexShader || !fragmentShader) + return; + if (!this.id) { + log("filter: could not create webgl program"); + return; + } + this.gl.attachShader(this.id, vertexShader); + this.gl.attachShader(this.id, fragmentShader); + this.gl.linkProgram(this.id); + if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { + log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); + return; + } + this.gl.useProgram(this.id); + collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = this.gl.getAttribLocation(this.id, a); + collect(vertexSource, "uniform", this.uniform); + collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = this.gl.getUniformLocation(this.id, u); + } +}; +function GLImageFilter() { + let drawCount = 0; + let sourceTexture = null; + let lastInChain = false; + let currentFramebufferIndex = -1; + let tempFramebuffers = [null, null]; + let filterChain = []; + let vertexBuffer = null; + let currentProgram = null; + const fxcanvas = canvas(100, 100); + const shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = fxcanvas.getContext("webgl"); + if (!gl) { + log("filter: cannot get webgl context"); + return; + } + this.gl = gl; + function resize(width, height) { + if (width === fxcanvas.width && height === fxcanvas.height) + return; + fxcanvas.width = width; + fxcanvas.height = height; + if (!vertexBuffer) { + const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); + vertexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); + tempFramebuffers = [null, null]; + } + function createFramebufferTexture(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + } + function getTempFramebuffer(index2) { + tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); + return tempFramebuffers[index2]; + } + function draw(flags = 0) { + if (!currentProgram) + return; + let source = null; + let target = null; + let flipY = false; + if (drawCount === 0) + source = sourceTexture; + else + source = getTempFramebuffer(currentFramebufferIndex).texture || null; + drawCount++; + if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = drawCount % 2 === 0; + } else { + currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; + target = getTempFramebuffer(currentFramebufferIndex).fbo || null; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + } + function compileShader(fragmentSource) { + if (shaderProgramCache[fragmentSource]) { + currentProgram = shaderProgramCache[fragmentSource]; + gl.useProgram((currentProgram ? currentProgram.id : null) || null); + return currentProgram; + } + currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); + if (!currentProgram) { + log("filter: could not get webgl program"); + return null; + } + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(currentProgram.attribute["pos"]); + gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(currentProgram.attribute["uv"]); + gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); + shaderProgramCache[fragmentSource] = currentProgram; + return currentProgram; + } + const filter = { + colorMatrix: (matrix) => { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; + const program = compileShader(shader); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + draw(); + }, + brightness: (brightness) => { + const b = (brightness || 0) + 1; + filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + saturation: (amount) => { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturate: () => { + filter.saturation(-1); + }, + contrast: (amount) => { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }, + negative: () => { + filter.contrast(-2); + }, + hue: (rotation) => { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturateLuminance: () => { + filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }, + sepia: () => { + filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + brownie: () => { + filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }, + vintagePinhole: () => { + filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }, + kodachrome: () => { + filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }, + technicolor: () => { + filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }, + polaroid: () => { + filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + shiftToBGR: () => { + filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + convolution: (matrix) => { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / fxcanvas.width; + const pixelSizeY = 1 / fxcanvas.height; + const program = compileShader(convolution); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); + draw(); + }, + detectEdges: () => { + filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }, + sobelX: () => { + filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }, + sobelY: () => { + filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }, + sharpen: (amount) => { + const a = amount || 1; + filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }, + emboss: (size2) => { + const s = size2 || 1; + filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }, + blur: (size2) => { + const blurSizeX = size2 / 7 / fxcanvas.width; + const blurSizeY = size2 / 7 / fxcanvas.height; + const program = compileShader(blur); + if (!program) + return; + gl.uniform2f(program.uniform["px"], 0, blurSizeY); + draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform["px"], blurSizeX, 0); + draw(); + }, + pixelate: (size2) => { + const blurSizeX = size2 / fxcanvas.width; + const blurSizeY = size2 / fxcanvas.height; + const program = compileShader(pixelate); + if (!program) + return; + gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); + draw(); + } + }; + this.add = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const func = filter[name]; + filterChain.push({ func, args }); + }; + this.reset = function() { + filterChain = []; + }; + this.get = function() { + return filterChain; + }; + this.apply = function(image28) { + resize(image28.width, image28.height); + drawCount = 0; + if (!sourceTexture) + sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); + for (let i = 0; i < filterChain.length; i++) { + lastInChain = i === filterChain.length - 1; + const f = filterChain[i]; + f.func.apply(this, f.args || []); + } + return fxcanvas; + }; + this.draw = function(image28) { + this.add("brightness", 0); + return this.apply(image28); + }; +} + +// src/image/enhance.ts +var tf = __toESM(require_tfjs_esm()); +async function histogramEqualization(inputImage) { + const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; + const rgb2 = tf.split(squeeze14, 3, 2); + const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; + const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; + const absMax = await Promise.all(max5.map((channel) => channel.data())); + const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); + const maxRange = maxValue > 1 ? 255 : 1; + const factor = maxRange / maxValue; + let final; + if (factor > 1) { + const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; + const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; + const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; + const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); + final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); + tf.dispose([...sub11, ...range, ...enh]); + } else { + final = tf.expandDims(squeeze14, 0); + } + tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); + return final; +} + +// src/image/image.ts +var maxSize = 3840; +var inCanvas = null; +var outCanvas = null; +var tmpCanvas = null; +var fx; +var last = { + inputSum: 0, + cacheDiff: 1, + sumMethod: 0, + inputTensor: void 0 +}; +function reset() { + last.inputSum = 0; + last.cacheDiff = 1; + last.sumMethod = 0; + last.inputTensor = void 0; +} +function canvas(width, height) { + let c; + if (env.browser) { + if (env.worker) { + if (typeof OffscreenCanvas === "undefined") + throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); + c = new OffscreenCanvas(width, height); + } else { + if (typeof document === "undefined") + throw new Error("canvas error: attempted to run in browser but DOM is not defined"); + c = document.createElement("canvas"); + c.width = width; + c.height = height; + } + } else { + if (typeof env.Canvas !== "undefined") + c = new env.Canvas(width, height); + else if (typeof globalThis.Canvas !== "undefined") + c = new globalThis.Canvas(width, height); + } + return c; +} +function copy(input, output) { + const outputCanvas = output || canvas(input.width, input.height); + const ctx = outputCanvas.getContext("2d"); + ctx.drawImage(input, 0, 0); + return outputCanvas; +} +async function process2(input, config3, getTensor = true) { + var _a, _b, _c; + if (!input) { + if (config3.debug) + log("input error: input is missing"); + return { tensor: null, canvas: null }; + } + if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("input error: type not recognized"); + } + if (input instanceof tf2.Tensor) { + let tensor7 = null; + if (input["isDisposedInternal"]) + throw new Error("input error: attempted to use tensor but it is disposed"); + if (!input.shape) + throw new Error("input error: attempted to use tensor without a shape"); + if (input.shape.length === 3) { + if (input.shape[2] === 3) { + tensor7 = tf2.expandDims(input, 0); + } else if (input.shape[2] === 4) { + const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); + tensor7 = tf2.expandDims(rgb2, 0); + tf2.dispose(rgb2); + } + } else if (input.shape.length === 4) { + if (input.shape[3] === 3) { + tensor7 = tf2.clone(input); + } else if (input.shape[3] === 4) { + tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + } + } + if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) + throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); + if (tensor7.dtype === "int32") { + const cast8 = tf2.cast(tensor7, "float32"); + tf2.dispose(tensor7); + tensor7 = cast8; + } + return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; + } + if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { + if (config3.debug) + log("input stream is not ready"); + return { tensor: null, canvas: inCanvas }; + } + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) { + if (config3.debug) + log("cannot determine input dimensions"); + return { tensor: null, canvas: inCanvas }; + } + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); + } + if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) + targetWidth = config3.filter.width; + else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) + targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); + if ((config3.filter.height || 0) > 0) + targetHeight = config3.filter.height; + else if ((config3.filter.width || 0) > 0) + targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("input error: cannot determine dimension"); + if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) + inCanvas = canvas(targetWidth, targetHeight); + const inCtx = inCanvas.getContext("2d"); + if (typeof ImageData !== "undefined" && input instanceof ImageData) { + inCtx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof inCtx.translate !== "undefined") { + inCtx.translate(originalWidth, 0); + inCtx.scale(-1, 1); + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + inCtx.setTransform(1, 0, 0, 1, 0, 0); + } else { + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + } + } + if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) + outCanvas = canvas(inCanvas.width, inCanvas.height); + if (config3.filter.enabled && env.webgl.supported) { + if (!fx) + fx = env.browser ? new GLImageFilter() : null; + env.filter = !!fx; + if (!(fx == null ? void 0 : fx.add)) { + if (config3.debug) + log("input process error: cannot initialize filters"); + env.webgl.supported = false; + config3.filter.enabled = false; + copy(inCanvas, outCanvas); + } else { + fx.reset(); + if (config3.filter.brightness !== 0) + fx.add("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.add("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.add("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.add("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.add("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.add("hue", config3.filter.hue); + if (config3.filter.negative) + fx.add("negative"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.vintage) + fx.add("brownie"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.kodachrome) + fx.add("kodachrome"); + if (config3.filter.technicolor) + fx.add("technicolor"); + if (config3.filter.polaroid) + fx.add("polaroid"); + if (config3.filter.pixelate !== 0) + fx.add("pixelate", config3.filter.pixelate); + if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) + outCanvas = fx.apply(inCanvas); + else + outCanvas = fx.draw(inCanvas); + } + } else { + copy(inCanvas, outCanvas); + if (fx) + fx = null; + env.filter = !!fx; + } + if (!getTensor) + return { tensor: null, canvas: outCanvas }; + if (!outCanvas) + throw new Error("canvas error: cannot create output"); + let pixels; + let depth = 3; + if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { + if (env.browser && tf2.browser) { + pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; + } else { + depth = input.data.length / input.height / input.width; + const arr = new Uint8Array(input.data.buffer); + pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); + } + } else { + if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) + tmpCanvas = canvas(outCanvas.width, outCanvas.height); + if (tf2.browser && env.browser) { + if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { + pixels = tf2.browser.fromPixels(outCanvas); + } else { + tmpCanvas = copy(outCanvas); + pixels = tf2.browser.fromPixels(tmpCanvas); + } + } else { + const tempCanvas = copy(outCanvas); + const tempCtx = tempCanvas.getContext("2d"); + const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); + depth = tempData.data.length / targetWidth / targetHeight; + const arr = new Uint8Array(tempData.data.buffer); + pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); + } + } + if (depth === 4) { + const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); + tf2.dispose(pixels); + pixels = rgb2; + } + if (!pixels) + throw new Error("input error: cannot create tensor"); + const casted = tf2.cast(pixels, "float32"); + const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); + tf2.dispose([pixels, casted]); + if (config3.filter.autoBrightness) { + const max5 = tf2.max(tensor6); + const maxVal = await max5.data(); + config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; + tf2.dispose(max5); + } + return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; +} +async function skip(config3, input) { + let skipFrame = false; + if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) + return skipFrame; + if (!last.inputTensor) { + last.inputTensor = tf2.clone(input); + } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { + tf2.dispose(last.inputTensor); + last.inputTensor = tf2.clone(input); + } else { + const t2 = {}; + t2.diff = tf2.sub(input, last.inputTensor); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; + tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); + last.inputTensor = tf2.clone(input); + skipFrame = diffRelative <= (config3.cacheSensitivity || 0); + } + return skipFrame; +} +async function compare(config3, input1, input2) { + const t2 = {}; + if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { + if (!config3.debug) + log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); + return 0; + } + if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { + if (!config3.debug) + log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); + return 0; + } + t2.input1 = tf2.clone(input1); + t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); + t2.diff = tf2.sub(t2.input1, t2.input2); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; + tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); + return diffRelative; +} + +// src/util/env.ts +var _canvas, _image, _imageData; +var Env = class { + constructor() { + __publicField(this, "browser"); + __publicField(this, "node"); + __publicField(this, "worker"); + __publicField(this, "platform", ""); + __publicField(this, "agent", ""); + __publicField(this, "backends", []); + __publicField(this, "initial"); + __publicField(this, "filter"); + __publicField(this, "tfjs"); + __publicField(this, "offscreen"); + __publicField(this, "perfadd", false); + __publicField(this, "tensorflow", { + version: void 0, + gpu: void 0 + }); + __publicField(this, "wasm", { + supported: void 0, + backend: void 0, + simd: void 0, + multithread: void 0 + }); + __publicField(this, "webgl", { + supported: void 0, + backend: void 0, + version: void 0, + renderer: void 0, + shader: void 0, + vendor: void 0 + }); + __publicField(this, "webgpu", { + supported: void 0, + backend: void 0, + adapter: void 0 + }); + __publicField(this, "cpu", { + model: void 0, + flags: [] + }); + __publicField(this, "kernels", []); + __privateAdd(this, _canvas, void 0); + __privateAdd(this, _image, void 0); + __privateAdd(this, _imageData, void 0); + this.browser = typeof navigator !== "undefined"; + this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; + this.tfjs = { version: tf3.version["tfjs-core"] }; + this.offscreen = typeof OffscreenCanvas !== "undefined"; + this.initial = true; + this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw == null ? void 0 : raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; + this.agent = navigator.userAgent.replace(raw[0], ""); + if (this.platform[1]) + this.agent = this.agent.replace(raw[1], ""); + this.agent = this.agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + this.platform = `${process.platform} ${process.arch}`; + this.agent = `NodeJS ${process.version}`; + } + } + get Canvas() { + return __privateGet(this, _canvas); + } + set Canvas(val) { + __privateSet(this, _canvas, val); + globalThis.Canvas = val; + } + get Image() { + return __privateGet(this, _image); + } + set Image(val) { + __privateSet(this, _image, val); + globalThis.Image = val; + } + get ImageData() { + return __privateGet(this, _imageData); + } + set ImageData(val) { + __privateSet(this, _imageData, val); + globalThis.ImageData = val; + } + async updateBackend() { + this.backends = Object.keys(tf3.engine().registryFactory); + try { + this.tensorflow = { + version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, + gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 + }; + } catch (e) { + } + this.wasm.supported = typeof WebAssembly !== "undefined"; + this.wasm.backend = this.backends.includes("wasm"); + if (this.wasm.supported && this.wasm.backend) { + this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + } + const c = canvas(100, 100); + const gl = c ? c.getContext("webgl2") : void 0; + this.webgl.supported = typeof gl !== "undefined"; + this.webgl.backend = this.backends.includes("webgl"); + if (this.webgl.supported && this.webgl.backend && gl) { + this.webgl.version = gl.getParameter(gl.VERSION); + this.webgl.vendor = gl.getParameter(gl.VENDOR); + this.webgl.renderer = gl.getParameter(gl.RENDERER); + this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); + } + this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; + this.webgpu.backend = this.backends.includes("webgpu"); + try { + if (this.webgpu.supported) { + const adapter = await navigator.gpu.requestAdapter(); + this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); + } + } catch (e) { + this.webgpu.supported = false; + } + try { + this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); + } catch (e) { + } + } + updateCPU() { + const cpu = { model: "", flags: [] }; + if (this.node && this.platform.startsWith("linux")) { + } + if (!this.cpu) + Object.defineProperty(this, "cpu", { value: cpu }); + else + this.cpu = cpu; + } +}; +_canvas = new WeakMap(); +_image = new WeakMap(); +_imageData = new WeakMap(); +var env = new Env(); + +// src/util/webcam.ts +var WebCam = class { + constructor() { + __publicField(this, "config"); + __publicField(this, "element"); + __publicField(this, "stream"); + __publicField(this, "devices", []); + __publicField(this, "enumerate", async () => { + try { + const devices = await navigator.mediaDevices.enumerateDevices(); + this.devices = devices.filter((device) => device.kind === "videoinput"); + } catch (e) { + this.devices = []; + } + return this.devices; + }); + __publicField(this, "start", async (webcamConfig) => { + var _a, _b; + if (webcamConfig == null ? void 0 : webcamConfig.debug) + this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; + if (webcamConfig == null ? void 0 : webcamConfig.crop) + this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; + if (webcamConfig == null ? void 0 : webcamConfig.mode) + this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; + if (webcamConfig == null ? void 0 : webcamConfig.width) + this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; + if (webcamConfig == null ? void 0 : webcamConfig.height) + this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; + if (webcamConfig == null ? void 0 : webcamConfig.id) + this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; + if (webcamConfig == null ? void 0 : webcamConfig.element) { + if (typeof webcamConfig.element === "string") { + const el = document.getElementById(webcamConfig.element); + if (el && el instanceof HTMLVideoElement) { + this.element = el; + } else { + if (this.config.debug) + log("webcam", "cannot get dom element", webcamConfig.element); + return; + } + } else if (webcamConfig.element instanceof HTMLVideoElement) { + this.element = webcamConfig.element; + } else { + if (this.config.debug) + log("webcam", "unknown dom element", webcamConfig.element); + return; + } + } else { + this.element = document.createElement("video"); + } + const requestedConstraints = { + audio: false, + video: { + facingMode: this.config.mode === "front" ? "user" : "environment", + resizeMode: this.config.crop ? "crop-and-scale" : "none" + } + }; + if (((_a = this.config) == null ? void 0 : _a.width) > 0) + requestedConstraints.video.width = { ideal: this.config.width }; + if (((_b = this.config) == null ? void 0 : _b.height) > 0) + requestedConstraints.video.height = { ideal: this.config.height }; + if (this.config.id) + requestedConstraints.video.deviceId = this.config.id; + this.element.addEventListener("play", () => { + if (this.config.debug) + log("webcam", "play"); + }); + this.element.addEventListener("pause", () => { + if (this.config.debug) + log("webcam", "pause"); + }); + this.element.addEventListener("click", async () => { + if (!this.element || !this.stream) + return; + if (this.element.paused) + await this.element.play(); + else + this.element.pause(); + }); + if (!(navigator == null ? void 0 : navigator.mediaDevices)) { + if (this.config.debug) + log("webcam", "no devices"); + return; + } + try { + this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); + } catch (err) { + log("webcam", err); + return; + } + if (!this.stream) { + if (this.config.debug) + log("webcam", "no stream"); + return; + } + this.element.srcObject = this.stream; + const ready3 = new Promise((resolve) => { + if (!this.element) + resolve(false); + else + this.element.onloadeddata = () => resolve(true); + }); + await ready3; + await this.element.play(); + if (this.config.debug) { + log("webcam", { + width: this.width, + height: this.height, + label: this.label, + stream: this.stream, + track: this.track, + settings: this.settings, + constraints: this.constraints, + capabilities: this.capabilities + }); + } + }); + __publicField(this, "pause", () => { + if (this.element) + this.element.pause(); + }); + __publicField(this, "play", async () => { + if (this.element) + await this.element.play(); + }); + __publicField(this, "stop", () => { + if (this.config.debug) + log("webcam", "stop"); + if (this.track) + this.track.stop(); + }); + this.config = { + element: void 0, + debug: true, + mode: "front", + crop: false, + width: 0, + height: 0 + }; + } + get track() { + if (!this.stream) + return void 0; + return this.stream.getVideoTracks()[0]; + } + get capabilities() { + if (!this.track) + return void 0; + return this.track.getCapabilities ? this.track.getCapabilities() : void 0; + } + get constraints() { + if (!this.track) + return void 0; + return this.track.getConstraints ? this.track.getConstraints() : void 0; + } + get settings() { + if (!this.stream) + return void 0; + const track = this.stream.getVideoTracks()[0]; + return track.getSettings ? track.getSettings() : void 0; + } + get label() { + if (!this.track) + return ""; + return this.track.label; + } + get paused() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.paused) || false; + } + get width() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; + } + get height() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; + } +}; + +// src/tfjs/load.ts +var tf4 = __toESM(require_tfjs_esm()); + +// models/models.json +var models_exports = {}; +__export(models_exports, { + age: () => age, + "anti-spoofing": () => anti_spoofing, + antispoof: () => antispoof, + blazeface: () => blazeface, + "blazeface-back": () => blazeface_back, + "blazeface-front": () => blazeface_front, + "blazepose-detector": () => blazepose_detector, + "blazepose-full": () => blazepose_full, + "blazepose-heavy": () => blazepose_heavy, + "blazepose-lite": () => blazepose_lite, + centernet: () => centernet, + default: () => models_default, + efficientpose: () => efficientpose, + "efficientpose-i-lite": () => efficientpose_i_lite, + "efficientpose-ii-lite": () => efficientpose_ii_lite, + "efficientpose-iv": () => efficientpose_iv, + emotion: () => emotion, + faceboxes: () => faceboxes, + facemesh: () => facemesh, + "facemesh-attention": () => facemesh_attention, + "facemesh-attention-pinto": () => facemesh_attention_pinto, + "facemesh-detection-full": () => facemesh_detection_full, + "facemesh-detection-short": () => facemesh_detection_short, + faceres: () => faceres, + "faceres-deep": () => faceres_deep, + gear: () => gear, + gender: () => gender, + "gender-ssrnet-imdb": () => gender_ssrnet_imdb, + handdetect: () => handdetect, + "handlandmark-full": () => handlandmark_full, + "handlandmark-lite": () => handlandmark_lite, + "handlandmark-sparse": () => handlandmark_sparse, + handskeleton: () => handskeleton, + handtrack: () => handtrack, + "insightface-efficientnet-b0": () => insightface_efficientnet_b0, + "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, + "insightface-mobilenet-emore": () => insightface_mobilenet_emore, + "insightface-mobilenet-swish": () => insightface_mobilenet_swish, + iris: () => iris, + liveness: () => liveness, + meet: () => meet, + mobileface: () => mobileface, + mobilefacenet: () => mobilefacenet, + models: () => models, + "movenet-lightning": () => movenet_lightning, + "movenet-multipose": () => movenet_multipose, + "movenet-thunder": () => movenet_thunder, + nanodet: () => nanodet, + "nanodet-e": () => nanodet_e, + "nanodet-g": () => nanodet_g, + "nanodet-m": () => nanodet_m, + "nanodet-t": () => nanodet_t, + posenet: () => posenet, + rvm: () => rvm, + selfie: () => selfie +}); +var antispoof = 853098; +var blazeface = 538928; +var centernet = 4030290; +var emotion = 820516; +var facemesh = 1477958; +var faceres = 6978814; +var handlandmark_lite = 2023432; +var handtrack = 2964837; +var iris = 2599092; +var liveness = 592976; +var models = 0; +var movenet_lightning = 4650216; +var age = 161240; +var blazeface_back = 538928; +var blazeface_front = 402048; +var blazepose_detector = 5928856; +var blazepose_full = 6339202; +var blazepose_heavy = 27502466; +var blazepose_lite = 2726402; +var efficientpose = 5651240; +var faceboxes = 2013002; +var facemesh_attention_pinto = 2387598; +var facemesh_attention = 2382414; +var facemesh_detection_full = 1026192; +var facemesh_detection_short = 201268; +var faceres_deep = 13957620; +var gear = 1498916; +var gender_ssrnet_imdb = 161236; +var gender = 201808; +var handdetect = 3515612; +var handlandmark_full = 5431368; +var handlandmark_sparse = 5286322; +var handskeleton = 5502280; +var meet = 372228; +var mobileface = 2183192; +var mobilefacenet = 5171976; +var movenet_multipose = 9448838; +var movenet_thunder = 12477112; +var nanodet = 7574558; +var posenet = 5032780; +var rvm = 3739355; +var selfie = 212886; +var anti_spoofing = 853098; +var efficientpose_i_lite = 2269064; +var efficientpose_ii_lite = 5651240; +var efficientpose_iv = 25643252; +var insightface_efficientnet_b0 = 13013224; +var insightface_ghostnet_strides1 = 8093408; +var insightface_ghostnet_strides2 = 8049584; +var insightface_mobilenet_emore = 6938536; +var insightface_mobilenet_swish = 12168584; +var nanodet_e = 12319156; +var nanodet_g = 7574558; +var nanodet_m = 1887474; +var nanodet_t = 5294216; +var models_default = { + antispoof, + blazeface, + centernet, + emotion, + facemesh, + faceres, + "handlandmark-lite": handlandmark_lite, + handtrack, + iris, + liveness, + models, + "movenet-lightning": movenet_lightning, + age, + "blazeface-back": blazeface_back, + "blazeface-front": blazeface_front, + "blazepose-detector": blazepose_detector, + "blazepose-full": blazepose_full, + "blazepose-heavy": blazepose_heavy, + "blazepose-lite": blazepose_lite, + efficientpose, + faceboxes, + "facemesh-attention-pinto": facemesh_attention_pinto, + "facemesh-attention": facemesh_attention, + "facemesh-detection-full": facemesh_detection_full, + "facemesh-detection-short": facemesh_detection_short, + "faceres-deep": faceres_deep, + gear, + "gender-ssrnet-imdb": gender_ssrnet_imdb, + gender, + handdetect, + "handlandmark-full": handlandmark_full, + "handlandmark-sparse": handlandmark_sparse, + handskeleton, + meet, + mobileface, + mobilefacenet, + "movenet-multipose": movenet_multipose, + "movenet-thunder": movenet_thunder, + nanodet, + posenet, + rvm, + selfie, + "anti-spoofing": anti_spoofing, + "efficientpose-i-lite": efficientpose_i_lite, + "efficientpose-ii-lite": efficientpose_ii_lite, + "efficientpose-iv": efficientpose_iv, + "insightface-efficientnet-b0": insightface_efficientnet_b0, + "insightface-ghostnet-strides1": insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": insightface_ghostnet_strides2, + "insightface-mobilenet-emore": insightface_mobilenet_emore, + "insightface-mobilenet-swish": insightface_mobilenet_swish, + "nanodet-e": nanodet_e, + "nanodet-g": nanodet_g, + "nanodet-m": nanodet_m, + "nanodet-t": nanodet_t +}; + +// src/tfjs/load.ts +var options = { + cacheModels: true, + cacheSupported: true, + verbose: true, + debug: false, + modelBasePath: "" +}; +var modelStats = {}; +async function httpHandler(url, init4) { + if (options.debug) + log("load model fetch:", url, init4); + return fetch(url, init4); +} +function setModelLoadOptions(config3) { + options.cacheModels = config3.cacheModels; + options.verbose = config3.debug; + options.modelBasePath = config3.modelBasePath; +} +async function loadModel(modelPath) { + var _a, _b, _c, _d; + let modelUrl = join(options.modelBasePath, modelPath || ""); + if (!modelUrl.toLowerCase().endsWith(".json")) + modelUrl += ".json"; + const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); + const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); + const cachedModelName = "indexeddb://" + shortModelName; + modelStats[shortModelName] = { + name: shortModelName, + sizeFromManifest: 0, + sizeLoadedWeights: 0, + sizeDesired: models_exports[shortModelName], + inCache: false, + url: "" + }; + options.cacheSupported = typeof indexedDB !== "undefined"; + let cachedModels = {}; + try { + cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; + } catch (e) { + options.cacheSupported = false; + } + modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); + modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; + const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; + let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); + let loaded = false; + try { + model23.findIOHandler(); + if (options.debug) + log("model load handler:", model23["handler"]); + } catch (err) { + log("error finding model i/o handler:", modelUrl, err); + } + try { + const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; + modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; + if (artifacts) + model23.loadSync(artifacts); + else + model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; + if (options.verbose) + log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); + loaded = true; + } catch (err) { + log("error loading model:", modelUrl, err); + } + if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { + try { + const saveResult = await model23.save(cachedModelName); + if (options.debug) + log("model saved:", cachedModelName, saveResult); + } catch (err) { + log("error saving model:", modelUrl, err); + } + } + return model23; +} + +// package.json +var version2 = "3.0.2"; + +// src/tfjs/backend.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/tfjs/humangl.ts +var tf5 = __toESM(require_tfjs_esm()); +var config2 = { + name: "humangl", + priority: 999, + canvas: null, + gl: null, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +function register(instance) { + var _a; + if (instance.config.backend !== "humangl") + return; + if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { + log("humangl error: backend invalid context"); + instance.models.reset(); + } + if (!tf5.findBackend(config2.name)) { + try { + config2.canvas = canvas(100, 100); + } catch (err) { + log("humangl error: cannot create canvas:", err); + return; + } + try { + config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); + if (!config2.gl) { + log("humangl error: cannot get webgl context"); + return; + } + const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); + if (!glv2) { + log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); + instance.config.backend = "webgl"; + return; + } + if (config2.canvas) { + config2.canvas.addEventListener("webglcontextlost", (e) => { + log("humangl error:", e.type); + log("possible browser memory leak using webgl or conflict with multiple backend registrations"); + instance.emit("error"); + throw new Error("backend error: webgl context lost"); + }); + config2.canvas.addEventListener("webglcontextrestored", (e) => { + log("humangl error: context restored:", e); + }); + config2.canvas.addEventListener("webglcontextcreationerror", (e) => { + log("humangl error: context create:", e); + }); + } + } catch (err) { + log("humangl error: cannot get webgl context:", err); + return; + } + try { + tf5.setWebGLContext(2, config2.gl); + } catch (err) { + log("humangl error: cannot set webgl context:", err); + return; + } + try { + const ctx = new tf5.GPGPUContext(config2.gl); + tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("humangl error: cannot register webgl backend:", err); + return; + } + try { + const kernels = tf5.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tf5.registerKernel(newKernelConfig); + }); + } catch (err) { + log("humangl error: cannot update webgl backend registration:", err); + return; + } + try { + if (tf5.env().flagRegistry.WEBGL_VERSION) + tf5.env().set("WEBGL_VERSION", 2); + } catch (err) { + log("humangl error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + const backend4 = tf5.backend(); + const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; + if (current) { + if (instance.config.debug) + log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); + } else { + log("humangl error: no current gl context:", current, config2.gl); + } + } +} + +// src/tfjs/constants.ts +var tf6 = __toESM(require_tfjs_esm()); +var constants = { + tf255: 255, + tf1: 1, + tf2: 2, + tf05: 0.5, + tf127: 127.5, + rgb: [0.2989, 0.587, 0.114] +}; +function init() { + constants.tf255 = tf6.scalar(255, "float32"); + constants.tf1 = tf6.scalar(1, "float32"); + constants.tf2 = tf6.scalar(2, "float32"); + constants.tf05 = tf6.scalar(0.5, "float32"); + constants.tf127 = tf6.scalar(127.5, "float32"); + constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); +} + +// src/tfjs/backend.ts +async function getBestBackend() { + var _a; + await env.updateBackend(); + if ((_a = env.tensorflow) == null ? void 0 : _a.version) + return "tensorflow"; + if (env.webgpu.supported && env.webgpu.backend) + return "webgpu"; + if (env.webgl.supported && env.webgl.backend) + return "webgl"; + if (env.wasm.supported && env.wasm.backend) + return "wasm"; + return "cpu"; +} +function registerCustomOps(config3) { + const newKernels = []; + if (!env.kernels.includes("mod")) { + const kernelMod = { + kernelName: "Mod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) + }; + tf7.registerKernel(kernelMod); + env.kernels.push("mod"); + newKernels.push("mod"); + } + if (!env.kernels.includes("floormod")) { + const kernelFloorMod = { + kernelName: "FloorMod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) + }; + tf7.registerKernel(kernelFloorMod); + env.kernels.push("floormod"); + newKernels.push("floormod"); + } + if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { + const kernelRotateWithOffset = { + kernelName: "RotateWithOffset", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => { + const backend4 = tf7.getBackend(); + tf7.setBackend("cpu"); + const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); + tf7.setBackend(backend4); + return t2; + }) + }; + tf7.registerKernel(kernelRotateWithOffset); + env.kernels.push("rotatewithoffset"); + newKernels.push("rotatewithoffset"); + } + if (newKernels.length > 0 && config3.debug) + log("registered kernels:", newKernels); +} +var defaultFlags = {}; +async function check(instance, force = false) { + var _a; + instance.state = "backend"; + if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) + instance.config.backend = await getBestBackend(); + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { + const timeStamp = now(); + if (instance.config.backend && instance.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { + if (instance.config.debug) + log("running inside web worker"); + } + if (env.browser && instance.config.backend === "tensorflow") { + if (instance.config.debug) + log("override: backend set to tensorflow while running in browser"); + instance.config.backend = "webgl"; + } + if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { + if (instance.config.debug) + log(`override: backend set to ${instance.config.backend} while running in nodejs`); + instance.config.backend = "tensorflow"; + } + if (env.browser && instance.config.backend === "webgpu") { + if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { + log("override: backend set to webgpu but browser does not support webgpu"); + instance.config.backend = "webgl"; + } else { + const adapter = await navigator.gpu.requestAdapter(); + if (instance.config.debug) + log("enumerated webgpu adapter:", adapter); + if (!adapter) { + log("override: backend set to webgpu but browser reports no available gpu"); + instance.config.backend = "webgl"; + } else { + const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; + log("webgpu adapter info:", adapterInfo); + } + } + } + let available = Object.keys(tf7.engine().registryFactory); + if (instance.config.backend === "humangl" && !available.includes("humangl")) { + register(instance); + available = Object.keys(tf7.engine().registryFactory); + } + if (instance.config.debug) + log("available backends:", available); + if (!available.includes(instance.config.backend)) { + log(`error: backend ${instance.config.backend} not found in registry`); + instance.config.backend = env.node ? "tensorflow" : "webgl"; + if (instance.config.debug) + log(`override: setting backend ${instance.config.backend}`); + } + if (instance.config.debug) + log("setting backend:", [instance.config.backend]); + if (instance.config.backend === "wasm") { + if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) + tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); + if (instance.config.debug) + log("wasm path:", instance.config.wasmPath); + if (typeof tf7.setWasmPaths !== "undefined") + tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); + else + throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); + let mt = false; + let simd = false; + try { + mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + if (instance.config.debug) + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (instance.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } catch (e) { + log("wasm detection failed"); + } + } + try { + await tf7.setBackend(instance.config.backend); + await tf7.ready(); + } catch (err) { + log("error: cannot set backend:", instance.config.backend, err); + return false; + } + if (instance.config.debug) + defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); + } + if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { + if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (tf7.env().flagRegistry.WEBGL_EXP_CONV) + tf7.env().set("WEBGL_EXP_CONV", true); + if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + } + if (tf7.getBackend() === "webgpu") { + } + if (instance.config.debug) { + const newFlags = tf7.env().flags; + const updatedFlags = {}; + for (const key of Object.keys(newFlags)) { + if (defaultFlags[key] === newFlags[key]) + continue; + updatedFlags[key] = newFlags[key]; + } + if (instance.config.debug && Object.keys(updatedFlags).length > 0) + log("backend:", tf7.getBackend(), "flags:", updatedFlags); + } + if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { + if (instance.config.debug) + log("flags:", instance.config["flags"]); + for (const [key, val] of Object.entries(instance.config.flags)) { + tf7.env().set(key, val); + } + } + tf7.enableProdMode(); + init(); + instance.performance.initBackend = Math.trunc(now() - timeStamp); + instance.config.backend = tf7.getBackend(); + await env.updateBackend(); + registerCustomOps(instance.config); + env.initial = false; + } + return true; +} +function fakeOps(kernelNames, config3) { + for (const kernelName of kernelNames) { + const kernelConfig = { + kernelName, + backendName: config3.backend, + kernelFunc: (param) => { + var _a; + if (config3.debug) + log("kernelFunc", kernelName, config3.backend, param); + return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; + } + }; + tf7.registerKernel(kernelConfig); + } + env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); +} + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body, + canvas: () => canvas2, + face: () => face, + gesture: () => gesture, + hand: () => hand, + init: () => init2, + object: () => object, + options: () => options2, + person: () => person +}); + +// src/draw/primitives.ts +var getCanvasContext = (input) => { + if (!input) + log("draw error: invalid canvas"); + else if (!input.getContext) + log("draw error: canvas context not defined"); + else { + const ctx = input.getContext("2d"); + if (!ctx) + log("draw error: cannot get canvas context"); + else + return ctx; + } + return null; +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); +var colorDepth = (z, opt) => { + if (!opt.useDepth || typeof z === "undefined") + return opt.color; + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); + return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; +}; +function labels(ctx, str, startX, startY, localOptions2) { + const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); + const x = Math.max(0, startX); + for (let i = line.length - 1; i >= 0; i--) { + const y = i * localOptions2.lineHeight + startY; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(line[i], x + 5, y + 16); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(line[i], x + 4, y + 15); + } +} +function point(ctx, x, y, z, localOptions2) { + ctx.fillStyle = colorDepth(z, localOptions2); + ctx.beginPath(); + ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions2) { + ctx.beginPath(); + ctx.lineWidth = localOptions2.lineWidth; + if (localOptions2.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.moveTo(x + localOptions2.roundRect, y); + ctx.lineTo(x + width - localOptions2.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); + ctx.lineTo(x + width, y + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); + ctx.lineTo(x + localOptions2.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); + ctx.lineTo(x, y + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); + ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); + } + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.lineWidth = localOptions2.lineWidth; + if (!localOptions2.useCurves || points.length <= 2) { + lines(ctx, points, localOptions2); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function arrow(ctx, from, to, radius = 5) { + let angle; + let x; + let y; + ctx.beginPath(); + ctx.moveTo(from[0], from[1]); + ctx.lineTo(to[0], to[1]); + angle = Math.atan2(to[1] - from[1], to[0] - from[0]); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + ctx.closePath(); + ctx.stroke(); + ctx.fill(); +} + +// src/draw/options.ts +var options2 = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + alpha: 0.5, + font: 'small-caps 16px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawAttention: true, + drawGestures: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false, + faceLabels: "", + bodyLabels: "", + bodyPartLabels: "", + objectLabels: "", + handLabels: "", + fingerLabels: "", + gestureLabels: "" +}; + +// src/face/facemeshcoords.ts +var meshAnnotations = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], + lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], + lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], + lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], + lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisIndices = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, + { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, + { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, + { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, + { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, + { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, + { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, + { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } +]; +var UV468 = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + [0.289712011814117, 0.380764007568359], + [0.499954998493195, 0.312398016452789], + [0.499987006187439, 0.269918978214264], + [0.500023007392883, 0.107050001621246], + [0.500023007392883, 0.666234016418457], + [0.5000159740448, 0.679224014282227], + [0.500023007392883, 0.692348003387451], + [0.499976992607117, 0.695277988910675], + [0.499976992607117, 0.70593398809433], + [0.499976992607117, 0.719385027885437], + [0.499976992607117, 0.737019002437592], + [0.499967992305756, 0.781370997428894], + [0.499816000461578, 0.562981009483337], + [0.473773002624512, 0.573909997940063], + [0.104906998574734, 0.254140973091125], + [0.365929991006851, 0.409575998783112], + [0.338757991790771, 0.41302502155304], + [0.311120003461838, 0.409460008144379], + [0.274657994508743, 0.389131009578705], + [0.393361985683441, 0.403706014156342], + [0.345234006643295, 0.344011008739471], + [0.370094001293182, 0.346076011657715], + [0.319321990013123, 0.347265005111694], + [0.297903001308441, 0.353591024875641], + [0.24779200553894, 0.410809993743896], + [0.396889001131058, 0.842755019664764], + [0.280097991228104, 0.375599980354309], + [0.106310002505779, 0.399955987930298], 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+ 343, + 357, + 412, + 437, + 343, + 399, + 344, + 360, + 440, + 420, + 437, + 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); +function connectionsToIndices(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var pairsLips = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var pairsFaceContour = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +var contourKeypoints = { + lips: connectionsToIndices(pairsLips), + leftEye: connectionsToIndices(pairsLeftEye), + leftEyebrow: connectionsToIndices(pairsLeftEyebrow), + leftIris: connectionsToIndices(pairsLeftIris), + rightEye: connectionsToIndices(pairsRightEye), + rightEyebrow: connectionsToIndices(pairsRightEyebrow), + rightIris: connectionsToIndices(pairsRightIris), + faceOval: connectionsToIndices(pairsFaceContour) +}; + +// src/face/constants.ts +var LIPS_CONNECTIONS = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var FACE_OVAL_CONNECTIONS = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +function connectionsToIndices2(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { + lips: connectionsToIndices2(LIPS_CONNECTIONS), + leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), + leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), + leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), + rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), + rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), + rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), + faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) +}; +var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); +var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); +var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ + 61, + 146, + 91, + 181, + 84, + 17, + 314, + 405, + 321, + 375, + 291, + 185, + 40, + 39, + 37, + 0, + 267, + 269, + 270, + 409, + 78, + 95, + 88, + 178, + 87, + 14, + 317, + 402, + 318, + 324, + 308, + 191, + 80, + 81, + 82, + 13, + 312, + 311, + 310, + 415, + 76, + 77, + 90, + 180, + 85, + 16, + 315, + 404, + 320, + 307, + 306, + 184, + 74, + 73, + 72, + 11, + 302, + 303, + 304, + 408, + 62, + 96, + 89, + 179, + 86, + 15, + 316, + 403, + 319, + 325, + 292, + 183, + 42, + 41, + 38, + 12, + 268, + 271, + 272, + 407 +]; +var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ + 33, + 7, + 163, + 144, + 145, + 153, + 154, + 155, + 133, + 246, + 161, + 160, + 159, + 158, + 157, + 173, + 130, + 25, + 110, + 24, + 23, + 22, + 26, + 112, + 243, + 247, + 30, + 29, + 27, + 28, + 56, + 190, + 226, + 31, + 228, + 229, + 230, + 231, + 232, + 233, + 244, + 113, + 225, + 224, + 223, + 222, + 221, + 189, + 35, + 124, + 46, + 53, + 52, + 65, + 143, + 111, + 117, + 118, + 119, + 120, + 121, + 128, + 245, + 156, + 70, + 63, + 105, + 66, + 107, + 55, + 193 +]; +var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ + 263, + 249, + 390, + 373, + 374, + 380, + 381, + 382, + 362, + 466, + 388, + 387, + 386, + 385, + 384, + 398, + 359, + 255, + 339, + 254, + 253, + 252, + 256, + 341, + 463, + 467, + 260, + 259, + 257, + 258, + 286, + 414, + 446, + 261, + 448, + 449, + 450, + 451, + 452, + 453, + 464, + 342, + 445, + 444, + 443, + 442, + 441, + 413, + 265, + 353, + 276, + 283, + 282, + 295, + 372, + 340, + 346, + 347, + 348, + 349, + 350, + 357, + 465, + 383, + 300, + 293, + 334, + 296, + 336, + 285, + 417 +]; + +// src/draw/face.ts +var localOptions; +function drawLabels(f, ctx) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i; + if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) + return; + let l = localOptions.faceLabels.slice(); + if (f.score) + l = replace(l, "[score]", 100 * f.score); + if (f.gender) + l = replace(l, "[gender]", f.gender); + if (f.genderScore) + l = replace(l, "[genderScore]", 100 * f.genderScore); + if (f.age) + l = replace(l, "[age]", f.age); + if (f.distance) + l = replace(l, "[distance]", 100 * f.distance); + if (f.real) + l = replace(l, "[real]", 100 * f.real); + if (f.live) + l = replace(l, "[live]", 100 * f.live); + if (f.emotion && f.emotion.length > 0) { + const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion2.length > 3) + emotion2.length = 3; + l = replace(l, "[emotions]", emotion2.join(" ")); + } + if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) + l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); + if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) + l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); + if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) + l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); + if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) + l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); + labels(ctx, l, f.box[0], f.box[1], localOptions); +} +function drawIrisElipse(f, ctx) { + var _a, _b, _c, _d; + if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } +} +function drawGazeSpheres(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") { + ctx.strokeStyle = "pink"; + const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90; + const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90; + const pathV = new Path2D(` + M ${f.box[0] + f.box[2] / 2} ${f.box[1]} C - ${o} ${e.box[1]}, - ${o} ${e.box[1]+e.box[3]}, - ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} - `),A=new Path2D(` - M ${e.box[0]} ${e.box[1]+e.box[3]/2} + ${valX} ${f.box[1]}, + ${valX} ${f.box[1] + f.box[3]}, + ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} + `); + const pathH = new Path2D(` + M ${f.box[0]} ${f.box[1] + f.box[3] / 2} C - ${e.box[0]} ${r}, - ${e.box[0]+e.box[2]} ${r}, - ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);$t(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function J2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)Z1(r,c,o)}}}function Q2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let x=o.fingerLabels.slice();x=_(x,"[label]",l),ne(r,x,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let x=0;x0?x-1:0][0],c[x>0?x-1:0][1]),r.lineTo(c[x][0],c[x][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function _2(e,t,n){var s;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function $2(e,t,n){var r;let o=a0(x0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let x=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",x),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face + ${f.box[0]} ${valY}, + ${f.box[0] + f.box[2]} ${valY}, + ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} + `); + ctx.stroke(pathH); + ctx.stroke(pathV); + } +} +function drawGazeArrows(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { + ctx.strokeStyle = "pink"; + ctx.fillStyle = "pink"; + const leftGaze = [ + f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); + const rightGaze = [ + f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); + } +} +function drawFacePolygons(f, ctx) { + if (localOptions.drawPolygons && f.mesh.length >= 468) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); + lines(ctx, points, localOptions); + } + drawIrisElipse(f, ctx); + } +} +function drawFacePoints(f, ctx) { + if (localOptions.drawPoints && f.mesh.length >= 468) { + for (let i = 0; i < f.mesh.length; i++) { + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); + if (localOptions.drawAttention) { + if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); + if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + } + } + } +} +function drawFaceBoxes(f, ctx) { + if (localOptions.drawBoxes) { + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); + } +} +function face(inCanvas2, result, drawOptions) { + localOptions = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + for (const f of result) { + drawFaceBoxes(f, ctx); + drawLabels(f, ctx); + if (f.mesh && f.mesh.length > 0) { + drawFacePoints(f, ctx); + drawFacePolygons(f, ctx); + drawGazeSpheres(f, ctx); + drawGazeArrows(f, ctx); + } + } +} + +// src/draw/body.ts +function body(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + ctx.lineWidth = localOptions2.lineWidth; + ctx.font = localOptions2.font; + if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.bodyLabels.slice(); + l = replace(l, "[score]", 100 * result[i].score); + labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); + } + } + if (localOptions2.drawPoints && result[i].keypoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) + continue; + ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); + } + } + if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { + ctx.font = localOptions2.font; + for (const pt of result[i].keypoints) { + if (!pt.score || pt.score === 0) + continue; + let l = localOptions2.bodyPartLabels.slice(); + l = replace(l, "[label]", pt.part); + l = replace(l, "[score]", 100 * pt.score); + labels(ctx, l, pt.position[0], pt.position[1], localOptions2); + } + } + if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { + for (const part of Object.values(result[i].annotations)) { + for (const connected4 of part) + curves(ctx, connected4, localOptions2); + } + } + } +} + +// src/draw/hand.ts +function hand(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.handLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + if (localOptions2.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = colorDepth(pt[2], localOptions2); + point(ctx, pt[0], pt[1], 0, localOptions2); + } + } + } + if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { + for (const [part, pt] of Object.entries(h.annotations)) { + let l = localOptions2.fingerLabels.slice(); + l = replace(l, "[label]", part); + labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); + } + } + if (localOptions2.drawPolygons && h.annotations) { + const addHandLine = (part) => { + if (!part || part.length === 0 || !part[0]) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions2); + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions2.lineWidth; + addHandLine(h.annotations.index); + addHandLine(h.annotations.middle); + addHandLine(h.annotations.ring); + addHandLine(h.annotations.pinky); + addHandLine(h.annotations.thumb); + } + } +} + +// src/draw/object.ts +function object(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.objectLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + } +} + +// src/draw/gesture.ts +function gesture(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions2.font; + ctx.fillStyle = localOptions2.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + const [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + let l = localOptions2.gestureLabels.slice(); + l = replace(l, "[where]", where[0]); + l = replace(l, "[who]", who); + l = replace(l, "[what]", what[1]); + labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); + i += 1; + } + } + } +} + +// src/draw/labels.ts +var defaultLabels = { + face: `face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -118,7 +6467,7523 @@ live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var r5=0;function Fs(e,t,n){let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;sa5,kpt:()=>A5});var A5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],a5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,U1,Gs=5,nt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function Y1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var q1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),q1[0]),A=D.mul(D.div(A,t2),q1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function Xs(e,t,n,o){var c,x;let r=[],s={};s.boxes=Zs(e,U1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},jn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||b>.06)&&(d=!1),f>b?f>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let M=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],T=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(T<.01||M<.01||T>.022||M>.022)&&(d=!1),(T<.01||M<.01)&&t.push({iris:A,gesture:"looking down"}),(T>.022||M>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Nn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Pt(e,t=1.5){let n=L2(e),o=Mt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function kt(e){let t=L2(e),n=Mt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function PA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Dn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return PA(n)}var In=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ze(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},f=Wn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Gn=1.65,Vn=[0,5,9,13,17,1,2],SA=0,jA=2,Zn=0,Et=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>J5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Pt(kt(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Pt(kt(n),Gn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=K5(o,[0,0]),c=a.map(d=>[...J5(d,l),d[2]]),x=Fn(r),i=[...L2(n),1],y=[Ze(i,x[0]),Ze(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Zn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,x,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let x=Pt(kt(c),Gn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Xn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,qn;async function Q5(e,t){let n=await qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Rt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Un(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",a2.modelUrl),e.debug&&u("cached model:",i2.modelUrl));let t=a2?new wt(a2):void 0;return t&&i2&&(qn=new Et(t,i2)),[a2,i2]}var Q=Z(H());var f0=[null,null],IA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],OA=["hand","fist","pinch","point","face","tip","pinchtip"],Kn=4,Jn=1.6,LA=512,CA=1.4,zt=Number.MAX_SAFE_INTEGER,_5=0,ke=[0,0],y0={boxes:[],hands:[]},Qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function _n(e){var t;if(R.initial&&(f0[0]=null),f0[0])e.debug&&u("cached model:",f0[0].modelUrl);else{Y2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),f0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=f0[0].executor?Object.values(f0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[0]}async function $n(e){var t;if(R.initial&&(f0[1]=null),f0[1])e.debug&&u("cached model:",f0[1].modelUrl);else{f0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=f0[1].executor?Object.values(f0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[1]}async function WA(e,t){let n=[];if(!e||!f0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,LA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await f0[0].executeAsync(o.cast,IA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[Kn]),a.splice(Kn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=Q.slice(o.boxes,y,1),p=await d.data();Q.dispose(d);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=ot(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=x[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function $5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Rt(o.keypoints);for(let i of Object.keys(Qn))o.annotations[i]=Qn[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function e1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],zt++;let n=(t.hand.skipTime||0)>g()-_5,o=zt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-_5,l=zt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):(y0.boxes=await WA(e,t),_5=g(),y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))),zt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[x].fingerScore&&y0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=ot(i.box,Jn),d=ot(i.boxRaw,Jn);y0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var C2={};ze(C2,{connected:()=>jt,horizontal:()=>t1,kpt:()=>St,relative:()=>o1,vertical:()=>n1});var St=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],t1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],n1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],o1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),r1=0;function to(e,t){var A,a,l,c,x,i,y,d,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0;let n=g();if(!e)return he();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(w.canvas=e.canvas),e.error&&(w.error=e.error),!w.body||e.body.length!==w.body.length)w.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v((r-1)*w.body[v].box[X]+B)/r),n0=e.body[v].boxRaw.map((B,X)=>((r-1)*w.body[v].boxRaw[X]+B)/r),U=e.body[v].keypoints.map((B,X)=>{var z,ee,we,Ee,T0,P2,R1,M1,P1;return{score:B.score,part:B.part,position:[w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[w.body[v].keypoints[X]?((r-1)*(((z=w.body[v].keypoints[X].distance)==null?void 0:z[0])||0)+(((ee=B.distance)==null?void 0:ee[0])||0))/r:(we=B.distance)==null?void 0:we[0],w.body[v].keypoints[X]?((r-1)*(((Ee=w.body[v].keypoints[X].distance)==null?void 0:Ee[1])||0)+(((T0=B.distance)==null?void 0:T0[1])||0))/r:(P2=B.distance)==null?void 0:P2[1],w.body[v].keypoints[X]?((r-1)*(((R1=w.body[v].keypoints[X].distance)==null?void 0:R1[2])||0)+(((M1=B.distance)==null?void 0:M1[2])||0))/r:(P1=B.distance)==null?void 0:P1[2]]}}),g0={},m0={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?m0=At:(a=t.body.modelPath)!=null&&a.includes("blazepose")?m0=tt:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(m0=C2);for(let[B,X]of Object.entries(m0.connected)){let z=[];for(let ee=0;eeT0.part===X[ee]),Ee=U.find(T0=>T0.part===X[ee+1]);we&&Ee&&z.push([we.position,Ee.position])}g0[B]=z}w.body[v]={...e.body[v],box:V,boxRaw:n0,keypoints:U,annotations:g0}}if(!w.hand||e.hand.length!==w.hand.length)w.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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uo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function ho(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:y,position:d})=>({score:i,part:y,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Wt=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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e.config.debug&&A.length>0&&u("model validation failed:",n,A),A.length>0?{name:n,missing:A,ops:s,url:a}:null}var F2=class{constructor(t){k(this,"instance");k(this,"models",{});this.models={},this.instance=t}stats(){let t=0,n=0,o=0;for(let s of Object.values(E0))t+=s.sizeFromManifest,n+=s.sizeLoadedWeights,o+=s.sizeDesired;let r=o>0?n/o:0;return{numLoadedModels:Object.values(E0).length,numDefinedModels:Object.keys(this.models).length,percentageLoaded:r,totalSizeFromManifest:t,totalSizeWeights:n,totalSizeLoading:o,modelStats:Object.values(E0)}}reset(){for(let t of Object.keys(this.models))this.models[t]=null}async load(t){var o,r,s,A,a,l,c,x,i,y,d,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0,v;R.initial&&this.reset(),t&&(this.instance=t);let n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?b3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)==null?void 0:o.enabled)&&!this.models.antispoof?Z3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)==null?void 0:r.enabled)&&!this.models.liveness?Y3(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)==null?void 0:s.enabled)&&!this.models.faceres?F3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)==null?void 0:A.enabled)&&!this.models.emotion?L3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)==null?void 0:a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?P3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)==null?void 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o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(E0).find(r=>r.startsWith(n.name));!o||(n.size=E0[o].sizeLoadedWeights,n.url=E0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Ft(this.instance,o,n);r&&t.push(r)}return t}};function So(e,t,n,o,r){var a,l,c,x,i,y;let s=0,A=[];for(let d of e){let p={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let h of t)d.box[0]>h.box[0]&&d.box[0]h.box[1]&&d.box[1]+d.box[3]p.body.box[0]&&h.box[0]+h.box[2]p.body.box[1]&&h.box[1]+h.box[3]p.body.box[0]&&h.box[1]+h.box[3]>p.body.box[1]&&h.box[1]+h.box[3]{h&&h.length===4&&(f.push(h[0],h[0]+h[2]),b.push(h[1],h[1]+h[3]))};M(p.face.box),M((x=p.body)==null?void 0:x.box),M((i=p.hands.left)==null?void 0:i.box),M((y=p.hands.right)==null?void 0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var d0=Z(H());var Bt=` + gaze: [gaze]\xB0`, + body: "body [score]%", + bodyPart: "[label] [score]%", + object: "[label] [score]%", + hand: "[label] [score]%", + finger: "[label]", + gesture: "[where] [who]: [what]" +}; + +// src/draw/draw.ts +var drawTime = 0; +function person(inCanvas2, result, drawOptions) { + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (let i = 0; i < result.length; i++) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels) { + const label = `person #${i}`; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +function canvas2(input, output) { + if (!input || !output) + return; + const ctx = getCanvasContext(output); + if (!ctx) + return; + ctx.drawImage(input, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + if (!(result == null ? void 0 : result.performance) || !inCanvas2) + return null; + const timeStamp = now(); + const localOptions2 = mergeDeep(options2, drawOptions); + const promise = Promise.all([ + face(inCanvas2, result.face, localOptions2), + body(inCanvas2, result.body, localOptions2), + hand(inCanvas2, result.hand, localOptions2), + object(inCanvas2, result.object, localOptions2), + gesture(inCanvas2, result.gesture, localOptions2) + ]); + drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); + result.performance.draw = drawTime; + return promise; +} +function init2() { + options2.faceLabels = defaultLabels.face; + options2.bodyLabels = defaultLabels.body; + options2.bodyPartLabels = defaultLabels.bodyPart; + options2.handLabels = defaultLabels.hand; + options2.fingerLabels = defaultLabels.finger; + options2.objectLabels = defaultLabels.object; + options2.gestureLabels = defaultLabels.gesture; +} + +// src/body/blazepose.ts +var tf9 = __toESM(require_tfjs_esm()); + +// src/body/blazeposecoords.ts +var blazeposecoords_exports = {}; +__export(blazeposecoords_exports, { + connected: () => connected, + kpt: () => kpt +}); +var kpt = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftPinky", + "rightPinky", + "leftIndex", + "rightIndex", + "leftThumb", + "rightThumb", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle", + "leftHeel", + "rightHeel", + "leftFoot", + "rightFoot", + "bodyCenter", + "bodyTop", + "leftPalm", + "leftHand", + "rightPalm", + "rightHand" +]; +var connected = { + shoulders: ["leftShoulder", "rightShoulder"], + hips: ["rightHip", "leftHip"], + mouth: ["leftMouth", "rightMouth"], + leftLegUpper: ["leftHip", "leftKnee"], + leftLegLower: ["leftKnee", "leftAnkle"], + leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], + leftTorso: ["leftShoulder", "leftHip"], + leftArmUpper: ["leftShoulder", "leftElbow"], + leftArmLower: ["leftElbow", "leftWrist"], + leftHand: ["leftWrist", "leftPalm"], + leftHandPinky: ["leftPalm", "leftPinky"], + leftHandIndex: ["leftPalm", "leftIndex"], + leftHandThumb: ["leftPalm", "leftThumb"], + leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], + rightLegUpper: ["rightHip", "rightKnee"], + rightLegLower: ["rightKnee", "rightAnkle"], + rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], + rightTorso: ["rightShoulder", "rightHip"], + rightArmUpper: ["rightShoulder", "rightElbow"], + rightArmLower: ["rightElbow", "rightWrist"], + rightHand: ["rightWrist", "rightPalm"], + rightHandPinky: ["rightPalm", "rightPinky"], + rightHandIndex: ["rightPalm", "rightIndex"], + rightHandThumb: ["rightPalm", "rightThumb"], + rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] +}; + +// src/body/blazeposedetector.ts +var tf8 = __toESM(require_tfjs_esm()); +var model; +var inputSize = 224; +var anchorTensor; +var numLayers = 5; +var strides = [8, 16, 32, 32, 32]; +function createAnchors() { + const anchors3 = []; + let layerId = 0; + while (layerId < numLayers) { + let anchorCount = 0; + let lastSameStrideLayer = layerId; + while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { + anchorCount += 2; + lastSameStrideLayer++; + } + const stride = strides[layerId]; + const featureMapHeight = Math.ceil(inputSize / stride); + const featureMapWidth = Math.ceil(inputSize / stride); + for (let y = 0; y < featureMapHeight; ++y) { + for (let x = 0; x < featureMapWidth; ++x) { + for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); + } + } + } + layerId = lastSameStrideLayer; + } + anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; +} +async function loadDetector(config3) { + if (env.initial) + model = null; + if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { + model = await loadModel(config3.body["detector"].modelPath); + const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; + inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug && model) + log("cached model:", model["modelUrl"]); + createAnchors(); + return model; +} +var cropFactor = [5, 5]; +function decodeBoxes(boxesTensor, anchor) { + return tf8.tidy(() => { + const split6 = tf8.split(boxesTensor, 12, 1); + let xCenter = tf8.squeeze(split6[0]); + let yCenter = tf8.squeeze(split6[1]); + let width = tf8.squeeze(split6[2]); + let height = tf8.squeeze(split6[3]); + xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); + yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); + width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); + height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); + const xMin = tf8.sub(xCenter, tf8.div(width, 2)); + const yMin = tf8.sub(yCenter, tf8.div(height, 2)); + const xMax = tf8.add(xMin, width); + const yMax = tf8.add(yMin, height); + const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); + return boxes; + }); +} +async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { + var _a, _b; + const detectedBoxes = []; + const t2 = {}; + t2.boxes = decodeBoxes(boxesTensor, anchorTensor); + t2.scores = tf8.sigmoid(logitsTensor); + t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); + const nms = await t2.nms.data(); + const scores = await t2.scores.data(); + const boxes = await t2.boxes.array(); + for (const i of Array.from(nms)) { + const score = scores[i]; + const boxRaw = boxes[i]; + const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; + const detectedBox = { score, boxRaw, box }; + detectedBoxes.push(detectedBox); + } + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return detectedBoxes; +} +async function detectBoxes(input, config3, outputSize2) { + const t2 = {}; + t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); + t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); + t2.logits = tf8.squeeze(t2.logitsRaw); + t2.boxes = tf8.squeeze(t2.boxesRaw); + const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return boxes; +} + +// src/util/box.ts +function calc(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function square(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; + const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); + const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function scale(box, scaleFact) { + const dist = [box[2] * scaleFact, box[3] * scaleFact]; + const newBox = [ + box[0] - (dist[0] - box[2]) / 2, + box[1] - (dist[1] - box[3]) / 2, + dist[0], + dist[1] + ]; + return newBox; +} + +// src/body/blazepose.ts +var model2; +var inputSize2 = 256; +var skipped = Number.MAX_SAFE_INTEGER; +var outputNodes = { + landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], + detector: [] +}; +var cache = []; +var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; +var lastTime = 0; +var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); +var loadDetect = (config3) => loadDetector(config3); +async function loadPose(config3) { + if (env.initial) + model2 = null; + if (!model2) { + model2 = await loadModel(config3.body.modelPath); + const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; + inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug) + log("cached model:", model2["modelUrl"]); + return model2; +} +function prepareImage(input, size2, cropBox) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + let final; + if (cropBox) { + t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + } + if (input.shape[1] !== input.shape[2]) { + const height = [ + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 + ]; + const width = [ + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 + ]; + padding = [ + [0, 0], + height, + width, + [0, 0] + ]; + t2.pad = tf9.pad(t2.cropped || input, padding); + t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else if (input.shape[1] !== size2) { + t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else { + final = tf9.div(t2.cropped || input, constants.tf255); + } + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + return final; +} +function rescaleKeypoints(keypoints, outputSize2, cropBox) { + for (const kpt4 of keypoints) { + kpt4.position = [ + Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), + Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), + kpt4.position[2] + ]; + kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; + } + if (cropBox) { + const width = cropBox[2] - cropBox[0]; + const height = cropBox[3] - cropBox[1]; + for (const kpt4 of keypoints) { + kpt4.positionRaw = [ + kpt4.positionRaw[0] / height + cropBox[1], + kpt4.positionRaw[1] / width + cropBox[0], + kpt4.positionRaw[2] + ]; + kpt4.position = [ + Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), + Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), + kpt4.positionRaw[2] + ]; + } + } + return keypoints; +} +function fixKeypoints(keypoints) { + const leftPalm = keypoints.find((k) => k.part === "leftPalm"); + const leftWrist = keypoints.find((k) => k.part === "leftWrist"); + const leftIndex = keypoints.find((k) => k.part === "leftIndex"); + leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; + const rightPalm = keypoints.find((k) => k.part === "rightPalm"); + const rightWrist = keypoints.find((k) => k.part === "rightWrist"); + const rightIndex = keypoints.find((k) => k.part === "rightIndex"); + rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; +} +async function detectLandmarks(input, config3, outputSize2) { + if (!(model2 == null ? void 0 : model2["executor"])) + return null; + const t2 = {}; + [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); + const poseScore = (await t2.poseflag.data())[0]; + const points = await t2.ld.data(); + const distances = await t2.world.data(); + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + const keypointsRelative = []; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + const score = sigmoid2(points[depth * i + 3]); + const presence = sigmoid2(points[depth * i + 4]); + const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; + const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; + const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; + const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; + keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); + } + if (poseScore < (config3.body.minConfidence || 0)) + return null; + fixKeypoints(keypointsRelative); + const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); + const kpts = keypoints.map((k) => k.position); + const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; + return body4; +} +async function predict(input, config3) { + var _a, _b, _c; + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime; + const skipFrame = skipped < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { + skipped++; + } else { + let boxes = []; + if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { + const preparedImage = prepareImage(input, 224); + boxes = await detectBoxes(preparedImage, config3, outputSize2); + tf9.dispose(preparedImage); + } else { + boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; + } + for (let i = 0; i < boxes.length; i++) { + const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); + cache.length = 0; + const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); + tf9.dispose(preparedBox); + if (!bodyResult) + continue; + bodyResult.id = i; + cache.push(bodyResult); + } + lastTime = now(); + skipped = 0; + } + return cache; +} + +// src/object/centernet.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/object/labels.ts +var labels2 = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/centernet.ts +var model3; +var inputSize3 = 0; +var last2 = []; +var lastTime2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +async function load(config3) { + if (env.initial) + model3 = null; + if (!model3) { + model3 = await loadModel(config3.object.modelPath); + const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; + inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} +async function process3(res, outputShape, config3) { + if (!res) + return []; + const t2 = {}; + const results = []; + const detections = await res.array(); + t2.squeeze = tf10.squeeze(res); + const arr = tf10.split(t2.squeeze, 6, 1); + t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); + t2.boxes = tf10.squeeze(t2.stack); + t2.scores = tf10.squeeze(arr[4]); + t2.classes = tf10.squeeze(arr[5]); + tf10.dispose([res, ...arr]); + t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t2.nms.data(); + let i = 0; + for (const id of Array.from(nms)) { + const score = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + if (Number.isNaN(classVal)) + continue; + const label = labels2[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize3, + detections[0][id][1] / inputSize3 + ]; + const boxRaw = [ + x, + y, + detections[0][id][2] / inputSize3 - x, + detections[0][id][3] / inputSize3 - y + ]; + const box = [ + Math.trunc(boxRaw[0] * outputShape[0]), + Math.trunc(boxRaw[1] * outputShape[1]), + Math.trunc(boxRaw[2] * outputShape[0]), + Math.trunc(boxRaw[3] * outputShape[1]) + ]; + results.push({ id: i++, score, class: classVal, label, box, boxRaw }); + } + Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); + return results; +} +async function predict2(input, config3) { + if (!(model3 == null ? void 0 : model3["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; + const skipFrame = skipped2 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { + skipped2++; + return last2; + } + skipped2 = 0; + return new Promise(async (resolve) => { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); + const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; + lastTime2 = now(); + tf10.dispose(resize); + const obj = await process3(objectT, outputSize2, config3); + last2 = obj; + resolve(obj); + }); +} + +// src/body/efficientpose.ts +var tf11 = __toESM(require_tfjs_esm()); + +// src/body/efficientposecoords.ts +var efficientposecoords_exports = {}; +__export(efficientposecoords_exports, { + connected: () => connected2, + kpt: () => kpt2 +}); +var kpt2 = [ + "head", + "neck", + "rightShoulder", + "rightElbow", + "rightWrist", + "chest", + "leftShoulder", + "leftElbow", + "leftWrist", + "bodyCenter", + "rightHip", + "rightKnee", + "rightAnkle", + "leftHip", + "leftKnee", + "leftAnkle" +]; +var connected2 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/efficientpose.ts +var model4; +var lastTime3 = 0; +var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; +var skipped3 = Number.MAX_SAFE_INTEGER; +async function load2(config3) { + if (env.initial) + model4 = null; + if (!model4) + model4 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + const reshaped = tf11.reshape(inputs, [height * width]); + const max5 = tf11.max(reshaped, 0); + const newScore = (await max5.data())[0]; + if (newScore > minScore) { + const coordinates = tf11.argMax(reshaped, 0); + const mod3 = tf11.mod(coordinates, width); + const x = (await mod3.data())[0]; + const div15 = tf11.div(coordinates, width); + const y = (await div15.data())[0]; + tf11.dispose([reshaped, max5, coordinates, mod3, div15]); + return [x, y, newScore]; + } + tf11.dispose([reshaped, max5]); + return [0, 0, newScore]; +} +async function predict3(image28, config3) { + if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) + return []; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; + const skipFrame = skipped3 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { + skipped3++; + return [cache2]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + const tensor6 = tf11.tidy(() => { + var _a, _b; + const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); + const enhance2 = tf11.mul(resize, constants.tf2); + const norm = tf11.sub(enhance2, constants.tf1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = model4 == null ? void 0 : model4.execute(tensor6); + lastTime3 = now(); + tf11.dispose(tensor6); + if (resT) { + cache2.keypoints.length = 0; + const squeeze14 = tf11.squeeze(resT); + tf11.dispose(resT); + const stack5 = tf11.unstack(squeeze14, 2); + tf11.dispose(squeeze14); + for (let id = 0; id < stack5.length; id++) { + const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); + if (partScore > (config3.body.minConfidence || 0)) { + cache2.keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: kpt2[id], + positionRaw: [ + x2 / model4.inputs[0].shape[2], + y2 / model4.inputs[0].shape[1] + ], + position: [ + Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), + Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) + ] + }); + } + } + stack5.forEach((s) => tf11.dispose(s)); + } + cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = cache2.keypoints.map((a) => a.position[0]); + const y = cache2.keypoints.map((a) => a.position[1]); + cache2.box = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); + const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); + cache2.boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + for (const [name, indexes] of Object.entries(connected2)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + cache2.annotations[name] = pt; + } + resolve([cache2]); + }); +} + +// src/face/face.ts +var tf25 = __toESM(require_tfjs_esm()); + +// src/face/facemesh.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/face/blazeface.ts +var tf13 = __toESM(require_tfjs_esm()); + +// src/face/facemeshutil.ts +var tf12 = __toESM(require_tfjs_esm()); +var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; +var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; +var clampBox = (box, input) => box ? [ + Math.trunc(Math.max(0, box.startPoint[0])), + Math.trunc(Math.max(0, box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) +] : [0, 0, 0, 0]; +var getRawBox = (box, input) => box ? [ + box.startPoint[0] / (input.shape[2] || 0), + box.startPoint[1] / (input.shape[1] || 0), + (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), + (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) +] : [0, 0, 0, 0]; +var scaleBoxCoordinates = (box, factor) => { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; +}; +var cutAndResize = (box, image28, cropSize) => { + const h = image28.shape[1]; + const w = image28.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; + const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); + const norm = tf12.div(crop, constants.tf255); + tf12.dispose(crop); + return norm; +}; +var enlargeBox = (box, factor) => { + const center = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; +}; +var squarifyBox = (box) => { + const centers = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = Math.max(...size2) / 2; + return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; +}; +var calculateLandmarksBoundingBox = (landmarks) => { + const x = landmarks.map((d) => d[0]); + const y = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; +}; +var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); +var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +var dot = (v1, v2) => { + let product = 0; + for (let i = 0; i < v1.length; i++) + product += v1[i] * v2[i]; + return product; +}; +var getColumnFrom2DArr = (arr, columnIndex) => { + const column = []; + for (let i = 0; i < arr.length; i++) + column.push(arr[i][columnIndex]); + return column; +}; +var multiplyTransformMatrices = (mat1, mat2) => { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + return product; +}; +var buildRotationMatrix = (rotation, center) => { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +}; +var invertTransformMatrix = (matrix) => { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; + return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; +}; +var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; +function generateAnchors(inputSize10) { + const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize10 + stride - 1) / stride); + const gridCols = Math.floor((inputSize10 + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) + anchors3.push([anchorX, anchorY]); + } + } + } + return anchors3; +} +function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { + const boxSize = getBoxSize(box); + const coordsScaled = coordsRaw.map((coord) => [ + boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), + boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), + coord[2] || 0 + ]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; + const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; + const boxCenter = getBoxCenter(box); + const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + offsets[0]), + Math.trunc(coord[1] + offsets[1]), + Math.trunc(coord[2] || 0) + ]); +} +function correctFaceRotation(rotate, box, input, inputSize10) { + const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + let angle = 0; + let rotationMatrix = fixedRotationMatrix; + let face4; + if (rotate && env.kernels.includes("rotatewithoffset")) { + angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + if (largeAngle) { + const center = getBoxCenter(box); + const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; + const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + rotationMatrix = buildRotationMatrix(-angle, center); + face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); + tf12.dispose(rotated); + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + return [angle, rotationMatrix, face4]; +} +var findFaceCenter = (mesh) => { + const x = mesh.map((m) => m[0]); + const y = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; +}; +var calculateFaceBox = (mesh, previousBox) => { + const center = findFaceCenter(mesh); + const boxSize = getBoxSize(previousBox); + const calculatedBox = { + startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], + endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] + }; + return calculatedBox; +}; + +// src/face/blazeface.ts +var keypointsCount = 6; +var faceBoxScaleFactor = 1.4; +var model5; +var anchors = null; +var inputSize4 = 0; +var inputSizeT = null; +var size = () => inputSize4; +async function load3(config3) { + var _a; + if (env.initial) + model5 = null; + if (!model5) + model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model5["modelUrl"]); + inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; + inputSizeT = tf13.scalar(inputSize4, "int32"); + anchors = tf13.tensor2d(generateAnchors(inputSize4)); + return model5; +} +function decodeBoxes2(boxOutputs) { + if (!anchors || !inputSizeT) + return tf13.zeros([0, 0]); + const t2 = {}; + t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); + t2.centers = tf13.add(t2.boxStarts, anchors); + t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); + t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); + t2.centersNormalized = tf13.div(t2.centers, inputSizeT); + t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); + t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); + t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); + t2.startNormalized = tf13.mul(t2.starts, inputSizeT); + t2.endNormalized = tf13.mul(t2.ends, inputSizeT); + const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} +async function getBoxes(inputImage, config3) { + var _a, _b, _c, _d; + if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return []; + const t2 = {}; + t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t2.div = tf13.div(t2.resized, constants.tf127); + t2.normalized = tf13.sub(t2.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t2.normalized); + if (Array.isArray(res) && res.length > 2) { + const sorted = res.sort((a, b) => a.size - b.size); + t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); + t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); + t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); + t2.batch = tf13.squeeze(t2.concat, [0]); + } else if (Array.isArray(res)) { + t2.batch = tf13.squeeze(res[0]); + } else { + t2.batch = tf13.squeeze(res); + } + tf13.dispose(res); + t2.boxes = decodeBoxes2(t2.batch); + t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); + t2.sigmoid = tf13.sigmoid(t2.logits); + t2.scores = tf13.squeeze(t2.sigmoid); + t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); + const nms = await t2.nms.array(); + const boxes = []; + const scores = await t2.scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scores[nms[i]]; + if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { + const b = {}; + b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); + b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = tf13.squeeze(b.slice); + b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); + const points = await b.bbox.data(); + const rawBox = { + startPoint: [points[0], points[1]], + endPoint: [points[2], points[3]], + landmarks: await b.landmarks.array(), + confidence + }; + const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); + const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); + const squaredBox = squarifyBox(enlargedBox); + boxes.push(squaredBox); + Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); + } + } + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} + +// src/face/iris.ts +var tf14 = __toESM(require_tfjs_esm()); +var model6; +var inputSize5 = 0; +var irisEnlarge = 2.3; +var leftOutline = meshAnnotations.leftEyeLower0; +var rightOutline = meshAnnotations.rightEyeLower0; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +async function load4(config3) { + var _a, _b; + if (env.initial) + model6 = null; + if (!model6) + model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model6["modelUrl"]); + inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; + if (inputSize5 === -1) + inputSize5 = 64; + return model6; +} +function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < irisIndices.length; i++) { + const { key, indices } = irisIndices[i]; + const originalIndices = meshAnnotations[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index2 = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index2][0], + newCoords[index2][1], + (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var getLeftToRightEyeDepthDifference = (rawCoords) => { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; +}; +var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { + const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); + const boxSize = getBoxSize(box); + let crop = tf14.image.cropAndResize(face4, [[ + box.startPoint[1] / meshSize, + box.startPoint[0] / meshSize, + box.endPoint[1] / meshSize, + box.endPoint[0] / meshSize + ]], [0], [inputSize5, inputSize5]); + if (flip && env.kernels.includes("flipleftright")) { + const flipped = tf14.image.flipLeftRight(crop); + tf14.dispose(crop); + crop = flipped; + } + return { box, boxSize, crop }; +}; +var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; +}; +var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { + const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); +}; +async function augmentIris(rawCoords, face4, meshSize) { + if (!(model6 == null ? void 0 : model6["executor"])) + return rawCoords; + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); + const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); + tf14.dispose(leftEyeCrop); + tf14.dispose(rightEyeCrop); + const eyePredictions = model6.execute(combined); + tf14.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tf14.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); + const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; +} + +// src/face/attention.ts +async function augment(rawCoords, results) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + const t2 = { + lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), + irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), + eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), + irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), + eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) + }; + for (const val of Object.values(t2)) { + if (!val) + return rawCoords; + } + const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisL.length / 2; i++) + rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); + const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisR.length / 2; i++) + rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t2.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + return rawCoords; +} + +// src/face/facemesh.ts +var cache3 = { + boxes: [], + skipped: Number.MAX_SAFE_INTEGER, + timestamp: 0 +}; +var model7 = null; +var inputSize6 = 0; +async function predict4(input, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + if (!(model7 == null ? void 0 : model7["executor"])) + return []; + const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; + const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); + if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { + cache3.boxes = await getBoxes(input, config3); + cache3.timestamp = now(); + cache3.skipped = 0; + } else { + cache3.skipped++; + } + const faces = []; + const newCache = []; + let id = 0; + const size2 = inputSize6; + for (let i = 0; i < cache3.boxes.length; i++) { + const box = cache3.boxes[i]; + let angle = 0; + let rotationMatrix; + const face4 = { + id: id++, + mesh: [], + meshRaw: [], + box: [0, 0, 0, 0], + boxRaw: [0, 0, 0, 0], + score: 0, + boxScore: 0, + faceScore: 0, + annotations: {} + }; + [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); + if (config3.filter.equalization) { + const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; + tf15.dispose(face4.tensor); + if (equilized) + face4.tensor = equilized; + } + face4.boxScore = Math.round(100 * box.confidence) / 100; + if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } else if (!model7) { + if (config3.debug) + log("face mesh detection requested, but model is not loaded"); + } else { + if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { + config3.face.attention.enabled = false; + tf15.dispose(face4.tensor); + return faces; + } + const results = model7.execute(face4.tensor); + const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); + const faceConfidence = await confidenceT.data(); + face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; + if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { + box.confidence = face4.faceScore; + if (config3.face.mesh.keepInvalid) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } + } else { + const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); + const coordsReshaped = tf15.reshape(meshT, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + tf15.dispose(coordsReshaped); + if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { + rawCoords = await augment(rawCoords, results); + } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); + } + face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(meshAnnotations)) + face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); + face4.score = face4.faceScore; + const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; + face4.box = clampBox(calculatedBox, input); + face4.boxRaw = getRawBox(calculatedBox, input); + newCache.push(calculatedBox); + } + tf15.dispose(results); + } + if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) + faces.push(face4); + else + tf15.dispose(face4.tensor); + } + cache3.boxes = newCache; + return faces; +} +async function load5(config3) { + var _a, _b, _c, _d, _e, _f; + if (env.initial) + model7 = null; + if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { + if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) + model7 = null; + } + if (!model7) { + if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) + model7 = await loadModel(config3.face.attention.modelPath); + else + model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); + } else if (config3.debug) { + log("cached model:", model7["modelUrl"]); + } + inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; + return model7; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/gear/emotion.ts +var tf16 = __toESM(require_tfjs_esm()); +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model8; +var last3 = []; +var lastCount = 0; +var lastTime4 = 0; +var skipped4 = Number.MAX_SAFE_INTEGER; +async function load6(config3) { + var _a; + if (env.initial) + model8 = null; + if (!model8) + model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model8["modelUrl"]); + return model8; +} +async function predict5(image28, config3, idx, count2) { + var _a, _b; + if (!model8) + return []; + const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; + if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { + skipped4++; + return last3[idx]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + var _a2; + const obj = []; + if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { + const t2 = {}; + const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; + t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); + t2.channels = tf16.mul(t2.resize, constants.rgb); + t2.grayscale = tf16.sum(t2.channels, 3, true); + t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); + t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); + t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); + lastTime4 = now(); + const data = await t2.emotion.data(); + for (let i = 0; i < data.length; i++) { + if (data[i] > (config3.face.emotion.minConfidence || 0)) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); + } + last3[idx] = obj; + lastCount = count2; + resolve(obj); + }); +} + +// src/face/faceres.ts +var tf17 = __toESM(require_tfjs_esm()); +var model9; +var last4 = []; +var lastTime5 = 0; +var lastCount2 = 0; +var skipped5 = Number.MAX_SAFE_INTEGER; +async function load7(config3) { + var _a; + if (env.initial) + model9 = null; + if (!model9) + model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +function enhance(input) { + const tensor6 = input.image || input.tensor || input; + if (!(model9 == null ? void 0 : model9.inputs[0].shape)) + return tensor6; + const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + const norm = tf17.mul(crop, constants.tf255); + tf17.dispose(crop); + return norm; +} +async function predict6(image28, config3, idx, count2) { + var _a, _b, _c, _d; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if (!(model9 == null ? void 0 : model9["executor"])) + return obj; + const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; + if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped5++; + return last4[idx]; + } + skipped5 = 0; + return new Promise(async (resolve) => { + var _a2; + if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { + const enhanced = enhance(image28); + const resT = model9 == null ? void 0 : model9.execute(enhanced); + lastTime5 = now(); + tf17.dispose(enhanced); + const genderT = resT.find((t2) => t2.shape[1] === 1); + const gender2 = await genderT.data(); + const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; + if (confidence > (config3.face.description.minConfidence || 0)) { + obj.gender = gender2[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); + const ageIdx = (await argmax.data())[0]; + tf17.dispose(argmax); + const ageT = resT.find((t2) => t2.shape[1] === 100); + const all2 = await ageT.data(); + obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; + if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) + log("faceres error:", { model: model9, result: resT }); + const desc = resT.find((t2) => t2.shape[1] === 1024); + const descriptor = desc ? await desc.data() : []; + obj.descriptor = Array.from(descriptor); + resT.forEach((t2) => tf17.dispose(t2)); + } + last4[idx] = obj; + lastCount2 = count2; + resolve(obj); + }); +} + +// src/face/mask.ts +var expandFact = 0.1; +var alpha = 0.5; +function insidePoly(x, y, polygon) { + let inside = false; + let j = polygon.length - 1; + for (let i = 0; i < polygon.length; j = i++) { + if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + inside = !inside; + } + return inside; +} +async function mask(face4) { + if (!face4.tensor) + return face4.tensor; + if (!face4.mesh || face4.mesh.length < 100) + return face4.tensor; + const width = face4.tensor.shape[2] || 0; + const height = face4.tensor.shape[1] || 0; + const buffer = await face4.tensor.buffer(); + let silhouette = []; + for (const pt of meshAnnotations.silhouette) + silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); + if (expandFact && expandFact > 0) + silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); + for (let x = 0; x < width; x++) { + for (let y = 0; y < height; y++) { + const inside = insidePoly(x / width, y / width, silhouette); + if (!inside) { + buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); + buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); + buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); + } + } + } + const output = buffer.toTensor(); + return output; +} + +// src/face/antispoof.ts +var tf18 = __toESM(require_tfjs_esm()); +var model10; +var cached = []; +var skipped6 = Number.MAX_SAFE_INTEGER; +var lastCount3 = 0; +var lastTime6 = 0; +async function load8(config3) { + var _a; + if (env.initial) + model10 = null; + if (!model10) + model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model10["modelUrl"]); + return model10; +} +async function predict7(image28, config3, idx, count2) { + var _a, _b; + if (!(model10 == null ? void 0 : model10["executor"])) + return 0; + const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; + const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { + skipped6++; + return cached[idx]; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const res = model10 == null ? void 0 : model10.execute(resize); + const num = (await res.data())[0]; + cached[idx] = Math.round(100 * num) / 100; + lastCount3 = count2; + lastTime6 = now(); + tf18.dispose([resize, res]); + resolve(cached[idx]); + }); +} + +// src/face/liveness.ts +var tf19 = __toESM(require_tfjs_esm()); +var model11; +var cached2 = []; +var skipped7 = Number.MAX_SAFE_INTEGER; +var lastCount4 = 0; +var lastTime7 = 0; +async function load9(config3) { + var _a; + if (env.initial) + model11 = null; + if (!model11) + model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model11["modelUrl"]); + return model11; +} +async function predict8(image28, config3, idx, count2) { + var _a, _b; + if (!(model11 == null ? void 0 : model11["executor"])) + return 0; + const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; + const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { + skipped7++; + return cached2[idx]; + } + skipped7 = 0; + return new Promise(async (resolve) => { + const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const res = model11 == null ? void 0 : model11.execute(resize); + const num = (await res.data())[0]; + cached2[idx] = Math.round(100 * num) / 100; + lastCount4 = count2; + lastTime7 = now(); + tf19.dispose([resize, res]); + resolve(cached2[idx]); + }); +} + +// src/gear/gear.ts +var tf20 = __toESM(require_tfjs_esm()); +var model12; +var last5 = []; +var raceNames = ["white", "black", "asian", "indian", "other"]; +var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; +var lastCount5 = 0; +var lastTime8 = 0; +var skipped8 = Number.MAX_SAFE_INTEGER; +async function load10(config3) { + var _a; + if (env.initial) + model12 = null; + if (!model12) + model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model12["modelUrl"]); + return model12; +} +async function predict9(image28, config3, idx, count2) { + var _a, _b; + if (!model12) + return { age: 0, gender: "unknown", genderScore: 0, race: [] }; + const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; + if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { + skipped8++; + return last5[idx]; + } + skipped8 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + if (!(model12 == null ? void 0 : model12.inputs[0].shape)) + return; + const t2 = {}; + const box = [[0, 0.1, 0.9, 0.9]]; + t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; + if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) + [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t2.gender.data(); + obj.gender = gender2[0] > gender2[1] ? "male" : "female"; + obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; + const race = await t2.race.data(); + for (let i = 0; i < race.length; i++) { + if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) + obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); + } + obj.race.sort((a, b) => b.score - a.score); + const ageDistribution = Array.from(await t2.age.data()); + const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); + let age2 = ageSorted[0][0]; + for (let i = 1; i < ageSorted.length; i++) + age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); + obj.age = Math.round(10 * age2) / 10; + Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); + last5[idx] = obj; + lastCount5 = count2; + lastTime8 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-age.ts +var tf21 = __toESM(require_tfjs_esm()); +var model13; +var last6 = []; +var lastCount6 = 0; +var lastTime9 = 0; +var skipped9 = Number.MAX_SAFE_INTEGER; +async function load11(config3) { + if (env.initial) + model13 = null; + if (!model13) + model13 = await loadModel(config3.face["ssrnet"].modelPathAge); + else if (config3.debug) + log("cached model:", model13["modelUrl"]); + return model13; +} +async function predict10(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model13) + return { age: 0 }; + const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; + if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { + skipped9++; + return last6[idx]; + } + skipped9 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) + return; + const t2 = {}; + t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t2.enhance = tf21.mul(t2.resize, constants.tf255); + const obj = { age: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.age = model13.execute(t2.enhance); + if (t2.age) { + const data = await t2.age.data(); + obj.age = Math.trunc(10 * data[0]) / 10; + } + Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); + last6[idx] = obj; + lastCount6 = count2; + lastTime9 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-gender.ts +var tf22 = __toESM(require_tfjs_esm()); +var model14; +var last7 = []; +var lastCount7 = 0; +var lastTime10 = 0; +var skipped10 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load12(config3) { + var _a; + if (env.initial) + model14 = null; + if (!model14) + model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); + else if (config3.debug) + log("cached model:", model14["modelUrl"]); + return model14; +} +async function predict11(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model14) + return { gender: "unknown", genderScore: 0 }; + const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; + if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped10++; + return last7[idx]; + } + skipped10 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model14 == null ? void 0 : model14.inputs[0].shape)) + return; + const t2 = {}; + t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t2.enhance = tf22.tidy(() => { + const [red, green, blue] = tf22.split(t2.resize, 3, 3); + const redNorm = tf22.mul(red, rgb[0]); + const greenNorm = tf22.mul(green, rgb[1]); + const blueNorm = tf22.mul(blue, rgb[2]); + const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); + const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); + return normalize2; + }); + const obj = { gender: "unknown", genderScore: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.gender = model14.execute(t2.enhance); + const data = await t2.gender.data(); + obj.gender = data[0] > data[1] ? "female" : "male"; + obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; + Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); + last7[idx] = obj; + lastCount7 = count2; + lastTime10 = now(); + resolve(obj); + }); +} + +// src/face/mobilefacenet.ts +var tf23 = __toESM(require_tfjs_esm()); +var model15; +var last8 = []; +var lastCount8 = 0; +var lastTime11 = 0; +var skipped11 = Number.MAX_SAFE_INTEGER; +async function load13(config3) { + var _a; + if (env.initial) + model15 = null; + if (!model15) + model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model15["modelUrl"]); + return model15; +} +async function predict12(input, config3, idx, count2) { + var _a, _b; + if (!(model15 == null ? void 0 : model15["executor"])) + return []; + const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; + if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { + skipped11++; + return last8[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t2.data = model15.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); + } + last8[idx] = data; + lastCount8 = count2; + lastTime11 = now(); + resolve(data); + }); +} + +// src/face/insightface.ts +var tf24 = __toESM(require_tfjs_esm()); +var model16; +var last9 = []; +var lastCount9 = 0; +var lastTime12 = 0; +var skipped12 = Number.MAX_SAFE_INTEGER; +async function load14(config3) { + if (env.initial) + model16 = null; + if (!model16) + model16 = await loadModel(config3.face["insightface"].modelPath); + else if (config3.debug) + log("cached model:", model16["modelUrl"]); + return model16; +} +async function predict13(input, config3, idx, count2) { + var _a, _b; + if (!(model16 == null ? void 0 : model16["executor"])) + return []; + const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; + if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { + skipped12++; + return last9[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t2.data = model16.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); + } + last9[idx] = data; + lastCount9 = count2; + lastTime12 = now(); + resolve(data); + }); +} + +// src/face/angles.ts +var calculateGaze = (face4) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); + const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; + const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; + const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); + strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face4, imageSize) => { + const normalize2 = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + if (Number.isNaN(thetaX)) + thetaX = 0; + if (Number.isNaN(thetaY)) + thetaY = 0; + if (Number.isNaN(thetaZ)) + thetaZ = 0; + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const mesh = face4.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); + const yAxis = normalize2(subVectors(pts[1], pts[0])); + let xAxis = normalize2(subVectors(pts[3], pts[2])); + const zAxis = normalize2(crossVectors(xAxis, yAxis)); + xAxis = crossVectors(yAxis, zAxis); + const matrix = [ + xAxis[0], + xAxis[1], + xAxis[2], + yAxis[0], + yAxis[1], + yAxis[2], + zAxis[0], + zAxis[1], + zAxis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; + +// src/face/anthropometry.ts +function calculateCameraDistance(face4, width) { + const f = face4 == null ? void 0 : face4.annotations; + if (!f) + return 0; + const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; + const cameraDistance = Math.round(1.17 / irisSize) / 100; + return cameraDistance; +} + +// src/face/face.ts +var detectFace = async (instance, input) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + let timeStamp = now(); + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let mobilefacenetRes; + let insightfaceRes; + let antispoofRes; + let livenessRes; + let descRes; + const faceRes = []; + instance.state = "run:face"; + const faces = await predict4(input, instance.config); + instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + instance.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { + const masked = await mask(faces[i]); + tf25.dispose(faces[i].tensor); + if (masked) + faces[i].tensor = masked; + } + const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; + instance.analyze("Start Emotion:"); + if (instance.config.async) { + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + } else { + instance.state = "run:emotion"; + timeStamp = now(); + emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Emotion:"); + instance.analyze("Start AntiSpoof:"); + if (instance.config.async) { + antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:antispoof"; + timeStamp = now(); + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End AntiSpoof:"); + instance.analyze("Start Liveness:"); + if (instance.config.async) { + livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:liveness"; + timeStamp = now(); + livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Liveness:"); + instance.analyze("Start GEAR:"); + if (instance.config.async) { + gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:gear"; + timeStamp = now(); + gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.gear = Math.trunc(now() - timeStamp); + } + instance.analyze("End GEAR:"); + instance.analyze("Start SSRNet:"); + if (instance.config.async) { + ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:ssrnet"; + timeStamp = now(); + ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.ssrnet = Math.trunc(now() - timeStamp); + } + instance.analyze("End SSRNet:"); + instance.analyze("Start MobileFaceNet:"); + if (instance.config.async) { + mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End MobileFaceNet:"); + instance.analyze("Start InsightFace:"); + if (instance.config.async) { + insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End InsightFace:"); + instance.analyze("Start Description:"); + if (instance.config.async) { + descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + } else { + instance.state = "run:description"; + timeStamp = now(); + descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Description:"); + if (instance.config.async) { + [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); + } + instance.analyze("Finish Face:"); + if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { + descRes = { + ...descRes, + age: ageRes.age, + gender: genderRes.gender, + genderScore: genderRes.genderScore + }; + } + if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { + descRes = { + ...descRes, + age: gearRes.age, + gender: gearRes.gender, + genderScore: gearRes.genderScore, + race: gearRes.race + }; + } + if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { + descRes.descriptor = mobilefacenetRes; + } + if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { + descRes.descriptor = insightfaceRes; + } + const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; + const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; + tf25.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + const res = { + ...faces[i], + id: i + }; + if (descRes.age) + res.age = descRes.age; + if (descRes.gender) + res.gender = descRes.gender; + if (descRes.genderScore) + res.genderScore = descRes.genderScore; + if (descRes.descriptor) + res.embedding = descRes.descriptor; + if (descRes.race) + res.race = descRes.race; + if (emotionRes) + res.emotion = emotionRes; + if (antispoofRes) + res.real = antispoofRes; + if (livenessRes) + res.live = livenessRes; + if (irisSize > 0) + res.distance = irisSize; + if (rotation) + res.rotation = rotation; + if (tensor6) + res.tensor = tensor6; + faceRes.push(res); + instance.analyze("End Face"); + } + instance.analyze("End FaceMesh:"); + if (instance.config.async) { + if (instance.performance.face) + delete instance.performance.face; + if (instance.performance.age) + delete instance.performance.age; + if (instance.performance.gender) + delete instance.performance.gender; + if (instance.performance.emotion) + delete instance.performance.emotion; + } + return faceRes; +}; + +// src/hand/fingerdef.ts +var Finger = { + thumb: 0, + index: 1, + middle: 2, + ring: 3, + pinky: 4, + all: [0, 1, 2, 3, 4], + nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, + pointsMapping: { + 0: [[0, 1], [1, 2], [2, 3], [3, 4]], + 1: [[0, 5], [5, 6], [6, 7], [7, 8]], + 2: [[0, 9], [9, 10], [10, 11], [11, 12]], + 3: [[0, 13], [13, 14], [14, 15], [15, 16]], + 4: [[0, 17], [17, 18], [18, 19], [19, 20]] + }, + getName: (value) => Finger.nameMapping[value], + getPoints: (value) => Finger.pointsMapping[value] +}; +var FingerCurl = { + none: 0, + half: 1, + full: 2, + nameMapping: { 0: "none", 1: "half", 2: "full" }, + getName: (value) => FingerCurl.nameMapping[value] +}; +var FingerDirection = { + verticalUp: 0, + verticalDown: 1, + horizontalLeft: 2, + horizontalRight: 3, + diagonalUpRight: 4, + diagonalUpLeft: 5, + diagonalDownRight: 6, + diagonalDownLeft: 7, + nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, + getName: (value) => FingerDirection.nameMapping[value] +}; +var FingerGesture = class { + constructor(name) { + __publicField(this, "name"); + __publicField(this, "curls"); + __publicField(this, "directions"); + __publicField(this, "weights"); + __publicField(this, "weightsRelative"); + this.name = name; + this.curls = {}; + this.directions = {}; + this.weights = [1, 1, 1, 1, 1]; + this.weightsRelative = [1, 1, 1, 1, 1]; + } + curl(finger, curl, confidence) { + if (typeof this.curls[finger] === "undefined") + this.curls[finger] = []; + this.curls[finger].push([curl, confidence]); + } + direction(finger, position, confidence) { + if (!this.directions[finger]) + this.directions[finger] = []; + this.directions[finger].push([position, confidence]); + } + weight(finger, weight) { + this.weights[finger] = weight; + const total = this.weights.reduce((a, b) => a + b, 0); + this.weightsRelative = this.weights.map((el) => el * 5 / total); + } + matchAgainst(detectedCurls, detectedDirections) { + let confidence = 0; + for (const fingerIdx in detectedCurls) { + const detectedCurl = detectedCurls[fingerIdx]; + const expectedCurls = this.curls[fingerIdx]; + if (typeof expectedCurls === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedCurl, score] of expectedCurls) { + if (detectedCurl === expectedCurl) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + for (const fingerIdx in detectedDirections) { + const detectedDirection = detectedDirections[fingerIdx]; + const expectedDirections = this.directions[fingerIdx]; + if (typeof expectedDirections === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedDirection, score] of expectedDirections) { + if (detectedDirection === expectedDirection) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + return confidence / 10; + } +}; + +// src/hand/fingergesture.ts +var { thumb, index, middle, ring, pinky } = Finger; +var { none, half, full } = FingerCurl; +var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; +var ThumbsUp = new FingerGesture("thumbs up"); +ThumbsUp.curl(thumb, none, 1); +ThumbsUp.direction(thumb, verticalUp, 1); +ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); +ThumbsUp.direction(thumb, diagonalUpRight, 0.25); +for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { + ThumbsUp.curl(finger, full, 1); + ThumbsUp.direction(finger, horizontalLeft, 1); + ThumbsUp.direction(finger, horizontalRight, 1); +} +var Victory = new FingerGesture("victory"); +Victory.curl(thumb, half, 0.5); +Victory.curl(thumb, none, 0.5); +Victory.direction(thumb, verticalUp, 1); +Victory.direction(thumb, diagonalUpLeft, 1); +Victory.curl(index, none, 1); +Victory.direction(index, verticalUp, 0.75); +Victory.direction(index, diagonalUpLeft, 1); +Victory.curl(middle, none, 1); +Victory.direction(middle, verticalUp, 1); +Victory.direction(middle, diagonalUpLeft, 0.75); +Victory.curl(ring, full, 1); +Victory.direction(ring, verticalUp, 0.2); +Victory.direction(ring, diagonalUpLeft, 1); +Victory.direction(ring, horizontalLeft, 0.2); +Victory.curl(pinky, full, 1); +Victory.direction(pinky, verticalUp, 0.2); +Victory.direction(pinky, diagonalUpLeft, 1); +Victory.direction(pinky, horizontalLeft, 0.2); +Victory.weight(index, 2); +Victory.weight(middle, 2); +var Point = new FingerGesture("point"); +Point.curl(thumb, full, 1); +Point.curl(index, none, 0.5); +Point.curl(middle, full, 0.5); +Point.curl(ring, full, 0.5); +Point.curl(pinky, full, 0.5); +Point.weight(index, 2); +Point.weight(middle, 2); +var MiddleFinger = new FingerGesture("middle finger"); +MiddleFinger.curl(thumb, none, 1); +MiddleFinger.curl(index, full, 0.5); +MiddleFinger.curl(middle, full, 0.5); +MiddleFinger.curl(ring, full, 0.5); +MiddleFinger.curl(pinky, full, 0.5); +MiddleFinger.weight(index, 2); +MiddleFinger.weight(middle, 2); +var OpenPalm = new FingerGesture("open palm"); +OpenPalm.curl(thumb, none, 0.75); +OpenPalm.curl(index, none, 0.75); +OpenPalm.curl(middle, none, 0.75); +OpenPalm.curl(ring, none, 0.75); +OpenPalm.curl(pinky, none, 0.75); +var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; + +// src/hand/fingerpose.ts +var minConfidence = 0.7; +var options3 = { + HALF_CURL_START_LIMIT: 60, + NO_CURL_START_LIMIT: 130, + DISTANCE_VOTE_POWER: 1.1, + SINGLE_ANGLE_VOTE_POWER: 0.9, + TOTAL_ANGLE_VOTE_POWER: 1.6 +}; +function calculateSlope(point1x, point1y, point2x, point2y) { + const value = (point1y - point2y) / (point1x - point2x); + let slope = Math.atan(value) * 180 / Math.PI; + if (slope <= 0) + slope = -slope; + else if (slope > 0) + slope = 180 - slope; + return slope; +} +function getSlopes(point1, point2) { + if (!point1 || !point2) + return [0, 0]; + const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); + if (point1.length === 2) + return slopeXY; + const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); + return [slopeXY, slopeYZ]; +} +function angleOrientationAt(angle, weightageAt = 1) { + let isVertical = 0; + let isDiagonal = 0; + let isHorizontal = 0; + if (angle >= 75 && angle <= 105) + isVertical = 1 * weightageAt; + else if (angle >= 25 && angle <= 155) + isDiagonal = 1 * weightageAt; + else + isHorizontal = 1 * weightageAt; + return [isVertical, isDiagonal, isHorizontal]; +} +function estimateFingerCurl(startPoint, midPoint, endPoint) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const start_mid_z_dist = startPoint[2] - midPoint[2]; + const start_end_z_dist = startPoint[2] - endPoint[2]; + const mid_end_z_dist = midPoint[2] - endPoint[2]; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); + let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); + if (cos_in > 1) + cos_in = 1; + else if (cos_in < -1) + cos_in = -1; + let angleOfCurve = Math.acos(cos_in); + angleOfCurve = 57.2958 * angleOfCurve % 180; + let fingerCurl; + if (angleOfCurve > options3.NO_CURL_START_LIMIT) + fingerCurl = FingerCurl.none; + else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) + fingerCurl = FingerCurl.half; + else + fingerCurl = FingerCurl.full; + return fingerCurl; +} +function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + if (max_dist_x === Math.abs(start_end_x_dist)) { + if (start_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else if (max_dist_x === Math.abs(start_mid_x_dist)) { + if (start_mid_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else { + if (mid_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } + return estimatedDirection; +} +function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { + let estimatedDirection; + if (max_dist_y === Math.abs(start_end_y_dist)) { + if (start_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else if (max_dist_y === Math.abs(start_mid_y_dist)) { + if (start_mid_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else { + if (mid_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } + return estimatedDirection; +} +function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + if (reqd_vertical_direction === FingerDirection.verticalUp) { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalUpLeft; + else + estimatedDirection = FingerDirection.diagonalUpRight; + } else { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalDownLeft; + else + estimatedDirection = FingerDirection.diagonalDownRight; + } + return estimatedDirection; +} +function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); + const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); + let voteVertical = 0; + let voteDiagonal = 0; + let voteHorizontal = 0; + const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); + if (start_end_x_y_dist_ratio > 1.5) + voteVertical += options3.DISTANCE_VOTE_POWER; + else if (start_end_x_y_dist_ratio > 0.66) + voteDiagonal += options3.DISTANCE_VOTE_POWER; + else + voteHorizontal += options3.DISTANCE_VOTE_POWER; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); + const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); + let calc_start_point_x = startPoint[0]; + let calc_start_point_y = startPoint[1]; + let calc_end_point_x = endPoint[0]; + let calc_end_point_y = endPoint[1]; + if (max_dist === start_mid_dist) { + calc_end_point_x = endPoint[0]; + calc_end_point_y = endPoint[1]; + } else if (max_dist === mid_end_dist) { + calc_start_point_x = midPoint[0]; + calc_start_point_y = midPoint[1]; + } + const calcStartPoint = [calc_start_point_x, calc_start_point_y]; + const calcEndPoint = [calc_end_point_x, calc_end_point_y]; + const totalAngle = getSlopes(calcStartPoint, calcEndPoint); + const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); + voteVertical += votes[0]; + voteDiagonal += votes[1]; + voteHorizontal += votes[2]; + for (const fingerSlope of fingerSlopes) { + const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); + voteVertical += fingerVotes[0]; + voteDiagonal += fingerVotes[1]; + voteHorizontal += fingerVotes[2]; + } + let estimatedDirection; + if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } else { + estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } + return estimatedDirection; +} +function estimate(landmarks) { + const slopesXY = []; + const slopesYZ = []; + const fingerCurls = []; + const fingerDirections = []; + if (!landmarks) + return { curls: fingerCurls, directions: fingerDirections }; + for (const finger of Finger.all) { + const points = Finger.getPoints(finger); + const slopeAtXY = []; + const slopeAtYZ = []; + for (const point2 of points) { + const point1 = landmarks[point2[0]]; + const point22 = landmarks[point2[1]]; + const slopes = getSlopes(point1, point22); + const slopeXY = slopes[0]; + const slopeYZ = slopes[1]; + slopeAtXY.push(slopeXY); + slopeAtYZ.push(slopeYZ); + } + slopesXY.push(slopeAtXY); + slopesYZ.push(slopeAtYZ); + } + for (const finger of Finger.all) { + const pointIndexAt = finger === Finger.thumb ? 1 : 0; + const fingerPointsAt = Finger.getPoints(finger); + const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; + const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; + const endPoint = landmarks[fingerPointsAt[3][1]]; + const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); + const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); + fingerCurls[finger] = fingerCurled; + fingerDirections[finger] = fingerPosition; + } + return { curls: fingerCurls, directions: fingerDirections }; +} +function analyze(keypoints) { + if (!keypoints || keypoints.length === 0) + return null; + const estimatorRes = estimate(keypoints); + const landmarks = {}; + for (const fingerIdx of Finger.all) { + landmarks[Finger.getName(fingerIdx)] = { + curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), + direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) + }; + } + return landmarks; +} +function match(keypoints) { + const poses = []; + if (!keypoints || keypoints.length === 0) + return poses; + const estimatorRes = estimate(keypoints); + for (const gesture2 of fingergesture_default) { + const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); + if (confidence >= minConfidence) + poses.push({ name: gesture2.name, confidence }); + } + return poses; +} + +// src/gesture/gesture.ts +var body2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); + } + } + return gestures; +}; +var face2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 450) { + const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); + const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; + if (Math.abs(zDiff / xDiff) <= 0.15) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); + const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2] || 0; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris2 = (res) => { + var _a, _b, _c, _d; + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > rightIrisCenterX) { + if (leftIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking right" }); + } else { + if (rightIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking left" }); + } + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + if (res[i].annotations) { + for (const [finger, pos] of Object.entries(res[i].annotations)) { + if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + if (res[i].keypoints) { + const poses = match(res[i].keypoints); + for (const pose of poses) + gestures.push({ hand: i, gesture: pose.name }); + } + } + return gestures; +}; + +// src/hand/handposedetector.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/hand/handposeutil.ts +var tf26 = __toESM(require_tfjs_esm()); +function getBoxSize2(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; +} +function getBoxCenter2(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box, image28, cropSize) { + const h = image28.shape[1]; + const w = image28.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tf26.image.cropAndResize(image28, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; +} +function enlargeBox2(box, factor = 1.5) { + const center = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function squarifyBox2(box) { + const centers = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const maxEdge = Math.max(...size2); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/hand/handposeanchors.ts +var anchors2 = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.171875, y: 0.078125 }, + { x: 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0.3125, y: 0.3125 }, + { x: 0.3125, y: 0.3125 }, + { x: 0.3125, y: 0.3125 }, + { x: 0.3125, y: 0.3125 }, + { x: 0.3125, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.4375, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.5625, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.6875, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/hand/handposedetector.ts +var HandDetector = class { + constructor(model23) { + __publicField(this, "model"); + __publicField(this, "anchors"); + __publicField(this, "anchorsTensor"); + __publicField(this, "inputSize"); + __publicField(this, "inputSizeTensor"); + __publicField(this, "doubleInputSizeTensor"); + var _a, _b, _c, _d; + this.model = model23; + this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tf27.tensor2d(this.anchors); + this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; + this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + const t2 = {}; + t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); + t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); + t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); + t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); + t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); + t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); + t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); + t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); + t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); + const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + normalizeLandmarks(rawPalmLandmarks, index2) { + const t2 = {}; + t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); + t2.div = tf27.div(t2.reshape, this.inputSizeTensor); + t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = tf27.mul(t2.landmarks, this.inputSizeTensor); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + async predict(input, config3) { + var _a; + const t2 = {}; + t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); + t2.div = tf27.div(t2.resize, constants.tf127); + t2.image = tf27.sub(t2.div, constants.tf1); + t2.batched = this.model.execute(t2.image); + t2.predictions = tf27.squeeze(t2.batched); + t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); + t2.sigmoid = tf27.sigmoid(t2.slice); + t2.scores = tf27.squeeze(t2.sigmoid); + const scores = await t2.scores.data(); + t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); + t2.norm = this.normalizeBoxes(t2.boxes); + t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t2.nms.array(); + const hands = []; + for (const index2 of nms) { + const p = {}; + p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); + p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); + p.norm = this.normalizeLandmarks(p.slice, index2); + p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); + const box = await p.box.data(); + const startPoint = box.slice(0, 2); + const endPoint = box.slice(2, 4); + const palmLandmarks = await p.palmLandmarks.array(); + const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; + const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); + hands.push(scaled); + Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); + } + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return hands; + } +}; + +// src/hand/handposepipeline.ts +var tf28 = __toESM(require_tfjs_esm()); +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var lastTime13 = 0; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + __publicField(this, "handDetector"); + __publicField(this, "handPoseModel"); + __publicField(this, "inputSize"); + __publicField(this, "storedBoxes"); + __publicField(this, "skipped"); + __publicField(this, "detectedHands"); + var _a, _b, _c; + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; + this.storedBoxes = []; + this.skipped = Number.MAX_SAFE_INTEGER; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box2, angle, rotationMatrix) { + const boxSize = getBoxSize2(box2); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box2), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image28, config3) { + let useFreshBox = false; + let boxes; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; + const skipFrame = this.skipped < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + boxes = await this.handDetector.predict(image28, config3); + this.skipped = 0; + } + if (config3.skipAllowed) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tf28.div(croppedInput, constants.tf255); + tf28.dispose(croppedInput); + tf28.dispose(rotatedImage); + const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); + lastTime13 = now(); + tf28.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tf28.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence / 4) { + const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tf28.dispose(keypoints); + tf28.dispose(keypointsReshaped); + const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords, + confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tf28.dispose(keypoints); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: 0, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, + landmarks: [] + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + if (hands.length > config3.hand.maxDetected) + hands.length = config3.hand.maxDetected; + return hands; + } +}; + +// src/hand/handpose.ts +var meshAnnotations2 = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palm: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict14(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations2 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations2)) { + annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); + } + } + const keypoints = predictions[i].landmarks; + let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw = [0, 0, 0, 0]; + if (keypoints && keypoints.length > 0) { + for (const pt of keypoints) { + if (pt[0] < box[0]) + box[0] = pt[0]; + if (pt[1] < box[1]) + box[1] = pt[1]; + if (pt[0] > box[2]) + box[2] = pt[0]; + if (pt[1] > box[3]) + box[3] = pt[1]; + } + box[2] -= box[0]; + box[3] -= box[1]; + boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; + } else { + box = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + const landmarks = analyze(keypoints); + hands.push({ + id: i, + score: Math.round(100 * predictions[i].confidence) / 100, + boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, + fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, + label: "hand", + box, + boxRaw, + keypoints, + annotations: annotations2, + landmarks + }); + } + return hands; +} +async function load15(config3) { + var _a, _b; + if (env.initial) { + handDetectorModel = null; + handPoseModel = null; + } + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, + config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null + ]); + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; + if (handDetector && handPoseModel) + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/hand/handtrack.ts +var tf29 = __toESM(require_tfjs_esm()); +var models2 = [null, null]; +var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; +var inputSize7 = [[0, 0], [0, 0]]; +var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; +var faceIndex = 4; +var boxExpandFact = 1.6; +var maxDetectorResolution = 512; +var detectorExpandFact = 1.4; +var skipped13 = Number.MAX_SAFE_INTEGER; +var lastTime14 = 0; +var outputSize = [0, 0]; +var cache4 = { + boxes: [], + hands: [] +}; +var fingerMap = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + base: [0], + palm: [0, 17, 13, 9, 5, 1, 0] +}; +async function loadDetect2(config3) { + var _a; + if (env.initial) + models2[0] = null; + if (!models2[0]) { + fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); + models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); + const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; + inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[0]["modelUrl"]); + return models2[0]; +} +async function loadSkeleton(config3) { + var _a; + if (env.initial) + models2[1] = null; + if (!models2[1]) { + models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); + const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; + inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[1]["modelUrl"]); + return models2[1]; +} +async function detectHands(input, config3) { + const hands = []; + if (!input || !models2[0]) + return hands; + const t2 = {}; + const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); + const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); + const width = Math.round(height * ratio2 / 8) * 8; + t2.resize = tf29.image.resizeBilinear(input, [height, width]); + t2.cast = tf29.cast(t2.resize, "int32"); + [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); + t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); + t2.scores = tf29.squeeze(t2.rawScores, [0]); + const classScores = tf29.unstack(t2.scores, 1); + tf29.dispose(classScores[faceIndex]); + classScores.splice(faceIndex, 1); + t2.filtered = tf29.stack(classScores, 1); + tf29.dispose(classScores); + t2.max = tf29.max(t2.filtered, 1); + t2.argmax = tf29.argMax(t2.filtered, 1); + let id = 0; + t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t2.nms.data(); + const scores = await t2.max.data(); + const classNum = await t2.argmax.data(); + for (const nmsIndex of Array.from(nms)) { + const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); + const boxYX = await boxSlice.data(); + tf29.dispose(boxSlice); + const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; + const boxRaw = scale(boxData, detectorExpandFact); + const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; + const score = scores[nmsIndex]; + const label = classes[classNum[nmsIndex]]; + const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; + hands.push(hand3); + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + hands.sort((a, b) => b.score - a.score); + if (hands.length > (config3.hand.maxDetected || 1)) + hands.length = config3.hand.maxDetected || 1; + return hands; +} +async function detectFingers(input, h, config3) { + const hand3 = { + id: h.id, + score: Math.round(100 * h.score) / 100, + boxScore: Math.round(100 * h.score) / 100, + fingerScore: 0, + box: h.box, + boxRaw: h.boxRaw, + label: h.label, + keypoints: [], + landmarks: {}, + annotations: {} + }; + if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { + const t2 = {}; + const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; + t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t2.div = tf29.div(t2.crop, constants.tf255); + [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); + const rawScore = (await t2.score.data())[0]; + const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; + if (score >= (config3.hand.minConfidence || 0)) { + hand3.fingerScore = score; + t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); + const coordsData = await t2.reshaped.array(); + const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); + const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); + hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); + hand3.landmarks = analyze(hand3.keypoints); + for (const key of Object.keys(fingerMap)) { + hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); + } + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + } + return hand3; +} +async function predict15(input, config3) { + var _a, _b; + if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) + return []; + outputSize = [input.shape[2] || 0, input.shape[1] || 0]; + skipped13++; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrame = skipped13 < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache4.hands; + } + return new Promise(async (resolve) => { + const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); + if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else { + cache4.boxes = await detectHands(input, config3); + lastTime14 = now(); + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + skipped13 = 0; + } + const oldCache = [...cache4.boxes]; + cache4.boxes.length = 0; + if (config3.cacheSensitivity > 0) { + for (let i = 0; i < cache4.hands.length; i++) { + const boxKpt = square(cache4.hands[i].keypoints, outputSize); + if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { + const boxScale = scale(boxKpt.box, boxExpandFact); + const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); + cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); + } + } + } + for (let i = 0; i < cache4.hands.length; i++) { + const bbox = calc(cache4.hands[i].keypoints, outputSize); + cache4.hands[i].box = bbox.box; + cache4.hands[i].boxRaw = bbox.boxRaw; + } + resolve(cache4.hands); + }); +} + +// src/result.ts +var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); + +// src/body/movenetcoords.ts +var movenetcoords_exports = {}; +__export(movenetcoords_exports, { + connected: () => connected3, + horizontal: () => horizontal, + kpt: () => kpt3, + relative: () => relative, + vertical: () => vertical +}); +var kpt3 = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var horizontal = [ + ["leftEye", "rightEye"], + ["leftEar", "rightEar"], + ["leftShoulder", "rightShoulder"], + ["leftElbow", "rightElbow"], + ["leftWrist", "rightWrist"], + ["leftHip", "rightHip"], + ["leftKnee", "rightKnee"], + ["leftAnkle", "rightAnkle"] +]; +var vertical = [ + ["leftKnee", "leftShoulder"], + ["rightKnee", "rightShoulder"], + ["leftAnkle", "leftKnee"], + ["rightAnkle", "rightKnee"] +]; +var relative = [ + [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], + [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] +]; +var connected3 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/util/interpolate.ts +var bufferedResult = empty(); +var interpolateTime = 0; +function calc2(newResult, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + const t0 = now(); + if (!newResult) + return empty(); + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; + if (newResult.canvas) + bufferedResult.canvas = newResult.canvas; + if (newResult.error) + bufferedResult.error = newResult.error; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { + var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + return { + score: newKpt.score, + part: newKpt.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + ], + distance: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + ] + }; + }); + const annotations2 = {}; + let coords = { connected: {} }; + if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) + coords = efficientposecoords_exports; + else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + coords = blazeposecoords_exports; + else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) + coords = movenetcoords_exports; + for (const [name, indexes] of Object.entries(coords.connected)) { + const pt = []; + for (let j = 0; j < indexes.length - 1; j++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[j]); + const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) + bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + let annotations2 = {}; + if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { + bufferedResult.hand[i].annotations = newResult.hand[i].annotations; + annotations2 = bufferedResult.hand[i].annotations; + } else if (newResult.hand[i].annotations) { + for (const key of Object.keys(newResult.hand[i].annotations)) { + annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + } + } + bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + if (newResult.face[i].rotation) { + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; + } else { + bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; + } + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + bufferedResult.width = newResult.width; + bufferedResult.height = newResult.height; + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); + if (newResult.performance) + bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; + return bufferedResult; +} + +// src/segmentation/meet.ts +var tf30 = __toESM(require_tfjs_esm()); +var model17; +async function load16(config3) { + if (!model17 || env.initial) + model17 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model17["modelUrl"]); + return model17; +} +async function predict16(input, config3) { + var _a; + if (!model17) + model17 = await load16(config3); + if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t2.norm = tf30.div(t2.resize, constants.tf255); + t2.res = model17.execute(t2.norm); + t2.squeeze = tf30.squeeze(t2.res, [0]); + [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); + t2.fg = tf30.softmax(t2.fgRaw); + t2.mul = tf30.mul(t2.fg, constants.tf255); + t2.expand = tf30.expandDims(t2.mul, 2); + t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf30.squeeze(input); + t2.concat = tf30.concat([t2.input, t2.output], -1); + rgba = tf30.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf30.cast(t2.output, "int32"); + break; + default: + rgba = tf30.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); + return rgba; +} + +// src/face/match.ts +var match_exports = {}; +__export(match_exports, { + distance: () => distance, + find: () => find, + similarity: () => similarity +}); +function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { + if (!descriptor1 || !descriptor1) + return Number.MAX_SAFE_INTEGER; + let sum3 = 0; + for (let i = 0; i < descriptor1.length; i++) { + const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); + sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; + } + return (options4.multiplier || 20) * sum3; +} +var normalizeDistance = (dist, order, min2, max5) => { + if (dist === 0) + return 1; + const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); + const norm = (1 - root / 100 - min2) / (max5 - min2); + const clamp2 = Math.max(Math.min(norm, 1), 0); + return clamp2; +}; +function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { + const dist = distance(descriptor1, descriptor2, options4); + return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); +} +function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { + if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { + return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; + } + let lowestDistance = Number.MAX_SAFE_INTEGER; + let index2 = -1; + for (let i = 0; i < descriptors.length; i++) { + const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; + if (res < lowestDistance) { + lowestDistance = res; + index2 = i; + } + if (lowestDistance < (options4.threshold || 0)) + break; + } + const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); + return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; +} + +// src/models.ts +var models_exports2 = {}; +__export(models_exports2, { + Models: () => Models, + validateModel: () => validateModel +}); + +// src/body/movenet.ts +var tf32 = __toESM(require_tfjs_esm()); + +// src/body/movenetfix.ts +var tf31 = __toESM(require_tfjs_esm()); +var maxJitter = 5e-3; +var cache5 = { + keypoints: [], + padding: [[0, 0], [0, 0], [0, 0], [0, 0]] +}; +function bodyParts(body4) { + for (const pair of horizontal) { + const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); + if (body4.keypoints[left] && body4.keypoints[right]) { + if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } + } + for (const pair of vertical) { + const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + if (body4.keypoints[lower] && body4.keypoints[higher]) { + if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { + body4.keypoints.splice(lower, 1); + } + } + } + for (const [pair, compare2] of relative) { + const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); + const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); + if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) + continue; + const distanceLeft = body4.keypoints[left] ? [ + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) + ] : [0, 0]; + const distanceRight = body4.keypoints[right] ? [ + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) + ] : [0, 0]; + if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } +} +function jitter(keypoints) { + for (let i = 0; i < keypoints.length; i++) { + if (keypoints[i] && cache5.keypoints[i]) { + const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; + if (diff[0] < maxJitter && diff[1] < maxJitter) { + keypoints[i] = cache5.keypoints[i]; + } else { + cache5.keypoints[i] = keypoints[i]; + } + } else { + cache5.keypoints[i] = keypoints[i]; + } + } + return keypoints; +} +function padInput(input, inputSize10) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + cache5.padding = [ + [0, 0], + [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], + [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], + [0, 0] + ]; + t2.pad = tf31.pad(input, cache5.padding); + t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); + const final = tf31.cast(t2.resize, "int32"); + Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); + return final; +} +function rescaleBody(body4, outputSize2) { + body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); + for (const kpt4 of body4.keypoints) { + kpt4.position = [ + kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], + kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] + ]; + kpt4.positionRaw = [ + kpt4.position[0] / outputSize2[0], + kpt4.position[1] / outputSize2[1] + ]; + } + const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); + body4.box = rescaledBoxes.box; + body4.boxRaw = rescaledBoxes.boxRaw; + return body4; +} + +// src/body/movenet.ts +var model18; +var inputSize8 = 0; +var skipped14 = Number.MAX_SAFE_INTEGER; +var cache6 = { + boxes: [], + bodies: [], + last: 0 +}; +async function load17(config3) { + var _a; + if (env.initial) + model18 = null; + if (!model18) { + fakeOps(["size"], config3); + model18 = await loadModel(config3.body.modelPath); + } else if (config3.debug) + log("cached model:", model18["modelUrl"]); + inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; + if (inputSize8 < 64) + inputSize8 = 256; + return model18; +} +function parseSinglePose(res, config3, image28) { + const kpt4 = res[0][0]; + const keypoints = []; + let score = 0; + for (let id = 0; id < kpt4.length; id++) { + score = kpt4[id][2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[id][1], kpt4[id][0]]; + keypoints.push({ + score: Math.round(100 * score) / 100, + part: kpt3[id], + positionRaw, + position: [ + Math.round((image28.shape[2] || 0) * positionRaw[0]), + Math.round((image28.shape[1] || 0) * positionRaw[1]) + ] + }); + } + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const bodies = []; + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + return bodies; +} +function parseMultiPose(res, config3, image28) { + const bodies = []; + for (let id = 0; id < res[0].length; id++) { + const kpt4 = res[0][id]; + const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; + if (totalScore > config3.body.minConfidence) { + const keypoints = []; + for (let i = 0; i < 17; i++) { + const score = kpt4[3 * i + 2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; + keypoints.push({ + part: kpt3[i], + score: Math.round(100 * score) / 100, + positionRaw, + position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] + }); + } + } + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + } + } + bodies.sort((a, b) => b.score - a.score); + if (bodies.length > config3.body.maxDetected) + bodies.length = config3.body.maxDetected; + return bodies; +} +async function predict17(input, config3) { + var _a; + if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) + return []; + if (!config3.skipAllowed) + cache6.boxes.length = 0; + skipped14++; + const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; + const skipFrame = skipped14 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache6.bodies; + } + return new Promise(async (resolve) => { + const t2 = {}; + skipped14 = 0; + t2.input = padInput(input, inputSize8); + t2.res = model18 == null ? void 0 : model18.execute(t2.input); + cache6.last = now(); + const res = await t2.res.array(); + cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + for (const body4 of cache6.bodies) { + rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); + jitter(body4.keypoints); + } + Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); + resolve(cache6.bodies); + }); +} + +// src/object/nanodet.ts +var tf33 = __toESM(require_tfjs_esm()); +var model19; +var last10 = []; +var lastTime15 = 0; +var skipped15 = Number.MAX_SAFE_INTEGER; +var inputSize9 = 0; +var scaleBox = 2.5; +async function load18(config3) { + if (!model19 || env.initial) { + model19 = await loadModel(config3.object.modelPath); + const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; + inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; + } else if (config3.debug) + log("cached model:", model19["modelUrl"]); + return model19; +} +async function process4(res, outputShape, config3) { + var _a, _b; + let id = 0; + let results = []; + const size2 = inputSize9; + for (const strideSize of [1, 2, 4]) { + const baseSize = strideSize * 13; + const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scores = await scoresT.array(); + const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); + const boxIdxT = tf33.argMax(boxesMaxT, 2); + const boxIdx = await boxIdxT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { + const score = scores[i][j]; + if (score > (config3.object.minConfidence || 0) && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw = [x, y, w, h]; + boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); + const box = [ + boxRaw[0] * outputShape[0], + boxRaw[1] * outputShape[1], + boxRaw[2] * outputShape[0], + boxRaw[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score) / 100, + class: j + 1, + label: labels2[j].label, + box: box.map((a) => Math.trunc(a)), + boxRaw + }; + results.push(result); + } + } + } + tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); + } + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = Array.from(await nms.data()); + tf33.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict18(image28, config3) { + if (!(model19 == null ? void 0 : model19["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; + const skipFrame = skipped15 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { + skipped15++; + return last10; + } + skipped15 = 0; + if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) + return last10; + return new Promise(async (resolve) => { + const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; + const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); + const normT = tf33.div(resizeT, constants.tf255); + const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); + let objectT; + if (config3.object.enabled) + objectT = model19.execute(transposeT); + lastTime15 = now(); + const obj = await process4(objectT, outputSize2, config3); + last10 = obj; + tf33.dispose([resizeT, normT, transposeT, ...objectT]); + resolve(obj); + }); +} + +// src/body/posenet.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/body/posenetutils.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; +function getBoundingBox(keypoints) { + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score, part, position }) => ({ + score, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })), + annotations: {} + }); + const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + __publicField(this, "priorityQueue"); + __publicField(this, "numberOfElements"); + __publicField(this, "getElementValue"); + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max5 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max5; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t2 = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t2; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min2, max5) { + if (a < min2) + return min2; + if (a > max5) + return max5; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/body/posenet.ts +var model20; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors( + { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, + { x: offsetPoint.x, y: offsetPoint.y } + ); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints; +} +function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence2, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < minConfidence2) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) + queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses, { x, y }, keypointId) { + return poses.some(({ keypoints }) => { + var _a; + const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints) { + const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { + const poses = []; + const queue = buildPartWithScoreQueue(minConfidence2, scores); + while (poses.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses, rootImageCoords, root.part.id)) + continue; + let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints = keypoints.filter((a) => a.score > minConfidence2); + const score = getInstanceScore(poses, keypoints); + const box = getBoundingBox(keypoints); + if (score > minConfidence2) + poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); + } + return poses; +} +async function predict19(input, config3) { + if (!(model20 == null ? void 0 : model20["executor"])) + return []; + const res = tf34.tidy(() => { + if (!model20.inputs[0].shape) + return []; + const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); + const results = model20.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tf34.squeeze(y, [0])); + results3d[1] = tf34.sigmoid(results3d[1]); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); + for (const t2 of res) + tf34.dispose(t2); + const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model20.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + return scaled; +} +async function load19(config3) { + if (!model20 || env.initial) + model20 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model20["modelUrl"]); + return model20; +} + +// src/segmentation/rvm.ts +var tf35 = __toESM(require_tfjs_esm()); +var model21; +var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; +var t = {}; +var ratio = 0; +function init3(config3) { + tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + t.r1i = tf35.tensor(0); + t.r2i = tf35.tensor(0); + t.r3i = tf35.tensor(0); + t.r4i = tf35.tensor(0); + ratio = config3.segmentation.ratio || 0.5; + t.downsample_ratio = tf35.tensor(ratio); +} +async function load20(config3) { + if (!model21 || env.initial) + model21 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model21["modelUrl"]); + init3(config3); + return model21; +} +var normalize = (r) => tf35.tidy(() => { + const squeeze14 = tf35.squeeze(r, [0]); + const mul15 = tf35.mul(squeeze14, constants.tf255); + const cast8 = tf35.cast(mul15, "int32"); + return cast8; +}); +function getRGBA(fgr, pha) { + const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = tf35.concat([rgb2, a], -1); + tf35.dispose([rgb2, a]); + return rgba; +} +function getState(state) { + return tf35.tidy(() => { + const r = {}; + r.unstack = tf35.unstack(state, -1); + r.concat = tf35.concat(r.unstack, 1); + r.split = tf35.split(r.concat, 4, 1); + r.stack = tf35.concat(r.split, 2); + r.squeeze = tf35.squeeze(r.stack, [0]); + r.expand = tf35.expandDims(r.squeeze, -1); + r.add = tf35.add(r.expand, 1); + r.mul = tf35.mul(r.add, 127.5); + r.cast = tf35.cast(r.mul, "int32"); + r.tile = tf35.tile(r.cast, [1, 1, 3]); + r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return tf35.concat([r.tile, r.alpha], -1); + }); +} +async function predict20(input, config3) { + if (!model21) + model21 = await load20(config3); + if (!(model21 == null ? void 0 : model21["executor"])) + return null; + t.src = tf35.div(input, 255); + if (ratio !== config3.segmentation.ratio) + init3(config3); + const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + rgba = getRGBA(fgr, pha); + break; + case "alpha": + rgba = getRGBA(null, pha); + break; + case "foreground": + rgba = getRGBA(fgr, null); + break; + case "state": + rgba = getState(r1o); + break; + default: + rgba = tf35.tensor(0); + } + tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; + return rgba; +} + +// src/segmentation/selfie.ts +var tf36 = __toESM(require_tfjs_esm()); +var model22; +async function load21(config3) { + if (!model22 || env.initial) + model22 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model22["modelUrl"]); + return model22; +} +async function predict21(input, config3) { + var _a; + if (!model22) + model22 = await load21(config3); + if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t2.norm = tf36.div(t2.resize, constants.tf255); + t2.res = model22.execute(t2.norm); + t2.squeeze = tf36.squeeze(t2.res, [0]); + t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t2.mul = tf36.mul(t2.alpha, constants.tf255); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf36.squeeze(input); + t2.concat = tf36.concat([t2.input, t2.mul], -1); + rgba = tf36.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf36.cast(t2.mul, "int32"); + break; + default: + rgba = tf36.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); + return rgba; +} + +// src/models.ts +function validateModel(instance, model23, name) { + var _a, _b; + if (!model23) + return null; + if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) + return null; + const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; + const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; + const ops = []; + const missing = []; + const url = model23["modelUrl"]; + const executor = model23["executor"]; + if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { + for (const kernel of Object.values(executor.graph.nodes)) { + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); + } + } else { + if (!executor && instance.config.debug) { + log("model not loaded", name); + } + } + for (const op of ops) { + if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); + } + } + if (instance.config.debug && missing.length > 0) + log("model validation failed:", name, missing); + return missing.length > 0 ? { name, missing, ops, url } : null; +} +var Models = class { + constructor(currentInstance) { + __publicField(this, "instance"); + __publicField(this, "models", {}); + this.models = {}; + this.instance = currentInstance; + } + stats() { + let totalSizeFromManifest = 0; + let totalSizeWeights = 0; + let totalSizeLoading = 0; + for (const m of Object.values(modelStats)) { + totalSizeFromManifest += m.sizeFromManifest; + totalSizeWeights += m.sizeLoadedWeights; + totalSizeLoading += m.sizeDesired; + } + const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; + return { + numLoadedModels: Object.values(modelStats).length, + numDefinedModels: Object.keys(this.models).length, + percentageLoaded, + totalSizeFromManifest, + totalSizeWeights, + totalSizeLoading, + modelStats: Object.values(modelStats) + }; + } + reset() { + for (const model23 of Object.keys(this.models)) + this.models[model23] = null; + } + async load(instance) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; + if (env.initial) + this.reset(); + if (instance) + this.instance = instance; + const m = {}; + m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; + m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; + m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; + m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; + m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; + m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; + m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; + m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; + m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; + m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; + m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; + m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; + m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; + m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; + m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; + m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; + m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; + if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) + [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; + m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; + m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; + m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; + m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; + for (const [model23, promise] of Object.entries(m)) { + if (promise == null ? void 0 : promise["then"]) + promise["then"]((val) => this.models[model23] = val); + } + await Promise.all(Object.values(m)); + } + list() { + const models3 = Object.keys(this.models).map((model23) => { + var _a; + return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; + }); + for (const m of models3) { + const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); + if (!stats) + continue; + m.size = modelStats[stats].sizeLoadedWeights; + m.url = modelStats[stats].url; + } + return models3; + } + loaded() { + const list = this.list(); + const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); + return loaded; + } + validate() { + const missing = []; + for (const defined of Object.keys(this.models)) { + const model23 = this.models[defined]; + if (!model23) + continue; + const res = validateModel(this.instance, model23, defined); + if (res) + missing.push(res); + } + return missing; + } +}; + +// src/util/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f; + let id = 0; + const persons = []; + for (const face4 of faces) { + const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture2 of gestures) { + if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) + person2.gestures.push(gesture2); + } + const x = []; + const y = []; + const extractXY = (box) => { + if (box && box.length === 4) { + x.push(box[0], box[0] + box[2]); + y.push(box[1], box[1] + box[3]); + } + }; + extractXY(person2.face.box); + extractXY((_d = person2.body) == null ? void 0 : _d.box); + extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); + extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons.push(person2); + } + return persons; +} + +// src/warmup.ts +var tf37 = __toESM(require_tfjs_esm()); + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -269,7 +14134,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Ht=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -837,4 +14703,563 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Bt);break;case"body":case"full":n=await t(Ht);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Aa(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Bt;break;case"full":case"body":n="data:image/jpeg;base64,"+Ht;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=te(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function aa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Bt):n=t(Ht);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ia(e){let t;return typeof createImageBitmap=="function"?t=await sa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await Aa(e):t=await aa(e),t}async function la(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let d=(y==null?void 0:y.modelSignature)&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])==null?void 0:l.shape)?[...y.inputs[0].shape]:[1,64,64,3],p=(y==null?void 0:y.modelSignature)&&((x=(c=y==null?void 0:y.inputs)==null?void 0:c[0])==null?void 0:x.dtype)?y.inputs[0].dtype:"float32";for(let b=0;bd0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function jo(e,t){await j2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?he():new Promise(async o=>{await e.models.load(),await la(e);let r=await ia(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var M2,B2,H2,Gt,Ue,v1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",et);k(this,"match",Nt);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");me(this,M2,void 0);me(this,B2,void 0);me(this,H2,void 0);k(this,"analyze",(...t)=>{if(!G0(this,B2))return;let n=this.tf.engine().state.numTensors,o=G0(this,M2);ge(this,M2,n);let r=n-o;r!==0&&u(...t,r)});me(this,Gt,t=>{if(!G0(this,H2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new U2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});me(this,Ue,{});let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Ye.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ye.modelBasePath=R.browser?"../models/":"file://models/",this.version=_t,Object.defineProperty(this,"version",{value:_t}),this.config=JSON.parse(JSON.stringify(Ye)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),F1(this.config),this.tf=ae,this.state="idle",ge(this,M2,0),ge(this,B2,!1),ge(this,H2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new F2(this),s5(),this.result=he(),this.process={tensor:null,canvas:null},this.faceTriangulation=j3,this.faceUVMap=N3,Ft(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ye)),this.config.backend=t,Jt(),R.initial=!0}validate(t){let n=Ut(Ye,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return X2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await X2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await ko(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await no(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Eo(o.tensor,this.config)),ae.dispose(o.tensor),r}compare(t,n){return D1(this.config,t,n)}async init(){await j2(this,!0),await this.tf.ready(),Jt()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await j2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return to(t,this.config)}async warmup(t){let n=g(),o=await jo(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,I,q,t0,G,$,A0,v,V,n0,U,g0,m0,B,X;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=G0(this,Gt).call(this,t);s&&(u(s,t),this.emit("error"),o(he(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await X2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(he("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await W1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?p1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?c5(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?u5(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?l1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await p1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await c5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await u5(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await l1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((t0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?Q5(a.tensor,d):[]:($=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?e1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(v=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&v.includes("handdetect")?x=this.config.hand.enabled?await Q5(a.tensor,d):[]:(n0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&n0.includes("handtrack")&&(x=this.config.hand.enabled?await e1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?i=this.config.object.enabled?d1(a.tensor,this.config):[]:(g0=this.config.object.modelPath)!=null&&g0.includes("centernet")&&(i=this.config.object.enabled?y5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(m0=this.config.object.modelPath)!=null&&m0.includes("nanodet")?i=this.config.object.enabled?await d1(a.tensor,this.config):[]:(B=this.config.object.modelPath)!=null&&B.includes("centernet")&&(i=this.config.object.enabled?await y5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Sn(l),...zn(c),...Nn(x),...jn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let f=((X=this.process.tensor)==null?void 0:X.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return So(l,c,x,p,f)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,Ue)[t.id]||(this.config.debug&&u("video start",t.id),G0(this,Ue)[t.id]=!0),!t.paused&&G0(this,Ue)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,Ue)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),G0(this,Ue)[t.id]=!1)}};M2=new WeakMap,B2=new WeakMap,H2=new WeakMap,Gt=new WeakMap,Ue=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); +2Q==`; + +// src/warmup.ts +async function warmupBitmap(instance) { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (instance.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "body": + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await instance.detect(bitmap, instance.config); + bitmap.close(); + } + return res; +} +async function warmupCanvas(instance) { + return new Promise((resolve) => { + let src; + switch (instance.config.warmup) { + case "face": + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + src = "data:image/jpeg;base64," + body3; + break; + default: + src = ""; + } + let img; + if (typeof Image !== "undefined") + img = new Image(); + else if (env.Image) + img = new env.Image(); + else { + resolve(void 0); + return; + } + img.onload = async () => { + const canvas3 = canvas(img.naturalWidth, img.naturalHeight); + if (!canvas3) { + log("Warmup: Canvas not found"); + resolve(void 0); + } else { + const ctx = canvas3.getContext("2d"); + if (ctx) + ctx.drawImage(img, 0, 0); + const tensor6 = await instance.image(canvas3, true); + const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; + resolve(res); + } + }; + if (src) + img.src = src; + else + resolve(void 0); + }); +} +async function warmupNode(instance) { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (instance.config.warmup === "face") + img = atob(face3); + else + img = atob(body3); + let res; + if ("node" in tf37 && tf37.getBackend() === "tensorflow") { + const data = tf37["node"].decodeJpeg(img); + const expanded = tf37.expandDims(data, 0); + instance.tf.dispose(data); + res = await instance.detect(expanded, instance.config); + instance.tf.dispose(expanded); + } else { + if (instance.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; +} +async function runInference(instance) { + let res; + if (typeof createImageBitmap === "function") + res = await warmupBitmap(instance); + else if (typeof Image !== "undefined" || env.Canvas !== void 0) + res = await warmupCanvas(instance); + else + res = await warmupNode(instance); + return res; +} +async function runCompile(instance) { + var _a, _b, _c, _d; + if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) + return; + const backendType = tf37.getBackend(); + const webGLBackend = tf37.backend(); + if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { + return; + } + tf37.env().set("ENGINE_COMPILE_ONLY", true); + const numTensorsStart = tf37.engine().state.numTensors; + const compiledModels = []; + for (const [modelName, model23] of Object.entries(instance.models.models)) { + if (!model23) + continue; + const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; + const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; + for (let dim = 0; dim < shape.length; dim++) { + if (shape[dim] === -1) + shape[dim] = dim === 0 ? 1 : 64; + } + const tensor6 = tf37.zeros(shape, dtype); + try { + const res = model23.execute(tensor6); + compiledModels.push(modelName); + if (Array.isArray(res)) + res.forEach((t2) => tf37.dispose(t2)); + else + tf37.dispose(res); + } catch (e) { + if (instance.config.debug) + log("compile fail model:", modelName); + } + tf37.dispose(tensor6); + } + const kernels = await webGLBackend["checkCompileCompletionAsync"](); + webGLBackend["getUniformLocations"](); + if (instance.config.debug) + log("compile pass:", { models: compiledModels, kernels: kernels.length }); + tf37.env().set("ENGINE_COMPILE_ONLY", false); + const numTensorsEnd = tf37.engine().state.numTensors; + if (numTensorsEnd - numTensorsStart > 0) + log("tensor leak:", numTensorsEnd - numTensorsStart); +} +async function warmup(instance, userConfig) { + await check(instance, false); + const t0 = now(); + instance.state = "warmup"; + if (userConfig) + instance.config = mergeDeep(instance.config, userConfig); + if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { + return empty(); + } + return new Promise(async (resolve) => { + await instance.models.load(); + await runCompile(instance); + const res = await runInference(instance); + const t1 = now(); + if (instance.config.debug) + log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); + instance.emit("warmup"); + resolve(res); + }); +} + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; +var Human = class { + constructor(userConfig) { + __publicField(this, "version"); + __publicField(this, "config"); + __publicField(this, "result"); + __publicField(this, "state"); + __publicField(this, "process"); + __publicField(this, "tf"); + __publicField(this, "env", env); + __publicField(this, "draw", draw_exports); + __publicField(this, "match", match_exports); + __publicField(this, "models"); + __publicField(this, "events"); + __publicField(this, "faceTriangulation"); + __publicField(this, "faceUVMap"); + __publicField(this, "performance"); + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __publicField(this, "analyze", (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }); + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.env.node && !(input instanceof tf38.Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __publicField(this, "webcam", new WebCam()); + __publicField(this, "emit", (event) => { + var _a; + if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) + this.events.dispatchEvent(new Event(event)); + }); + __privateAdd(this, _loops, {}); + const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); + config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; + config.modelBasePath = env.browser ? "../models/" : "file://models/"; + this.version = version2; + Object.defineProperty(this, "version", { value: version2 }); + this.config = JSON.parse(JSON.stringify(config)); + Object.seal(this.config); + this.config.cacheModels = typeof indexedDB !== "undefined"; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + setModelLoadOptions(this.config); + this.tf = tf38; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + this.performance = {}; + this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; + this.models = new Models(this); + init2(); + this.result = empty(); + this.process = { tensor: null, canvas: null }; + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + validateModel(this, null, ""); + this.emit("create"); + if (this.config.debug || this.env.browser) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version["tfjs-core"]}`); + const envTemp = JSON.parse(JSON.stringify(this.env)); + delete envTemp.kernels; + delete envTemp.initial; + delete envTemp.perfadd; + if (this.config.debug) + log("environment:", envTemp); + } + reset() { + const currentBackend = this.config.backend; + this.config = JSON.parse(JSON.stringify(config)); + this.config.backend = currentBackend; + reset(); + env.initial = true; + } + validate(userConfig) { + const msgs = validate(config, userConfig || this.config); + if (msgs.length === 0) + this.config = mergeDeep(this.config, userConfig); + return msgs; + } + now() { + return now(); + } + image(input, getTensor = false) { + return process2(input, this.config, getTensor); + } + async segmentation(input, userConfig) { + var _a, _b, _c; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (!this.config.segmentation.enabled) + return null; + const processed = await process2(input, this.config); + if (!processed.tensor) + return null; + let tensor6 = null; + if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) + tensor6 = await predict20(processed.tensor, this.config); + if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) + tensor6 = await predict16(processed.tensor, this.config); + if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) + tensor6 = await predict21(processed.tensor, this.config); + tf38.dispose(processed.tensor); + return tensor6; + } + compare(firstImageTensor, secondImageTensor) { + return compare(this.config, firstImageTensor, secondImageTensor); + } + async init() { + await check(this, true); + await this.tf.ready(); + reset(); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + const count2 = Object.values(this.models.models).filter((model23) => model23).length; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (this.env.initial) { + if (!await check(this, false)) + log("error: backend check failed"); + await tf38.ready(); + if (this.env.browser) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("tf flags:", this.tf.ENV.flags); + } + } + await this.models.load(this); + if (this.env.initial && this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + this.env.initial = false; + const loaded = Object.values(this.models.models).filter((model23) => model23).length; + if (loaded !== count2) { + this.models.validate(); + this.emit("load"); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.loadModels || 0)) + this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; + } + next(result = this.result) { + return calc2(result, this.config); + } + async warmup(userConfig) { + const t0 = now(); + const res = await warmup(this, userConfig); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t0); + return res; + } + async profile(input, userConfig) { + const profile = await this.tf.profile(() => this.detect(input, userConfig)); + const kernels = {}; + let total = 0; + for (const kernel of profile.kernels) { + const ms = Number(kernel.kernelTimeMs) || 0; + if (kernels[kernel.name]) + kernels[kernel.name] += ms; + else + kernels[kernel.name] = ms; + total += ms; + } + const kernelArr = []; + Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); + for (const kernel of kernelArr) { + kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; + kernel.time = Math.round(1e3 * kernel.time) / 1e3; + } + kernelArr.sort((a, b) => b.time - a.time); + kernelArr.length = 20; + return kernelArr; + } + async detect(input, userConfig) { + this.state = "detect"; + return new Promise(async (resolve) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; + this.state = "config"; + let timeStamp; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + this.emit("error"); + resolve(empty(error)); + } + const timeStart = now(); + await this.load(); + timeStamp = now(); + this.state = "image"; + const img = await process2(input, this.config); + this.process = img; + this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (!img.tensor) { + if (this.config.debug) + log("could not convert input to tensor"); + this.emit("error"); + resolve(empty("could not convert input to tensor")); + return; + } + this.emit("image"); + timeStamp = now(); + this.config.skipAllowed = await skip(this.config, img.tensor); + this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; + if (!this.performance.totalFrames) + this.performance.totalFrames = 0; + if (!this.performance.cachedFrames) + this.performance.cachedFrames = 0; + this.performance.totalFrames++; + if (this.config.skipAllowed) + this.performance.cachedFrames++; + this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes = []; + let bodyRes = []; + let handRes = []; + let objectRes = []; + this.state = "detect:face"; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; + this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) + faceRes = await faceRes; + this.analyze("Start Body:"); + this.state = "detect:body"; + const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) + bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; + else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; + else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; + else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) + bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; + if (this.performance.body) + delete this.performance.body; + } else { + timeStamp = now(); + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; + else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; + else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; + else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; + this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + this.state = "detect:hand"; + const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) + handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; + else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) + handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + timeStamp = now(); + if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) + handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; + else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) + handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; + this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + this.state = "detect:object"; + if (this.config.async) { + if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) + objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; + else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) + objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + timeStamp = now(); + if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; + else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) + objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; + this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Object:"); + this.state = "detect:await"; + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + this.state = "detect:gesture"; + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; + if (!this.config.async) + this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); + const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: this.process.canvas, + timestamp: Date.now(), + error: null, + width: shape[2], + height: shape[1], + get persons() { + return join2(faceRes, bodyRes, handRes, gestureRes, shape); + } + }; + tf38.dispose(img.tensor); + this.emit("detect"); + this.state = "idle"; + resolve(this.result); + }); + } + async sleep(ms) { + return new Promise((resolve) => { + setTimeout(resolve, ms); + }); + } + async video(element, run = true, delay = 0) { + if (run) { + if (!__privateGet(this, _loops)[element.id]) { + if (this.config.debug) + log("video start", element.id); + __privateGet(this, _loops)[element.id] = true; + } + if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) + await this.detect(element); + if (delay > 0) + await this.sleep(delay); + if (__privateGet(this, _loops)[element.id]) + requestAnimationFrame(() => this.video(element, run, delay)); + } else { + if (this.config.debug) + log("video stop", element.id); + __privateGet(this, _loops)[element.id] = false; + } + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_sanity = new WeakMap(); +_loops = new WeakMap(); +// Annotate the CommonJS export names for ESM import in node: +0 && (module.exports = { + Env, + Human, + defaults, + draw, + empty, + env, + match, + models +}); diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js index 86143ffd..724c66ea 100644 --- a/dist/human.node-wasm.js +++ b/dist/human.node-wasm.js @@ -4,7 +4,318 @@ author: ' */ -"use strict";var Io=Object.create;var k2=Object.defineProperty;var Oo=Object.getOwnPropertyDescriptor;var Lo=Object.getOwnPropertyNames;var Co=Object.getPrototypeOf,Wo=Object.prototype.hasOwnProperty;var Do=(e,t,n)=>t in e?k2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Fo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ze=(e,t)=>{for(var n in t)k2(e,n,{get:t[n],enumerable:!0})},w1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Lo(t))!Wo.call(e,r)&&r!==n&&k2(e,r,{get:()=>t[r],enumerable:!(o=Oo(t,r))||o.enumerable});return e};var Z=(e,t,n)=>(n=e!=null?Io(Co(e)):{},w1(t||!e||!e.__esModule?k2(n,"default",{value:e,enumerable:!0}):n,e)),Bo=e=>w1(k2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Do(e,typeof t!="symbol"?t+"":t,n),n),E1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var G0=(e,t,n)=>(E1(e,t,"read from private field"),n?n.call(e):t.get(e)),me=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},ge=(e,t,n,o)=>(E1(e,t,"write to private 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da={};ze(da,{Env:()=>z2,Human:()=>R1,default:()=>R1,defaults:()=>Ye,draw:()=>nt,empty:()=>he,env:()=>R,match:()=>Ot,models:()=>v1});module.exports=Bo(da);var ae=Z(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function S1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Yt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Yt(e[r],t[r],r,o);else{let s=e&&typeof 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Ye={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var I0=Z(H());var N=Z(H());var j1=` +"use strict"; +var __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; +var __commonJS = (cb, mod3) => function __require() { + return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; +}; +var __export = (target, all2) => { + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( + isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, + mod3 +)); +var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); +var __publicField = (obj, key, value) => { + __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); + return value; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all2) => { + for (var name in all2) + __defProp2(target, name, { get: all2[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); + var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); + var tf_node_wasm_exports = {}; + __export2(tf_node_wasm_exports, { + version: () => version7 + }); + module2.exports = __toCommonJS2(tf_node_wasm_exports); + __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-core"), module2.exports); + __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-converter"), module2.exports); + __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports); + var version4 = "4.2.0"; + var version22 = "4.2.0"; + var version32 = "4.2.0"; + var version42 = "4.2.0"; + var version5 = "4.2.0"; + var version6 = "0.0.1-alpha.17"; + var version7 = { + tfjs: version4, + "tfjs-core": version4, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version42, + "tfjs-backend-wasm": version5, + "tfjs-backend-webgpu": version6 + }; + } +}); + +// src/human.ts +var human_exports = {}; +__export(human_exports, { + Env: () => Env, + Human: () => Human, + default: () => Human, + defaults: () => config, + draw: () => draw_exports, + empty: () => empty, + env: () => env, + match: () => match_exports, + models: () => models_exports2 +}); +module.exports = __toCommonJS(human_exports); +var tf38 = __toESM(require_tfjs_esm()); + +// src/util/util.ts +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`modelpath error: expecting json file: ${path}`); + return path; +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function validate(defaults, config3, parent = "config", msgs = []) { + for (const key of Object.keys(config3)) { + if (typeof config3[key] === "object") { + validate(defaults[key], config3[key], key, msgs); + } else { + const defined = defaults && typeof defaults[key] !== "undefined"; + if (!defined) + msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); + const same = defaults && typeof defaults[key] === typeof config3[key]; + if (defined && !same) + msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); + } + } + if (config3.debug && parent === "config" && msgs.length > 0) + log("invalid configuration", msgs); + return msgs; +} +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "", + modelBasePath: "", + cacheModels: true, + validateModels: true, + wasmPath: "", + wasmPlatformFetch: false, + debug: false, + async: true, + warmup: "full", + cacheSensitivity: 0.7, + skipAllowed: false, + deallocate: false, + flags: {}, + softwareKernels: false, + filter: { + enabled: true, + equalization: false, + width: 0, + height: 0, + flip: false, + return: true, + autoBrightness: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: false, + maxDetected: 1, + skipFrames: 99, + skipTime: 2500, + minConfidence: 0.2, + iouThreshold: 0.1, + mask: false, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json", + keepInvalid: false + }, + attention: { + enabled: false, + modelPath: "facemesh-attention.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 99, + skipTime: 1500, + modelPath: "emotion.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 99, + skipTime: 3e3, + minConfidence: 0.1 + }, + antispoof: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "antispoof.json" + }, + liveness: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "liveness.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: -1, + minConfidence: 0.3, + skipFrames: 1, + skipTime: 200 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 99, + skipTime: 1e3, + minConfidence: 0.5, + iouThreshold: 0.2, + maxDetected: -1, + landmarks: true, + detector: { + modelPath: "handtrack.json" + }, + skeleton: { + modelPath: "handlandmark-lite.json" + } + }, + object: { + enabled: false, + modelPath: "centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 99, + skipTime: 2e3 + }, + segmentation: { + enabled: false, + modelPath: "rvm.json", + ratio: 0.5, + mode: "default" + } +}; + +// src/util/env.ts +var tf3 = __toESM(require_tfjs_esm()); + +// src/image/image.ts +var tf2 = __toESM(require_tfjs_esm()); + +// src/image/imagefxshaders.ts +var vertexIdentity = ` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -14,7 +325,8 @@ vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var N1=` +`; +var colorMatrixWithAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -26,7 +338,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,I1=` +`; +var colorMatrixWithoutAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -38,7 +351,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,O1=` +`; +var pixelate = ` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -51,7 +365,8 @@ vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,L1=` +`; +var blur = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -74,7 +389,8 @@ gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`,C1=` +`; +var convolution = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -96,20 +412,6055 @@ c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`;var Kt=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,s)=>(n[s]=0,r))},Jt=class{constructor(t,n,o){k(this,"uniform",{});k(this,"attribute",{});k(this,"gl");k(this,"id");k(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(u(`filter: gl compile failed: ${this.gl.getShaderInfoLog(o)||"unknown"}`),null)):(u("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),s=this.compile(o,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!s)){if(!this.id){u("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){u(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)||"unknown"}`);return}this.gl.useProgram(this.id),Kt(n,"attribute",this.attribute);for(let A in this.attribute)this.attribute[A]=this.gl.getAttribLocation(this.id,A);Kt(n,"uniform",this.uniform),Kt(o,"uniform",this.uniform);for(let A in this.uniform)this.uniform[A]=this.gl.getUniformLocation(this.id,A)}}};function W1(){let e=0,t=null,n=!1,o=-1,r=[null,null],s=[],A=null,a=null,l=te(100,100),c={},x={INTERMEDIATE:1},i=l.getContext("webgl");if(!i){u("filter: cannot get webgl context");return}this.gl=i;function y(T,m){if(!(T===l.width&&m===l.height)){if(l.width=T,l.height=m,!A){let h=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);A=i.createBuffer(),i.bindBuffer(i.ARRAY_BUFFER,A),i.bufferData(i.ARRAY_BUFFER,h,i.STATIC_DRAW),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}i.viewport(0,0,l.width,l.height),r=[null,null]}}function d(T,m){let h=i.createFramebuffer();i.bindFramebuffer(i.FRAMEBUFFER,h);let S=i.createRenderbuffer();i.bindRenderbuffer(i.RENDERBUFFER,S);let P=i.createTexture();return i.bindTexture(i.TEXTURE_2D,P),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,T,m,0,i.RGBA,i.UNSIGNED_BYTE,null),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.framebufferTexture2D(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,P,0),i.bindTexture(i.TEXTURE_2D,null),i.bindFramebuffer(i.FRAMEBUFFER,null),{fbo:h,texture:P}}function p(T){return r[T]=r[T]||d(l.width,l.height),r[T]}function f(T=0){if(!a)return;let m=null,h=null,S=!1;e===0?m=t:m=p(o).texture||null,e++,n&&!(T&x.INTERMEDIATE)?(h=null,S=e%2===0):(o=(o+1)%2,h=p(o).fbo||null),i.bindTexture(i.TEXTURE_2D,m),i.bindFramebuffer(i.FRAMEBUFFER,h),i.uniform1f(a.uniform.flipY,S?-1:1),i.drawArrays(i.TRIANGLES,0,6)}function b(T){if(c[T])return a=c[T],i.useProgram((a?a.id:null)||null),a;if(a=new Jt(i,j1,T),!a)return u("filter: could not get webgl 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m=Math.cos(T),h=Math.sin(T),S=.213,P=.715,I=.072;M.colorMatrix([S+m*(1-S)+h*-S,P+m*-P+h*-P,I+m*-I+h*(1-I),0,0,S+m*-S+h*.143,P+m*(1-P)+h*.14,I+m*-I+h*-.283,0,0,S+m*-S+h*-(1-S),P+m*-P+h*P,I+m*(1-I)+h*I,0,0,0,0,0,1,0])},desaturateLuminance:()=>{M.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{M.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{M.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{M.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{M.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{M.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{M.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{M.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:T=>{let m=new Float32Array(T),h=1/l.width,S=1/l.height,P=b(C1);!P||(i.uniform1fv(P.uniform.m,m),i.uniform2f(P.uniform.px,h,S),f())},detectEdges:()=>{M.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{M.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{M.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:T=>{let m=T||1;M.convolution.call(this,[0,-1*m,0,-1*m,1+4*m,-1*m,0,-1*m,0])},emboss:T=>{let m=T||1;M.convolution.call(this,[-2*m,-1*m,0,-1*m,1,1*m,0,1*m,2*m])},blur:T=>{let m=T/7/l.width,h=T/7/l.height,S=b(L1);!S||(i.uniform2f(S.uniform.px,0,h),f(x.INTERMEDIATE),i.uniform2f(S.uniform.px,m,0),f())},pixelate:T=>{let m=T/l.width,h=T/l.height,S=b(O1);!S||(i.uniform2f(S.uniform.size,m,h),f())}};this.add=function(T){let m=Array.prototype.slice.call(arguments,1),h=M[T];s.push({func:h,args:m})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(T){y(T.width,T.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,T);for(let m=0;mx.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let x=[Y.sub(n[0],o[0]),Y.sub(n[1],o[1]),Y.sub(n[2],o[2])],i=[Y.sub(r[0],o[0]),Y.sub(r[1],o[1]),Y.sub(r[2],o[2])],y=[Y.mul(x[0],l),Y.mul(x[1],l),Y.mul(x[2],l)],d=Y.stack([y[0],y[1],y[2]],2);c=Y.reshape(d,[1,t.shape[0]||0,t.shape[1]||0,3]),Y.dispose([...x,...i,...y])}else c=Y.expandDims(t,0);return Y.dispose([...n,...o,...r,n,t,e]),c}var X2=3840,v0=null,R0=null,c2=null,e0,J0={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function 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0:d.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!v0||v0.width!==s||v0.height!==A)&&(v0=te(s,A));let a=v0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,v0.width,v0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,v0.width,v0.height),(!R0||v0.width!==R0.width||v0.height!==R0.height)&&(R0=te(v0.width,v0.height)),t.filter.enabled&&R.webgl.supported?(e0||(e0=R.browser?new W1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((p=e0.get())==null?void 0:p.length)>1?R0=e0.apply(v0):R0=e0.draw(v0)):(t.debug&&u("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,q2(v0,R0))):(q2(v0,R0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:R0};if(!R0)throw new 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0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}}function Os(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ke(e.rotation.angle.pitch)/90,s=new Path2D(` - M ${e.box[0]+e.box[2]/2} ${e.box[1]} +`; + +// src/image/imagefx.ts +var collect = (source, prefix, collection) => { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match2, name) => { + collection[name] = 0; + return match2; + }); +}; +var GLProgram = class { + constructor(gl, vertexSource, fragmentSource) { + __publicField(this, "uniform", {}); + __publicField(this, "attribute", {}); + __publicField(this, "gl"); + __publicField(this, "id"); + __publicField(this, "compile", (source, type) => { + const shader = this.gl.createShader(type); + if (!shader) { + log("filter: could not create shader"); + return null; + } + this.gl.shaderSource(shader, source); + this.gl.compileShader(shader); + if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { + log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); + return null; + } + return shader; + }); + this.gl = gl; + const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); + const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); + this.id = this.gl.createProgram(); + if (!vertexShader || !fragmentShader) + return; + if (!this.id) { + log("filter: could not create webgl program"); + return; + } + this.gl.attachShader(this.id, vertexShader); + this.gl.attachShader(this.id, fragmentShader); + this.gl.linkProgram(this.id); + if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { + log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); + return; + } + this.gl.useProgram(this.id); + collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = this.gl.getAttribLocation(this.id, a); + collect(vertexSource, "uniform", this.uniform); + collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = this.gl.getUniformLocation(this.id, u); + } +}; +function GLImageFilter() { + let drawCount = 0; + let sourceTexture = null; + let lastInChain = false; + let currentFramebufferIndex = -1; + let tempFramebuffers = [null, null]; + let filterChain = []; + let vertexBuffer = null; + let currentProgram = null; + const fxcanvas = canvas(100, 100); + const shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = fxcanvas.getContext("webgl"); + if (!gl) { + log("filter: cannot get webgl context"); + return; + } + this.gl = gl; + function resize(width, height) { + if (width === fxcanvas.width && height === fxcanvas.height) + return; + fxcanvas.width = width; + fxcanvas.height = height; + if (!vertexBuffer) { + const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); + vertexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); + tempFramebuffers = [null, null]; + } + function createFramebufferTexture(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + } + function getTempFramebuffer(index2) { + tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); + return tempFramebuffers[index2]; + } + function draw(flags = 0) { + if (!currentProgram) + return; + let source = null; + let target = null; + let flipY = false; + if (drawCount === 0) + source = sourceTexture; + else + source = getTempFramebuffer(currentFramebufferIndex).texture || null; + drawCount++; + if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = drawCount % 2 === 0; + } else { + currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; + target = getTempFramebuffer(currentFramebufferIndex).fbo || null; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + } + function compileShader(fragmentSource) { + if (shaderProgramCache[fragmentSource]) { + currentProgram = shaderProgramCache[fragmentSource]; + gl.useProgram((currentProgram ? currentProgram.id : null) || null); + return currentProgram; + } + currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); + if (!currentProgram) { + log("filter: could not get webgl program"); + return null; + } + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(currentProgram.attribute["pos"]); + gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(currentProgram.attribute["uv"]); + gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); + shaderProgramCache[fragmentSource] = currentProgram; + return currentProgram; + } + const filter = { + colorMatrix: (matrix) => { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; + const program = compileShader(shader); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + draw(); + }, + brightness: (brightness) => { + const b = (brightness || 0) + 1; + filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + saturation: (amount) => { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturate: () => { + filter.saturation(-1); + }, + contrast: (amount) => { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }, + negative: () => { + filter.contrast(-2); + }, + hue: (rotation) => { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturateLuminance: () => { + filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }, + sepia: () => { + filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + brownie: () => { + filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }, + vintagePinhole: () => { + filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }, + kodachrome: () => { + filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }, + technicolor: () => { + filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }, + polaroid: () => { + filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + shiftToBGR: () => { + filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + convolution: (matrix) => { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / fxcanvas.width; + const pixelSizeY = 1 / fxcanvas.height; + const program = compileShader(convolution); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); + draw(); + }, + detectEdges: () => { + filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }, + sobelX: () => { + filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }, + sobelY: () => { + filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }, + sharpen: (amount) => { + const a = amount || 1; + filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }, + emboss: (size2) => { + const s = size2 || 1; + filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }, + blur: (size2) => { + const blurSizeX = size2 / 7 / fxcanvas.width; + const blurSizeY = size2 / 7 / fxcanvas.height; + const program = compileShader(blur); + if (!program) + return; + gl.uniform2f(program.uniform["px"], 0, blurSizeY); + draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform["px"], blurSizeX, 0); + draw(); + }, + pixelate: (size2) => { + const blurSizeX = size2 / fxcanvas.width; + const blurSizeY = size2 / fxcanvas.height; + const program = compileShader(pixelate); + if (!program) + return; + gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); + draw(); + } + }; + this.add = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const func = filter[name]; + filterChain.push({ func, args }); + }; + this.reset = function() { + filterChain = []; + }; + this.get = function() { + return filterChain; + }; + this.apply = function(image28) { + resize(image28.width, image28.height); + drawCount = 0; + if (!sourceTexture) + sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); + for (let i = 0; i < filterChain.length; i++) { + lastInChain = i === filterChain.length - 1; + const f = filterChain[i]; + f.func.apply(this, f.args || []); + } + return fxcanvas; + }; + this.draw = function(image28) { + this.add("brightness", 0); + return this.apply(image28); + }; +} + +// src/image/enhance.ts +var tf = __toESM(require_tfjs_esm()); +async function histogramEqualization(inputImage) { + const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; + const rgb2 = tf.split(squeeze14, 3, 2); + const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; + const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; + const absMax = await Promise.all(max5.map((channel) => channel.data())); + const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); + const maxRange = maxValue > 1 ? 255 : 1; + const factor = maxRange / maxValue; + let final; + if (factor > 1) { + const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; + const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; + const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; + const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); + final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); + tf.dispose([...sub11, ...range, ...enh]); + } else { + final = tf.expandDims(squeeze14, 0); + } + tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); + return final; +} + +// src/image/image.ts +var maxSize = 3840; +var inCanvas = null; +var outCanvas = null; +var tmpCanvas = null; +var fx; +var last = { + inputSum: 0, + cacheDiff: 1, + sumMethod: 0, + inputTensor: void 0 +}; +function reset() { + last.inputSum = 0; + last.cacheDiff = 1; + last.sumMethod = 0; + last.inputTensor = void 0; +} +function canvas(width, height) { + let c; + if (env.browser) { + if (env.worker) { + if (typeof OffscreenCanvas === "undefined") + throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); + c = new OffscreenCanvas(width, height); + } else { + if (typeof document === "undefined") + throw new Error("canvas error: attempted to run in browser but DOM is not defined"); + c = document.createElement("canvas"); + c.width = width; + c.height = height; + } + } else { + if (typeof env.Canvas !== "undefined") + c = new env.Canvas(width, height); + else if (typeof globalThis.Canvas !== "undefined") + c = new globalThis.Canvas(width, height); + } + return c; +} +function copy(input, output) { + const outputCanvas = output || canvas(input.width, input.height); + const ctx = outputCanvas.getContext("2d"); + ctx.drawImage(input, 0, 0); + return outputCanvas; +} +async function process2(input, config3, getTensor = true) { + var _a, _b, _c; + if (!input) { + if (config3.debug) + log("input error: input is missing"); + return { tensor: null, canvas: null }; + } + if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("input error: type not recognized"); + } + if (input instanceof tf2.Tensor) { + let tensor7 = null; + if (input["isDisposedInternal"]) + throw new Error("input error: attempted to use tensor but it is disposed"); + if (!input.shape) + throw new Error("input error: attempted to use tensor without a shape"); + if (input.shape.length === 3) { + if (input.shape[2] === 3) { + tensor7 = tf2.expandDims(input, 0); + } else if (input.shape[2] === 4) { + const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); + tensor7 = tf2.expandDims(rgb2, 0); + tf2.dispose(rgb2); + } + } else if (input.shape.length === 4) { + if (input.shape[3] === 3) { + tensor7 = tf2.clone(input); + } else if (input.shape[3] === 4) { + tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + } + } + if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) + throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); + if (tensor7.dtype === "int32") { + const cast8 = tf2.cast(tensor7, "float32"); + tf2.dispose(tensor7); + tensor7 = cast8; + } + return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; + } + if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { + if (config3.debug) + log("input stream is not ready"); + return { tensor: null, canvas: inCanvas }; + } + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) { + if (config3.debug) + log("cannot determine input dimensions"); + return { tensor: null, canvas: inCanvas }; + } + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); + } + if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) + targetWidth = config3.filter.width; + else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) + targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); + if ((config3.filter.height || 0) > 0) + targetHeight = config3.filter.height; + else if ((config3.filter.width || 0) > 0) + targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("input error: cannot determine dimension"); + if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) + inCanvas = canvas(targetWidth, targetHeight); + const inCtx = inCanvas.getContext("2d"); + if (typeof ImageData !== "undefined" && input instanceof ImageData) { + inCtx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof inCtx.translate !== "undefined") { + inCtx.translate(originalWidth, 0); + inCtx.scale(-1, 1); + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + inCtx.setTransform(1, 0, 0, 1, 0, 0); + } else { + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + } + } + if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) + outCanvas = canvas(inCanvas.width, inCanvas.height); + if (config3.filter.enabled && env.webgl.supported) { + if (!fx) + fx = env.browser ? new GLImageFilter() : null; + env.filter = !!fx; + if (!(fx == null ? void 0 : fx.add)) { + if (config3.debug) + log("input process error: cannot initialize filters"); + env.webgl.supported = false; + config3.filter.enabled = false; + copy(inCanvas, outCanvas); + } else { + fx.reset(); + if (config3.filter.brightness !== 0) + fx.add("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.add("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.add("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.add("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.add("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.add("hue", config3.filter.hue); + if (config3.filter.negative) + fx.add("negative"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.vintage) + fx.add("brownie"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.kodachrome) + fx.add("kodachrome"); + if (config3.filter.technicolor) + fx.add("technicolor"); + if (config3.filter.polaroid) + fx.add("polaroid"); + if (config3.filter.pixelate !== 0) + fx.add("pixelate", config3.filter.pixelate); + if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) + outCanvas = fx.apply(inCanvas); + else + outCanvas = fx.draw(inCanvas); + } + } else { + copy(inCanvas, outCanvas); + if (fx) + fx = null; + env.filter = !!fx; + } + if (!getTensor) + return { tensor: null, canvas: outCanvas }; + if (!outCanvas) + throw new Error("canvas error: cannot create output"); + let pixels; + let depth = 3; + if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { + if (env.browser && tf2.browser) { + pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; + } else { + depth = input.data.length / input.height / input.width; + const arr = new Uint8Array(input.data.buffer); + pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); + } + } else { + if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) + tmpCanvas = canvas(outCanvas.width, outCanvas.height); + if (tf2.browser && env.browser) { + if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { + pixels = tf2.browser.fromPixels(outCanvas); + } else { + tmpCanvas = copy(outCanvas); + pixels = tf2.browser.fromPixels(tmpCanvas); + } + } else { + const tempCanvas = copy(outCanvas); + const tempCtx = tempCanvas.getContext("2d"); + const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); + depth = tempData.data.length / targetWidth / targetHeight; + const arr = new Uint8Array(tempData.data.buffer); + pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); + } + } + if (depth === 4) { + const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); + tf2.dispose(pixels); + pixels = rgb2; + } + if (!pixels) + throw new Error("input error: cannot create tensor"); + const casted = tf2.cast(pixels, "float32"); + const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); + tf2.dispose([pixels, casted]); + if (config3.filter.autoBrightness) { + const max5 = tf2.max(tensor6); + const maxVal = await max5.data(); + config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; + tf2.dispose(max5); + } + return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; +} +async function skip(config3, input) { + let skipFrame = false; + if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) + return skipFrame; + if (!last.inputTensor) { + last.inputTensor = tf2.clone(input); + } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { + tf2.dispose(last.inputTensor); + last.inputTensor = tf2.clone(input); + } else { + const t2 = {}; + t2.diff = tf2.sub(input, last.inputTensor); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; + tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); + last.inputTensor = tf2.clone(input); + skipFrame = diffRelative <= (config3.cacheSensitivity || 0); + } + return skipFrame; +} +async function compare(config3, input1, input2) { + const t2 = {}; + if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { + if (!config3.debug) + log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); + return 0; + } + if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { + if (!config3.debug) + log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); + return 0; + } + t2.input1 = tf2.clone(input1); + t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); + t2.diff = tf2.sub(t2.input1, t2.input2); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; + tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); + return diffRelative; +} + +// src/util/env.ts +var _canvas, _image, _imageData; +var Env = class { + constructor() { + __publicField(this, "browser"); + __publicField(this, "node"); + __publicField(this, "worker"); + __publicField(this, "platform", ""); + __publicField(this, "agent", ""); + __publicField(this, "backends", []); + __publicField(this, "initial"); + __publicField(this, "filter"); + __publicField(this, "tfjs"); + __publicField(this, "offscreen"); + __publicField(this, "perfadd", false); + __publicField(this, "tensorflow", { + version: void 0, + gpu: void 0 + }); + __publicField(this, "wasm", { + supported: void 0, + backend: void 0, + simd: void 0, + multithread: void 0 + }); + __publicField(this, "webgl", { + supported: void 0, + backend: void 0, + version: void 0, + renderer: void 0, + shader: void 0, + vendor: void 0 + }); + __publicField(this, "webgpu", { + supported: void 0, + backend: void 0, + adapter: void 0 + }); + __publicField(this, "cpu", { + model: void 0, + flags: [] + }); + __publicField(this, "kernels", []); + __privateAdd(this, _canvas, void 0); + __privateAdd(this, _image, void 0); + __privateAdd(this, _imageData, void 0); + this.browser = typeof navigator !== "undefined"; + this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; + this.tfjs = { version: tf3.version["tfjs-core"] }; + this.offscreen = typeof OffscreenCanvas !== "undefined"; + this.initial = true; + this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw == null ? void 0 : raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; + this.agent = navigator.userAgent.replace(raw[0], ""); + if (this.platform[1]) + this.agent = this.agent.replace(raw[1], ""); + this.agent = this.agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + this.platform = `${process.platform} ${process.arch}`; + this.agent = `NodeJS ${process.version}`; + } + } + get Canvas() { + return __privateGet(this, _canvas); + } + set Canvas(val) { + __privateSet(this, _canvas, val); + globalThis.Canvas = val; + } + get Image() { + return __privateGet(this, _image); + } + set Image(val) { + __privateSet(this, _image, val); + globalThis.Image = val; + } + get ImageData() { + return __privateGet(this, _imageData); + } + set ImageData(val) { + __privateSet(this, _imageData, val); + globalThis.ImageData = val; + } + async updateBackend() { + this.backends = Object.keys(tf3.engine().registryFactory); + try { + this.tensorflow = { + version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, + gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 + }; + } catch (e) { + } + this.wasm.supported = typeof WebAssembly !== "undefined"; + this.wasm.backend = this.backends.includes("wasm"); + if (this.wasm.supported && this.wasm.backend) { + this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + } + const c = canvas(100, 100); + const gl = c ? c.getContext("webgl2") : void 0; + this.webgl.supported = typeof gl !== "undefined"; + this.webgl.backend = this.backends.includes("webgl"); + if (this.webgl.supported && this.webgl.backend && gl) { + this.webgl.version = gl.getParameter(gl.VERSION); + this.webgl.vendor = gl.getParameter(gl.VENDOR); + this.webgl.renderer = gl.getParameter(gl.RENDERER); + this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); + } + this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; + this.webgpu.backend = this.backends.includes("webgpu"); + try { + if (this.webgpu.supported) { + const adapter = await navigator.gpu.requestAdapter(); + this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); + } + } catch (e) { + this.webgpu.supported = false; + } + try { + this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); + } catch (e) { + } + } + updateCPU() { + const cpu = { model: "", flags: [] }; + if (this.node && this.platform.startsWith("linux")) { + } + if (!this.cpu) + Object.defineProperty(this, "cpu", { value: cpu }); + else + this.cpu = cpu; + } +}; +_canvas = new WeakMap(); +_image = new WeakMap(); +_imageData = new WeakMap(); +var env = new Env(); + +// src/util/webcam.ts +var WebCam = class { + constructor() { + __publicField(this, "config"); + __publicField(this, "element"); + __publicField(this, "stream"); + __publicField(this, "devices", []); + __publicField(this, "enumerate", async () => { + try { + const devices = await navigator.mediaDevices.enumerateDevices(); + this.devices = devices.filter((device) => device.kind === "videoinput"); + } catch (e) { + this.devices = []; + } + return this.devices; + }); + __publicField(this, "start", async (webcamConfig) => { + var _a, _b; + if (webcamConfig == null ? void 0 : webcamConfig.debug) + this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; + if (webcamConfig == null ? void 0 : webcamConfig.crop) + this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; + if (webcamConfig == null ? void 0 : webcamConfig.mode) + this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; + if (webcamConfig == null ? void 0 : webcamConfig.width) + this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; + if (webcamConfig == null ? void 0 : webcamConfig.height) + this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; + if (webcamConfig == null ? void 0 : webcamConfig.id) + this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; + if (webcamConfig == null ? void 0 : webcamConfig.element) { + if (typeof webcamConfig.element === "string") { + const el = document.getElementById(webcamConfig.element); + if (el && el instanceof HTMLVideoElement) { + this.element = el; + } else { + if (this.config.debug) + log("webcam", "cannot get dom element", webcamConfig.element); + return; + } + } else if (webcamConfig.element instanceof HTMLVideoElement) { + this.element = webcamConfig.element; + } else { + if (this.config.debug) + log("webcam", "unknown dom element", webcamConfig.element); + return; + } + } else { + this.element = document.createElement("video"); + } + const requestedConstraints = { + audio: false, + video: { + facingMode: this.config.mode === "front" ? "user" : "environment", + resizeMode: this.config.crop ? "crop-and-scale" : "none" + } + }; + if (((_a = this.config) == null ? void 0 : _a.width) > 0) + requestedConstraints.video.width = { ideal: this.config.width }; + if (((_b = this.config) == null ? void 0 : _b.height) > 0) + requestedConstraints.video.height = { ideal: this.config.height }; + if (this.config.id) + requestedConstraints.video.deviceId = this.config.id; + this.element.addEventListener("play", () => { + if (this.config.debug) + log("webcam", "play"); + }); + this.element.addEventListener("pause", () => { + if (this.config.debug) + log("webcam", "pause"); + }); + this.element.addEventListener("click", async () => { + if (!this.element || !this.stream) + return; + if (this.element.paused) + await this.element.play(); + else + this.element.pause(); + }); + if (!(navigator == null ? void 0 : navigator.mediaDevices)) { + if (this.config.debug) + log("webcam", "no devices"); + return; + } + try { + this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); + } catch (err) { + log("webcam", err); + return; + } + if (!this.stream) { + if (this.config.debug) + log("webcam", "no stream"); + return; + } + this.element.srcObject = this.stream; + const ready3 = new Promise((resolve) => { + if (!this.element) + resolve(false); + else + this.element.onloadeddata = () => resolve(true); + }); + await ready3; + await this.element.play(); + if (this.config.debug) { + log("webcam", { + width: this.width, + height: this.height, + label: this.label, + stream: this.stream, + track: this.track, + settings: this.settings, + constraints: this.constraints, + capabilities: this.capabilities + }); + } + }); + __publicField(this, "pause", () => { + if (this.element) + this.element.pause(); + }); + __publicField(this, "play", async () => { + if (this.element) + await this.element.play(); + }); + __publicField(this, "stop", () => { + if (this.config.debug) + log("webcam", "stop"); + if (this.track) + this.track.stop(); + }); + this.config = { + element: void 0, + debug: true, + mode: "front", + crop: false, + width: 0, + height: 0 + }; + } + get track() { + if (!this.stream) + return void 0; + return this.stream.getVideoTracks()[0]; + } + get capabilities() { + if (!this.track) + return void 0; + return this.track.getCapabilities ? this.track.getCapabilities() : void 0; + } + get constraints() { + if (!this.track) + return void 0; + return this.track.getConstraints ? this.track.getConstraints() : void 0; + } + get settings() { + if (!this.stream) + return void 0; + const track = this.stream.getVideoTracks()[0]; + return track.getSettings ? track.getSettings() : void 0; + } + get label() { + if (!this.track) + return ""; + return this.track.label; + } + get paused() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.paused) || false; + } + get width() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; + } + get height() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; + } +}; + +// src/tfjs/load.ts +var tf4 = __toESM(require_tfjs_esm()); + +// models/models.json +var models_exports = {}; +__export(models_exports, { + age: () => age, + "anti-spoofing": () => anti_spoofing, + antispoof: () => antispoof, + blazeface: () => blazeface, + "blazeface-back": () => blazeface_back, + "blazeface-front": () => blazeface_front, + "blazepose-detector": () => blazepose_detector, + "blazepose-full": () => blazepose_full, + "blazepose-heavy": () => blazepose_heavy, + "blazepose-lite": () => blazepose_lite, + centernet: () => centernet, + default: () => models_default, + efficientpose: () => efficientpose, + "efficientpose-i-lite": () => efficientpose_i_lite, + "efficientpose-ii-lite": () => efficientpose_ii_lite, + "efficientpose-iv": () => efficientpose_iv, + emotion: () => emotion, + faceboxes: () => faceboxes, + facemesh: () => facemesh, + "facemesh-attention": () => facemesh_attention, + "facemesh-attention-pinto": () => facemesh_attention_pinto, + "facemesh-detection-full": () => facemesh_detection_full, + "facemesh-detection-short": () => facemesh_detection_short, + faceres: () => faceres, + "faceres-deep": () => faceres_deep, + gear: () => gear, + gender: () => gender, + "gender-ssrnet-imdb": () => gender_ssrnet_imdb, + handdetect: () => handdetect, + "handlandmark-full": () => handlandmark_full, + "handlandmark-lite": () => handlandmark_lite, + "handlandmark-sparse": () => handlandmark_sparse, + handskeleton: () => handskeleton, + handtrack: () => handtrack, + "insightface-efficientnet-b0": () => insightface_efficientnet_b0, + "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, + "insightface-mobilenet-emore": () => insightface_mobilenet_emore, + "insightface-mobilenet-swish": () => insightface_mobilenet_swish, + iris: () => iris, + liveness: () => liveness, + meet: () => meet, + mobileface: () => mobileface, + mobilefacenet: () => mobilefacenet, + models: () => models, + "movenet-lightning": () => movenet_lightning, + "movenet-multipose": () => movenet_multipose, + "movenet-thunder": () => movenet_thunder, + nanodet: () => nanodet, + "nanodet-e": () => nanodet_e, + "nanodet-g": () => nanodet_g, + "nanodet-m": () => nanodet_m, + "nanodet-t": () => nanodet_t, + posenet: () => posenet, + rvm: () => rvm, + selfie: () => selfie +}); +var antispoof = 853098; +var blazeface = 538928; +var centernet = 4030290; +var emotion = 820516; +var facemesh = 1477958; +var faceres = 6978814; +var handlandmark_lite = 2023432; +var handtrack = 2964837; +var iris = 2599092; +var liveness = 592976; +var models = 0; +var movenet_lightning = 4650216; +var age = 161240; +var blazeface_back = 538928; +var blazeface_front = 402048; +var blazepose_detector = 5928856; +var blazepose_full = 6339202; +var blazepose_heavy = 27502466; +var blazepose_lite = 2726402; +var efficientpose = 5651240; +var faceboxes = 2013002; +var facemesh_attention_pinto = 2387598; +var facemesh_attention = 2382414; +var facemesh_detection_full = 1026192; +var facemesh_detection_short = 201268; +var faceres_deep = 13957620; +var gear = 1498916; +var gender_ssrnet_imdb = 161236; +var gender = 201808; +var handdetect = 3515612; +var handlandmark_full = 5431368; +var handlandmark_sparse = 5286322; +var handskeleton = 5502280; +var meet = 372228; +var mobileface = 2183192; +var mobilefacenet = 5171976; +var movenet_multipose = 9448838; +var movenet_thunder = 12477112; +var nanodet = 7574558; +var posenet = 5032780; +var rvm = 3739355; +var selfie = 212886; +var anti_spoofing = 853098; +var efficientpose_i_lite = 2269064; +var efficientpose_ii_lite = 5651240; +var efficientpose_iv = 25643252; +var insightface_efficientnet_b0 = 13013224; +var insightface_ghostnet_strides1 = 8093408; +var insightface_ghostnet_strides2 = 8049584; +var insightface_mobilenet_emore = 6938536; +var insightface_mobilenet_swish = 12168584; +var nanodet_e = 12319156; +var nanodet_g = 7574558; +var nanodet_m = 1887474; +var nanodet_t = 5294216; +var models_default = { + antispoof, + blazeface, + centernet, + emotion, + facemesh, + faceres, + "handlandmark-lite": handlandmark_lite, + handtrack, + iris, + liveness, + models, + "movenet-lightning": movenet_lightning, + age, + "blazeface-back": blazeface_back, + "blazeface-front": blazeface_front, + "blazepose-detector": blazepose_detector, + "blazepose-full": blazepose_full, + "blazepose-heavy": blazepose_heavy, + "blazepose-lite": blazepose_lite, + efficientpose, + faceboxes, + "facemesh-attention-pinto": facemesh_attention_pinto, + "facemesh-attention": facemesh_attention, + "facemesh-detection-full": facemesh_detection_full, + "facemesh-detection-short": facemesh_detection_short, + "faceres-deep": faceres_deep, + gear, + "gender-ssrnet-imdb": gender_ssrnet_imdb, + gender, + handdetect, + "handlandmark-full": handlandmark_full, + "handlandmark-sparse": handlandmark_sparse, + handskeleton, + meet, + mobileface, + mobilefacenet, + "movenet-multipose": movenet_multipose, + "movenet-thunder": movenet_thunder, + nanodet, + posenet, + rvm, + selfie, + "anti-spoofing": anti_spoofing, + "efficientpose-i-lite": efficientpose_i_lite, + "efficientpose-ii-lite": efficientpose_ii_lite, + "efficientpose-iv": efficientpose_iv, + "insightface-efficientnet-b0": insightface_efficientnet_b0, + "insightface-ghostnet-strides1": insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": insightface_ghostnet_strides2, + "insightface-mobilenet-emore": insightface_mobilenet_emore, + "insightface-mobilenet-swish": insightface_mobilenet_swish, + "nanodet-e": nanodet_e, + "nanodet-g": nanodet_g, + "nanodet-m": nanodet_m, + "nanodet-t": nanodet_t +}; + +// src/tfjs/load.ts +var options = { + cacheModels: true, + cacheSupported: true, + verbose: true, + debug: false, + modelBasePath: "" +}; +var modelStats = {}; +async function httpHandler(url, init4) { + if (options.debug) + log("load model fetch:", url, init4); + return fetch(url, init4); +} +function setModelLoadOptions(config3) { + options.cacheModels = config3.cacheModels; + options.verbose = config3.debug; + options.modelBasePath = config3.modelBasePath; +} +async function loadModel(modelPath) { + var _a, _b, _c, _d; + let modelUrl = join(options.modelBasePath, modelPath || ""); + if (!modelUrl.toLowerCase().endsWith(".json")) + modelUrl += ".json"; + const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); + const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); + const cachedModelName = "indexeddb://" + shortModelName; + modelStats[shortModelName] = { + name: shortModelName, + sizeFromManifest: 0, + sizeLoadedWeights: 0, + sizeDesired: models_exports[shortModelName], + inCache: false, + url: "" + }; + options.cacheSupported = typeof indexedDB !== "undefined"; + let cachedModels = {}; + try { + cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; + } catch (e) { + options.cacheSupported = false; + } + modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); + modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; + const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; + let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); + let loaded = false; + try { + model23.findIOHandler(); + if (options.debug) + log("model load handler:", model23["handler"]); + } catch (err) { + log("error finding model i/o handler:", modelUrl, err); + } + try { + const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; + modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; + if (artifacts) + model23.loadSync(artifacts); + else + model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; + if (options.verbose) + log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); + loaded = true; + } catch (err) { + log("error loading model:", modelUrl, err); + } + if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { + try { + const saveResult = await model23.save(cachedModelName); + if (options.debug) + log("model saved:", cachedModelName, saveResult); + } catch (err) { + log("error saving model:", modelUrl, err); + } + } + return model23; +} + +// package.json +var version2 = "3.0.2"; + +// src/tfjs/backend.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/tfjs/humangl.ts +var tf5 = __toESM(require_tfjs_esm()); +var config2 = { + name: "humangl", + priority: 999, + canvas: null, + gl: null, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +function register(instance) { + var _a; + if (instance.config.backend !== "humangl") + return; + if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { + log("humangl error: backend invalid context"); + instance.models.reset(); + } + if (!tf5.findBackend(config2.name)) { + try { + config2.canvas = canvas(100, 100); + } catch (err) { + log("humangl error: cannot create canvas:", err); + return; + } + try { + config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); + if (!config2.gl) { + log("humangl error: cannot get webgl context"); + return; + } + const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); + if (!glv2) { + log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); + instance.config.backend = "webgl"; + return; + } + if (config2.canvas) { + config2.canvas.addEventListener("webglcontextlost", (e) => { + log("humangl error:", e.type); + log("possible browser memory leak using webgl or conflict with multiple backend registrations"); + instance.emit("error"); + throw new Error("backend error: webgl context lost"); + }); + config2.canvas.addEventListener("webglcontextrestored", (e) => { + log("humangl error: context restored:", e); + }); + config2.canvas.addEventListener("webglcontextcreationerror", (e) => { + log("humangl error: context create:", e); + }); + } + } catch (err) { + log("humangl error: cannot get webgl context:", err); + return; + } + try { + tf5.setWebGLContext(2, config2.gl); + } catch (err) { + log("humangl error: cannot set webgl context:", err); + return; + } + try { + const ctx = new tf5.GPGPUContext(config2.gl); + tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("humangl error: cannot register webgl backend:", err); + return; + } + try { + const kernels = tf5.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tf5.registerKernel(newKernelConfig); + }); + } catch (err) { + log("humangl error: cannot update webgl backend registration:", err); + return; + } + try { + if (tf5.env().flagRegistry.WEBGL_VERSION) + tf5.env().set("WEBGL_VERSION", 2); + } catch (err) { + log("humangl error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + const backend4 = tf5.backend(); + const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; + if (current) { + if (instance.config.debug) + log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); + } else { + log("humangl error: no current gl context:", current, config2.gl); + } + } +} + +// src/tfjs/constants.ts +var tf6 = __toESM(require_tfjs_esm()); +var constants = { + tf255: 255, + tf1: 1, + tf2: 2, + tf05: 0.5, + tf127: 127.5, + rgb: [0.2989, 0.587, 0.114] +}; +function init() { + constants.tf255 = tf6.scalar(255, "float32"); + constants.tf1 = tf6.scalar(1, "float32"); + constants.tf2 = tf6.scalar(2, "float32"); + constants.tf05 = tf6.scalar(0.5, "float32"); + constants.tf127 = tf6.scalar(127.5, "float32"); + constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); +} + +// src/tfjs/backend.ts +async function getBestBackend() { + var _a; + await env.updateBackend(); + if ((_a = env.tensorflow) == null ? void 0 : _a.version) + return "tensorflow"; + if (env.webgpu.supported && env.webgpu.backend) + return "webgpu"; + if (env.webgl.supported && env.webgl.backend) + return "webgl"; + if (env.wasm.supported && env.wasm.backend) + return "wasm"; + return "cpu"; +} +function registerCustomOps(config3) { + const newKernels = []; + if (!env.kernels.includes("mod")) { + const kernelMod = { + kernelName: "Mod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) + }; + tf7.registerKernel(kernelMod); + env.kernels.push("mod"); + newKernels.push("mod"); + } + if (!env.kernels.includes("floormod")) { + const kernelFloorMod = { + kernelName: "FloorMod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) + }; + tf7.registerKernel(kernelFloorMod); + env.kernels.push("floormod"); + newKernels.push("floormod"); + } + if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { + const kernelRotateWithOffset = { + kernelName: "RotateWithOffset", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => { + const backend4 = tf7.getBackend(); + tf7.setBackend("cpu"); + const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); + tf7.setBackend(backend4); + return t2; + }) + }; + tf7.registerKernel(kernelRotateWithOffset); + env.kernels.push("rotatewithoffset"); + newKernels.push("rotatewithoffset"); + } + if (newKernels.length > 0 && config3.debug) + log("registered kernels:", newKernels); +} +var defaultFlags = {}; +async function check(instance, force = false) { + var _a; + instance.state = "backend"; + if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) + instance.config.backend = await getBestBackend(); + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { + const timeStamp = now(); + if (instance.config.backend && instance.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { + if (instance.config.debug) + log("running inside web worker"); + } + if (env.browser && instance.config.backend === "tensorflow") { + if (instance.config.debug) + log("override: backend set to tensorflow while running in browser"); + instance.config.backend = "webgl"; + } + if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { + if (instance.config.debug) + log(`override: backend set to ${instance.config.backend} while running in nodejs`); + instance.config.backend = "tensorflow"; + } + if (env.browser && instance.config.backend === "webgpu") { + if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { + log("override: backend set to webgpu but browser does not support webgpu"); + instance.config.backend = "webgl"; + } else { + const adapter = await navigator.gpu.requestAdapter(); + if (instance.config.debug) + log("enumerated webgpu adapter:", adapter); + if (!adapter) { + log("override: backend set to webgpu but browser reports no available gpu"); + instance.config.backend = "webgl"; + } else { + const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; + log("webgpu adapter info:", adapterInfo); + } + } + } + let available = Object.keys(tf7.engine().registryFactory); + if (instance.config.backend === "humangl" && !available.includes("humangl")) { + register(instance); + available = Object.keys(tf7.engine().registryFactory); + } + if (instance.config.debug) + log("available backends:", available); + if (!available.includes(instance.config.backend)) { + log(`error: backend ${instance.config.backend} not found in registry`); + instance.config.backend = env.node ? "tensorflow" : "webgl"; + if (instance.config.debug) + log(`override: setting backend ${instance.config.backend}`); + } + if (instance.config.debug) + log("setting backend:", [instance.config.backend]); + if (instance.config.backend === "wasm") { + if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) + tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); + if (instance.config.debug) + log("wasm path:", instance.config.wasmPath); + if (typeof tf7.setWasmPaths !== "undefined") + tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); + else + throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); + let mt = false; + let simd = false; + try { + mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + if (instance.config.debug) + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (instance.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } catch (e) { + log("wasm detection failed"); + } + } + try { + await tf7.setBackend(instance.config.backend); + await tf7.ready(); + } catch (err) { + log("error: cannot set backend:", instance.config.backend, err); + return false; + } + if (instance.config.debug) + defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); + } + if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { + if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (tf7.env().flagRegistry.WEBGL_EXP_CONV) + tf7.env().set("WEBGL_EXP_CONV", true); + if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + } + if (tf7.getBackend() === "webgpu") { + } + if (instance.config.debug) { + const newFlags = tf7.env().flags; + const updatedFlags = {}; + for (const key of Object.keys(newFlags)) { + if (defaultFlags[key] === newFlags[key]) + continue; + updatedFlags[key] = newFlags[key]; + } + if (instance.config.debug && Object.keys(updatedFlags).length > 0) + log("backend:", tf7.getBackend(), "flags:", updatedFlags); + } + if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { + if (instance.config.debug) + log("flags:", instance.config["flags"]); + for (const [key, val] of Object.entries(instance.config.flags)) { + tf7.env().set(key, val); + } + } + tf7.enableProdMode(); + init(); + instance.performance.initBackend = Math.trunc(now() - timeStamp); + instance.config.backend = tf7.getBackend(); + await env.updateBackend(); + registerCustomOps(instance.config); + env.initial = false; + } + return true; +} +function fakeOps(kernelNames, config3) { + for (const kernelName of kernelNames) { + const kernelConfig = { + kernelName, + backendName: config3.backend, + kernelFunc: (param) => { + var _a; + if (config3.debug) + log("kernelFunc", kernelName, config3.backend, param); + return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; + } + }; + tf7.registerKernel(kernelConfig); + } + env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); +} + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body, + canvas: () => canvas2, + face: () => face, + gesture: () => gesture, + hand: () => hand, + init: () => init2, + object: () => object, + options: () => options2, + person: () => person +}); + +// src/draw/primitives.ts +var getCanvasContext = (input) => { + if (!input) + log("draw error: invalid canvas"); + else if (!input.getContext) + log("draw error: canvas context not defined"); + else { + const ctx = input.getContext("2d"); + if (!ctx) + log("draw error: cannot get canvas context"); + else + return ctx; + } + return null; +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); +var colorDepth = (z, opt) => { + if (!opt.useDepth || typeof z === "undefined") + return opt.color; + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); + return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; +}; +function labels(ctx, str, startX, startY, localOptions2) { + const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); + const x = Math.max(0, startX); + for (let i = line.length - 1; i >= 0; i--) { + const y = i * localOptions2.lineHeight + startY; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(line[i], x + 5, y + 16); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(line[i], x + 4, y + 15); + } +} +function point(ctx, x, y, z, localOptions2) { + ctx.fillStyle = colorDepth(z, localOptions2); + ctx.beginPath(); + ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions2) { + ctx.beginPath(); + ctx.lineWidth = localOptions2.lineWidth; + if (localOptions2.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.moveTo(x + localOptions2.roundRect, y); + ctx.lineTo(x + width - localOptions2.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); + ctx.lineTo(x + width, y + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); + ctx.lineTo(x + localOptions2.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); + ctx.lineTo(x, y + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); + ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); + } + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.lineWidth = localOptions2.lineWidth; + if (!localOptions2.useCurves || points.length <= 2) { + lines(ctx, points, localOptions2); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function arrow(ctx, from, to, radius = 5) { + let angle; + let x; + let y; + ctx.beginPath(); + ctx.moveTo(from[0], from[1]); + ctx.lineTo(to[0], to[1]); + angle = Math.atan2(to[1] - from[1], to[0] - from[0]); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + ctx.closePath(); + ctx.stroke(); + ctx.fill(); +} + +// src/draw/options.ts +var options2 = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + alpha: 0.5, + font: 'small-caps 16px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawAttention: true, + drawGestures: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false, + faceLabels: "", + bodyLabels: "", + bodyPartLabels: "", + objectLabels: "", + handLabels: "", + fingerLabels: "", + gestureLabels: "" +}; + +// src/face/facemeshcoords.ts +var meshAnnotations = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], + lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], + lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], + lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], + lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisIndices = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, + { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, + { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, + { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, + { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, + { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, + { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, + { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } +]; +var UV468 = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + [0.289712011814117, 0.380764007568359], + [0.499954998493195, 0.312398016452789], + [0.499987006187439, 0.269918978214264], + [0.500023007392883, 0.107050001621246], + 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+ 344, + 440, + 438, + 458, + 459, + 461, + 364, + 434, + 394, + 428, + 396, + 262, + 274, + 354, + 457, + 317, + 316, + 402, + 316, + 315, + 403, + 315, + 314, + 404, + 314, + 313, + 405, + 313, + 421, + 406, + 323, + 366, + 361, + 292, + 306, + 407, + 306, + 291, + 408, + 291, + 287, + 409, + 287, + 432, + 410, + 427, + 434, + 411, + 372, + 264, + 383, + 459, + 309, + 457, + 366, + 352, + 401, + 1, + 274, + 4, + 418, + 421, + 262, + 331, + 294, + 358, + 435, + 433, + 367, + 392, + 289, + 439, + 328, + 462, + 326, + 94, + 2, + 370, + 289, + 305, + 455, + 339, + 254, + 448, + 359, + 255, + 446, + 254, + 253, + 449, + 253, + 252, + 450, + 252, + 256, + 451, + 256, + 341, + 452, + 414, + 413, + 463, + 286, + 441, + 414, + 286, + 258, + 441, + 258, + 257, + 442, + 257, + 259, + 443, + 259, + 260, + 444, + 260, + 467, + 445, + 309, + 459, + 250, + 305, + 289, + 290, + 305, + 290, + 460, + 401, + 376, + 435, + 309, + 250, + 392, + 376, + 411, + 433, + 453, + 341, + 464, + 357, + 453, + 465, + 343, + 357, + 412, + 437, + 343, + 399, + 344, + 360, + 440, + 420, + 437, + 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); +function connectionsToIndices(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var pairsLips = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var pairsFaceContour = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +var contourKeypoints = { + lips: connectionsToIndices(pairsLips), + leftEye: connectionsToIndices(pairsLeftEye), + leftEyebrow: connectionsToIndices(pairsLeftEyebrow), + leftIris: connectionsToIndices(pairsLeftIris), + rightEye: connectionsToIndices(pairsRightEye), + rightEyebrow: connectionsToIndices(pairsRightEyebrow), + rightIris: connectionsToIndices(pairsRightIris), + faceOval: connectionsToIndices(pairsFaceContour) +}; + +// src/face/constants.ts +var LIPS_CONNECTIONS = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var FACE_OVAL_CONNECTIONS = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +function connectionsToIndices2(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { + lips: connectionsToIndices2(LIPS_CONNECTIONS), + leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), + leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), + leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), + rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), + rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), + rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), + faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) +}; +var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); +var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); +var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ + 61, + 146, + 91, + 181, + 84, + 17, + 314, + 405, + 321, + 375, + 291, + 185, + 40, + 39, + 37, + 0, + 267, + 269, + 270, + 409, + 78, + 95, + 88, + 178, + 87, + 14, + 317, + 402, + 318, + 324, + 308, + 191, + 80, + 81, + 82, + 13, + 312, + 311, + 310, + 415, + 76, + 77, + 90, + 180, + 85, + 16, + 315, + 404, + 320, + 307, + 306, + 184, + 74, + 73, + 72, + 11, + 302, + 303, + 304, + 408, + 62, + 96, + 89, + 179, + 86, + 15, + 316, + 403, + 319, + 325, + 292, + 183, + 42, + 41, + 38, + 12, + 268, + 271, + 272, + 407 +]; +var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ + 33, + 7, + 163, + 144, + 145, + 153, + 154, + 155, + 133, + 246, + 161, + 160, + 159, + 158, + 157, + 173, + 130, + 25, + 110, + 24, + 23, + 22, + 26, + 112, + 243, + 247, + 30, + 29, + 27, + 28, + 56, + 190, + 226, + 31, + 228, + 229, + 230, + 231, + 232, + 233, + 244, + 113, + 225, + 224, + 223, + 222, + 221, + 189, + 35, + 124, + 46, + 53, + 52, + 65, + 143, + 111, + 117, + 118, + 119, + 120, + 121, + 128, + 245, + 156, + 70, + 63, + 105, + 66, + 107, + 55, + 193 +]; +var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ + 263, + 249, + 390, + 373, + 374, + 380, + 381, + 382, + 362, + 466, + 388, + 387, + 386, + 385, + 384, + 398, + 359, + 255, + 339, + 254, + 253, + 252, + 256, + 341, + 463, + 467, + 260, + 259, + 257, + 258, + 286, + 414, + 446, + 261, + 448, + 449, + 450, + 451, + 452, + 453, + 464, + 342, + 445, + 444, + 443, + 442, + 441, + 413, + 265, + 353, + 276, + 283, + 282, + 295, + 372, + 340, + 346, + 347, + 348, + 349, + 350, + 357, + 465, + 383, + 300, + 293, + 334, + 296, + 336, + 285, + 417 +]; + +// src/draw/face.ts +var localOptions; +function drawLabels(f, ctx) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i; + if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) + return; + let l = localOptions.faceLabels.slice(); + if (f.score) + l = replace(l, "[score]", 100 * f.score); + if (f.gender) + l = replace(l, "[gender]", f.gender); + if (f.genderScore) + l = replace(l, "[genderScore]", 100 * f.genderScore); + if (f.age) + l = replace(l, "[age]", f.age); + if (f.distance) + l = replace(l, "[distance]", 100 * f.distance); + if (f.real) + l = replace(l, "[real]", 100 * f.real); + if (f.live) + l = replace(l, "[live]", 100 * f.live); + if (f.emotion && f.emotion.length > 0) { + const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion2.length > 3) + emotion2.length = 3; + l = replace(l, "[emotions]", emotion2.join(" ")); + } + if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) + l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); + if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) + l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); + if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) + l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); + if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) + l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); + labels(ctx, l, f.box[0], f.box[1], localOptions); +} +function drawIrisElipse(f, ctx) { + var _a, _b, _c, _d; + if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } +} +function drawGazeSpheres(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") { + ctx.strokeStyle = "pink"; + const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90; + const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90; + const pathV = new Path2D(` + M ${f.box[0] + f.box[2] / 2} ${f.box[1]} C - ${o} ${e.box[1]}, - ${o} ${e.box[1]+e.box[3]}, - ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} - `),A=new Path2D(` - M ${e.box[0]} ${e.box[1]+e.box[3]/2} + ${valX} ${f.box[1]}, + ${valX} ${f.box[1] + f.box[3]}, + ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} + `); + const pathH = new Path2D(` + M ${f.box[0]} ${f.box[1] + f.box[3] / 2} C - ${e.box[0]} ${r}, - ${e.box[0]+e.box[2]} ${r}, - ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];t5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];t5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);e5(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function _2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)X1(r,c,o)}}}function $2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let x=o.fingerLabels.slice();x=_(x,"[label]",l),ne(r,x,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let x=0;x0?x-1:0][0],c[x>0?x-1:0][1]),r.lineTo(c[x][0],c[x][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function et(e,t,n){var s;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function tt(e,t,n){var r;let o=a0(x0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let x=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",x),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face + ${f.box[0]} ${valY}, + ${f.box[0] + f.box[2]} ${valY}, + ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} + `); + ctx.stroke(pathH); + ctx.stroke(pathV); + } +} +function drawGazeArrows(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { + ctx.strokeStyle = "pink"; + ctx.fillStyle = "pink"; + const leftGaze = [ + f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); + const rightGaze = [ + f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); + } +} +function drawFacePolygons(f, ctx) { + if (localOptions.drawPolygons && f.mesh.length >= 468) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); + lines(ctx, points, localOptions); + } + drawIrisElipse(f, ctx); + } +} +function drawFacePoints(f, ctx) { + if (localOptions.drawPoints && f.mesh.length >= 468) { + for (let i = 0; i < f.mesh.length; i++) { + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); + if (localOptions.drawAttention) { + if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); + if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + } + } + } +} +function drawFaceBoxes(f, ctx) { + if (localOptions.drawBoxes) { + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); + } +} +function face(inCanvas2, result, drawOptions) { + localOptions = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + for (const f of result) { + drawFaceBoxes(f, ctx); + drawLabels(f, ctx); + if (f.mesh && f.mesh.length > 0) { + drawFacePoints(f, ctx); + drawFacePolygons(f, ctx); + drawGazeSpheres(f, ctx); + drawGazeArrows(f, ctx); + } + } +} + +// src/draw/body.ts +function body(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + ctx.lineWidth = localOptions2.lineWidth; + ctx.font = localOptions2.font; + if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.bodyLabels.slice(); + l = replace(l, "[score]", 100 * result[i].score); + labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); + } + } + if (localOptions2.drawPoints && result[i].keypoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) + continue; + ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); + } + } + if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { + ctx.font = localOptions2.font; + for (const pt of result[i].keypoints) { + if (!pt.score || pt.score === 0) + continue; + let l = localOptions2.bodyPartLabels.slice(); + l = replace(l, "[label]", pt.part); + l = replace(l, "[score]", 100 * pt.score); + labels(ctx, l, pt.position[0], pt.position[1], localOptions2); + } + } + if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { + for (const part of Object.values(result[i].annotations)) { + for (const connected4 of part) + curves(ctx, connected4, localOptions2); + } + } + } +} + +// src/draw/hand.ts +function hand(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.handLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + if (localOptions2.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = colorDepth(pt[2], localOptions2); + point(ctx, pt[0], pt[1], 0, localOptions2); + } + } + } + if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { + for (const [part, pt] of Object.entries(h.annotations)) { + let l = localOptions2.fingerLabels.slice(); + l = replace(l, "[label]", part); + labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); + } + } + if (localOptions2.drawPolygons && h.annotations) { + const addHandLine = (part) => { + if (!part || part.length === 0 || !part[0]) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions2); + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions2.lineWidth; + addHandLine(h.annotations.index); + addHandLine(h.annotations.middle); + addHandLine(h.annotations.ring); + addHandLine(h.annotations.pinky); + addHandLine(h.annotations.thumb); + } + } +} + +// src/draw/object.ts +function object(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.objectLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + } +} + +// src/draw/gesture.ts +function gesture(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions2.font; + ctx.fillStyle = localOptions2.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + const [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + let l = localOptions2.gestureLabels.slice(); + l = replace(l, "[where]", where[0]); + l = replace(l, "[who]", who); + l = replace(l, "[what]", what[1]); + labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); + i += 1; + } + } + } +} + +// src/draw/labels.ts +var defaultLabels = { + face: `face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -118,7 +6469,7523 @@ live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var s5=0;function Fs(e,t,n){let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;si5,kpt:()=>a5});var a5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],i5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,Y1,Gs=5,rt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function K1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var U1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),U1[0]),A=D.mul(D.div(A,t2),U1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function Xs(e,t,n,o){var c,x;let r=[],s={};s.boxes=Zs(e,Y1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let y=a[i],d=l[i],p=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],f={score:y,boxRaw:d,box:p};r.push(f)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function J1(e,t,n){let o={};o.res=_0==null?void 0:_0.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await Xs(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function ve(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function Q1(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var F0,c5=256,l5=Number.MAX_SAFE_INTEGER,qs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],_1=0,$1=e=>1-1/(1+Math.exp(e)),t3=e=>K1(e);async function n3(e){if(R.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await O(e.body.modelPath);let t=F0!=null&&F0.executor?Object.values(F0.modelSignature.inputs):void 0;c5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return F0}function e3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=B0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Oe=[[0,0],a,l,[0,0]],o.pad=B0.pad(o.cropped||e,Oe),o.resize=B0.image.resizeBilinear(o.pad,[t,t]),r=B0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=B0.image.resizeBilinear(o.cropped||e,[t,t]),r=B0.div(o.resize,C.tf255)):r=B0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>B0.dispose(o[a])),r}function Us(e,t,n){for(let o of 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s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},f=Dn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Vn=1.65,Zn=[0,5,9,13,17,1,2],SA=0,jA=2,Xn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>Q5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Vn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=J5(o,[0,0]),c=a.map(d=>[...Q5(d,l),d[2]]),x=Bn(r),i=[...W2(n),1],y=[Ze(i,x[0]),Ze(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Xn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,x,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let x=wt(Et(c),Vn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,Un;async function _5(e,t){let n=await Un.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Yn(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",a2.modelUrl),e.debug&&u("cached model:",i2.modelUrl));let t=a2?new zt(a2):void 0;return t&&i2&&(Un=new St(t,i2)),[a2,i2]}var Q=Z(H());var f0=[null,null],IA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],OA=["hand","fist","pinch","point","face","tip","pinchtip"],Jn=4,Qn=1.6,LA=512,CA=1.4,jt=Number.MAX_SAFE_INTEGER,$5=0,ke=[0,0],y0={boxes:[],hands:[]},_n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function $n(e){var t;if(R.initial&&(f0[0]=null),f0[0])e.debug&&u("cached model:",f0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),f0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=f0[0].executor?Object.values(f0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[0]}async function eo(e){var t;if(R.initial&&(f0[1]=null),f0[1])e.debug&&u("cached model:",f0[1].modelUrl);else{f0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=f0[1].executor?Object.values(f0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[1]}async function WA(e,t){let n=[];if(!e||!f0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,LA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await f0[0].executeAsync(o.cast,IA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[Jn]),a.splice(Jn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=Q.slice(o.boxes,y,1),p=await d.data();Q.dispose(d);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=st(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=x[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function e1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(_n))o.annotations[i]=_n[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function t1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-$5,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-$5,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(x=>e1(e,x,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(x=>e1(e,x,t))):(y0.boxes=await WA(e,t),$5=g(),y0.hands=await Promise.all(y0.boxes.map(x=>e1(e,x,t))),jt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[x].fingerScore&&y0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=st(i.box,Qn),d=st(i.boxRaw,Qn);y0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var D2={};ze(D2,{connected:()=>It,horizontal:()=>n1,kpt:()=>Nt,relative:()=>r1,vertical:()=>o1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],n1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],o1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],r1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],It={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),s1=0;function no(e,t){var A,a,l,c,x,i,y,d,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0;let n=g();if(!e)return he();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(w.canvas=e.canvas),e.error&&(w.error=e.error),!w.body||e.body.length!==w.body.length)w.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v((r-1)*w.body[v].box[X]+B)/r),n0=e.body[v].boxRaw.map((B,X)=>((r-1)*w.body[v].boxRaw[X]+B)/r),U=e.body[v].keypoints.map((B,X)=>{var z,ee,we,Ee,T0,P2,M1,P1,k1;return{score:B.score,part:B.part,position:[w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],w.body[v].keypoints[X]?((r-1)*(w.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[w.body[v].keypoints[X]?((r-1)*(((z=w.body[v].keypoints[X].distance)==null?void 0:z[0])||0)+(((ee=B.distance)==null?void 0:ee[0])||0))/r:(we=B.distance)==null?void 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v=0;v((r-1)*w.hand[v].box[B]+m0)/r),n0=e.hand[v].boxRaw.map((m0,B)=>((r-1)*w.hand[v].boxRaw[B]+m0)/r);w.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(w.hand[v].keypoints=e.hand[v].keypoints);let U=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((m0,B)=>m0.map((X,z)=>((r-1)*(w.hand[v].keypoints[B][z]||1)+(X||0))/r)):[],g0={};if(Object.keys(w.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)w.hand[v].annotations=e.hand[v].annotations,g0=w.hand[v].annotations;else if(e.hand[v].annotations)for(let m0 of Object.keys(e.hand[v].annotations))g0[m0]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[m0])!=null&&i[0]?e.hand[v].annotations[m0].map((B,X)=>B.map((z,ee)=>((r-1)*w.hand[v].annotations[m0][X][ee]+z)/r)):null;w.hand[v]={...e.hand[v],box:V,boxRaw:n0,keypoints:U,annotations:g0}}if(!w.face||e.face.length!==w.face.length)w.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v((r-1)*w.face[v].box[g0]+U)/r),n0=e.face[v].boxRaw.map((U,g0)=>((r-1)*w.face[v].boxRaw[g0]+U)/r);if(e.face[v].rotation){let U={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};U.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,U.angle={roll:((r-1)*(((p=(d=w.face[v].rotation)==null?void 0:d.angle)==null?void 0:p.roll)||0)+(((b=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:b.roll)||0))/r,yaw:((r-1)*(((T=(M=w.face[v].rotation)==null?void 0:M.angle)==null?void 0:T.yaw)||0)+(((h=(m=e.face[v].rotation)==null?void 0:m.angle)==null?void 0:h.yaw)||0))/r,pitch:((r-1)*(((P=(S=w.face[v].rotation)==null?void 0:S.angle)==null?void 0:P.pitch)||0)+(((q=(I=e.face[v].rotation)==null?void 0:I.angle)==null?void 0:q.pitch)||0))/r},U.gaze={bearing:((r-1)*(((t0=w.face[v].rotation)==null?void 0:t0.gaze.bearing)||0)+(((G=e.face[v].rotation)==null?void 0:G.gaze.bearing)||0))/r,strength:((r-1)*((($=w.face[v].rotation)==null?void 0:$.gaze.strength)||0)+(((A0=e.face[v].rotation)==null?void 0:A0.gaze.strength)||0))/r},w.face[v]={...e.face[v],rotation:U,box:V,boxRaw:n0}}else w.face[v]={...e.face[v],box:V,boxRaw:n0}}if(!w.object||e.object.length!==w.object.length)w.object=JSON.parse(JSON.stringify(e.object));else for(let v=0;v((r-1)*w.object[v].box[g0]+U)/r),n0=e.object[v].boxRaw.map((U,g0)=>((r-1)*w.object[v].boxRaw[g0]+U)/r);w.object[v]={...e.object[v],box:V,boxRaw:n0}}if(e.persons){let v=e.persons;if(!w.persons||v.length!==w.persons.length)w.persons=JSON.parse(JSON.stringify(v));else for(let V=0;V((r-1)*w.persons[V].box[U]+n0)/r)}e.gesture&&(w.gesture=e.gesture),w.width=e.width,w.height=e.height;let s=g();return s1=R.perfadd?s1+Math.round(s-n):Math.round(s-n),e.performance&&(w.performance={...e.performance,interpolate:s1}),w}var s0=Z(H());var j0;async function A1(e){return!j0||R.initial?j0=await O(e.segmentation.modelPath):e.debug&&u("cached model:",j0.modelUrl),j0}async function oo(e,t){var r;if(j0||(j0=await 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0:o.enabled)&&!this.models.antispoof?X3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)==null?void 0:r.enabled)&&!this.models.liveness?K3(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)==null?void 0:s.enabled)&&!this.models.faceres?B3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)==null?void 0:A.enabled)&&!this.models.emotion?C3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)==null?void 0:a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?k3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)==null?void 0:c.enabled)&&!this.models.facemesh?j3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)==null?void 0:x.enabled)&&!this.models.gear?$3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)==null?void 0:i.enabled)&&!this.models.ssrnetage?on(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)==null?void 0:y.enabled)&&!this.models.ssrnetgender?an(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)==null?void 0:d.enabled)&&!this.models.mobilefacenet?yn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)==null?void 0:p.enabled)&&!this.models.insightface?hn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)==null?void 0:f.includes("blazepose"))?n3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?t3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)==null?void 0:b.includes("efficientpose"))?i3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)==null?void 0:M.includes("movenet"))?xo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)==null?void 0:T.includes("posenet"))?Ro(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((h=(m=this.instance.config.hand.detector)==null?void 0:m.modelPath)==null?void 0:h.includes("handtrack"))?$n(this.instance.config):null,n.handskeleton=this.instance.config.hand.enabled&&this.instance.config.hand.landmarks&&!this.models.handskeleton&&((P=(S=this.instance.config.hand.detector)==null?void 0:S.modelPath)==null?void 0:P.includes("handtrack"))?eo(this.instance.config):null,(q=(I=this.instance.config.hand.detector)==null?void 0:I.modelPath)!=null&&q.includes("handdetect")&&([n.handpose,n.handskeleton]=this.models.handpose?[null,null]:await Yn(this.instance.config)),n.centernet=this.instance.config.object.enabled&&!this.models.centernet&&((t0=this.instance.config.object.modelPath)==null?void 0:t0.includes("centernet"))?s3(this.instance.config):null,n.nanodet=this.instance.config.object.enabled&&!this.models.nanodet&&((G=this.instance.config.object.modelPath)==null?void 0:G.includes("nanodet"))?mo(this.instance.config):null,n.selfie=this.instance.config.segmentation.enabled&&!this.models.selfie&&(($=this.instance.config.segmentation.modelPath)==null?void 0:$.includes("selfie"))?T1(this.instance.config):null,n.meet=this.instance.config.segmentation.enabled&&!this.models.meet&&((A0=this.instance.config.segmentation.modelPath)==null?void 0:A0.includes("meet"))?A1(this.instance.config):null,n.rvm=this.instance.config.segmentation.enabled&&!this.models.rvm&&((v=this.instance.config.segmentation.modelPath)==null?void 0:v.includes("rvm"))?g1(this.instance.config):null;for(let[V,n0]of Object.entries(n))n0!=null&&n0.then&&n0.then(U=>this.models[V]=U);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(E0).find(r=>r.startsWith(n.name));!o||(n.size=E0[o].sizeLoadedWeights,n.url=E0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Ht(this.instance,o,n);r&&t.push(r)}return t}};function jo(e,t,n,o,r){var a,l,c,x,i,y;let s=0,A=[];for(let d of e){let p={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let h of t)d.box[0]>h.box[0]&&d.box[0]h.box[1]&&d.box[1]+d.box[3]p.body.box[0]&&h.box[0]+h.box[2]p.body.box[1]&&h.box[1]+h.box[3]p.body.box[0]&&h.box[1]+h.box[3]>p.body.box[1]&&h.box[1]+h.box[3]{h&&h.length===4&&(f.push(h[0],h[0]+h[2]),b.push(h[1],h[1]+h[3]))};M(p.face.box),M((x=p.body)==null?void 0:x.box),M((i=p.hands.left)==null?void 0:i.box),M((y=p.hands.right)==null?void 0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var d0=Z(H());var Gt=` + gaze: [gaze]\xB0`, + body: "body [score]%", + bodyPart: "[label] [score]%", + object: "[label] [score]%", + hand: "[label] [score]%", + finger: "[label]", + gesture: "[where] [who]: [what]" +}; + +// src/draw/draw.ts +var drawTime = 0; +function person(inCanvas2, result, drawOptions) { + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (let i = 0; i < result.length; i++) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels) { + const label = `person #${i}`; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +function canvas2(input, output) { + if (!input || !output) + return; + const ctx = getCanvasContext(output); + if (!ctx) + return; + ctx.drawImage(input, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + if (!(result == null ? void 0 : result.performance) || !inCanvas2) + return null; + const timeStamp = now(); + const localOptions2 = mergeDeep(options2, drawOptions); + const promise = Promise.all([ + face(inCanvas2, result.face, localOptions2), + body(inCanvas2, result.body, localOptions2), + hand(inCanvas2, result.hand, localOptions2), + object(inCanvas2, result.object, localOptions2), + gesture(inCanvas2, result.gesture, localOptions2) + ]); + drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); + result.performance.draw = drawTime; + return promise; +} +function init2() { + options2.faceLabels = defaultLabels.face; + options2.bodyLabels = defaultLabels.body; + options2.bodyPartLabels = defaultLabels.bodyPart; + options2.handLabels = defaultLabels.hand; + options2.fingerLabels = defaultLabels.finger; + options2.objectLabels = defaultLabels.object; + options2.gestureLabels = defaultLabels.gesture; +} + +// src/body/blazepose.ts +var tf9 = __toESM(require_tfjs_esm()); + +// src/body/blazeposecoords.ts +var blazeposecoords_exports = {}; +__export(blazeposecoords_exports, { + connected: () => connected, + kpt: () => kpt +}); +var kpt = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftPinky", + "rightPinky", + "leftIndex", + "rightIndex", + "leftThumb", + "rightThumb", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle", + "leftHeel", + "rightHeel", + "leftFoot", + "rightFoot", + "bodyCenter", + "bodyTop", + "leftPalm", + "leftHand", + "rightPalm", + "rightHand" +]; +var connected = { + shoulders: ["leftShoulder", "rightShoulder"], + hips: ["rightHip", "leftHip"], + mouth: ["leftMouth", "rightMouth"], + leftLegUpper: ["leftHip", "leftKnee"], + leftLegLower: ["leftKnee", "leftAnkle"], + leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], + leftTorso: ["leftShoulder", "leftHip"], + leftArmUpper: ["leftShoulder", "leftElbow"], + leftArmLower: ["leftElbow", "leftWrist"], + leftHand: ["leftWrist", "leftPalm"], + leftHandPinky: ["leftPalm", "leftPinky"], + leftHandIndex: ["leftPalm", "leftIndex"], + leftHandThumb: ["leftPalm", "leftThumb"], + leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], + rightLegUpper: ["rightHip", "rightKnee"], + rightLegLower: ["rightKnee", "rightAnkle"], + rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], + rightTorso: ["rightShoulder", "rightHip"], + rightArmUpper: ["rightShoulder", "rightElbow"], + rightArmLower: ["rightElbow", "rightWrist"], + rightHand: ["rightWrist", "rightPalm"], + rightHandPinky: ["rightPalm", "rightPinky"], + rightHandIndex: ["rightPalm", "rightIndex"], + rightHandThumb: ["rightPalm", "rightThumb"], + rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] +}; + +// src/body/blazeposedetector.ts +var tf8 = __toESM(require_tfjs_esm()); +var model; +var inputSize = 224; +var anchorTensor; +var numLayers = 5; +var strides = [8, 16, 32, 32, 32]; +function createAnchors() { + const anchors3 = []; + let layerId = 0; + while (layerId < numLayers) { + let anchorCount = 0; + let lastSameStrideLayer = layerId; + while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { + anchorCount += 2; + lastSameStrideLayer++; + } + const stride = strides[layerId]; + const featureMapHeight = Math.ceil(inputSize / stride); + const featureMapWidth = Math.ceil(inputSize / stride); + for (let y = 0; y < featureMapHeight; ++y) { + for (let x = 0; x < featureMapWidth; ++x) { + for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); + } + } + } + layerId = lastSameStrideLayer; + } + anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; +} +async function loadDetector(config3) { + if (env.initial) + model = null; + if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { + model = await loadModel(config3.body["detector"].modelPath); + const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; + inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug && model) + log("cached model:", model["modelUrl"]); + createAnchors(); + return model; +} +var cropFactor = [5, 5]; +function decodeBoxes(boxesTensor, anchor) { + return tf8.tidy(() => { + const split6 = tf8.split(boxesTensor, 12, 1); + let xCenter = tf8.squeeze(split6[0]); + let yCenter = tf8.squeeze(split6[1]); + let width = tf8.squeeze(split6[2]); + let height = tf8.squeeze(split6[3]); + xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); + yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); + width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); + height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); + const xMin = tf8.sub(xCenter, tf8.div(width, 2)); + const yMin = tf8.sub(yCenter, tf8.div(height, 2)); + const xMax = tf8.add(xMin, width); + const yMax = tf8.add(yMin, height); + const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); + return boxes; + }); +} +async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { + var _a, _b; + const detectedBoxes = []; + const t2 = {}; + t2.boxes = decodeBoxes(boxesTensor, anchorTensor); + t2.scores = tf8.sigmoid(logitsTensor); + t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); + const nms = await t2.nms.data(); + const scores = await t2.scores.data(); + const boxes = await t2.boxes.array(); + for (const i of Array.from(nms)) { + const score = scores[i]; + const boxRaw = boxes[i]; + const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; + const detectedBox = { score, boxRaw, box }; + detectedBoxes.push(detectedBox); + } + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return detectedBoxes; +} +async function detectBoxes(input, config3, outputSize2) { + const t2 = {}; + t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); + t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); + t2.logits = tf8.squeeze(t2.logitsRaw); + t2.boxes = tf8.squeeze(t2.boxesRaw); + const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return boxes; +} + +// src/util/box.ts +function calc(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function square(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; + const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); + const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function scale(box, scaleFact) { + const dist = [box[2] * scaleFact, box[3] * scaleFact]; + const newBox = [ + box[0] - (dist[0] - box[2]) / 2, + box[1] - (dist[1] - box[3]) / 2, + dist[0], + dist[1] + ]; + return newBox; +} + +// src/body/blazepose.ts +var model2; +var inputSize2 = 256; +var skipped = Number.MAX_SAFE_INTEGER; +var outputNodes = { + landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], + detector: [] +}; +var cache = []; +var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; +var lastTime = 0; +var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); +var loadDetect = (config3) => loadDetector(config3); +async function loadPose(config3) { + if (env.initial) + model2 = null; + if (!model2) { + model2 = await loadModel(config3.body.modelPath); + const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; + inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug) + log("cached model:", model2["modelUrl"]); + return model2; +} +function prepareImage(input, size2, cropBox) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + let final; + if (cropBox) { + t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + } + if (input.shape[1] !== input.shape[2]) { + const height = [ + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 + ]; + const width = [ + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 + ]; + padding = [ + [0, 0], + height, + width, + [0, 0] + ]; + t2.pad = tf9.pad(t2.cropped || input, padding); + t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else if (input.shape[1] !== size2) { + t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else { + final = tf9.div(t2.cropped || input, constants.tf255); + } + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + return final; +} +function rescaleKeypoints(keypoints, outputSize2, cropBox) { + for (const kpt4 of keypoints) { + kpt4.position = [ + Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), + Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), + kpt4.position[2] + ]; + kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; + } + if (cropBox) { + const width = cropBox[2] - cropBox[0]; + const height = cropBox[3] - cropBox[1]; + for (const kpt4 of keypoints) { + kpt4.positionRaw = [ + kpt4.positionRaw[0] / height + cropBox[1], + kpt4.positionRaw[1] / width + cropBox[0], + kpt4.positionRaw[2] + ]; + kpt4.position = [ + Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), + Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), + kpt4.positionRaw[2] + ]; + } + } + return keypoints; +} +function fixKeypoints(keypoints) { + const leftPalm = keypoints.find((k) => k.part === "leftPalm"); + const leftWrist = keypoints.find((k) => k.part === "leftWrist"); + const leftIndex = keypoints.find((k) => k.part === "leftIndex"); + leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; + const rightPalm = keypoints.find((k) => k.part === "rightPalm"); + const rightWrist = keypoints.find((k) => k.part === "rightWrist"); + const rightIndex = keypoints.find((k) => k.part === "rightIndex"); + rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; +} +async function detectLandmarks(input, config3, outputSize2) { + if (!(model2 == null ? void 0 : model2["executor"])) + return null; + const t2 = {}; + [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); + const poseScore = (await t2.poseflag.data())[0]; + const points = await t2.ld.data(); + const distances = await t2.world.data(); + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + const keypointsRelative = []; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + const score = sigmoid2(points[depth * i + 3]); + const presence = sigmoid2(points[depth * i + 4]); + const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; + const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; + const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; + const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; + keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); + } + if (poseScore < (config3.body.minConfidence || 0)) + return null; + fixKeypoints(keypointsRelative); + const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); + const kpts = keypoints.map((k) => k.position); + const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; + return body4; +} +async function predict(input, config3) { + var _a, _b, _c; + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime; + const skipFrame = skipped < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { + skipped++; + } else { + let boxes = []; + if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { + const preparedImage = prepareImage(input, 224); + boxes = await detectBoxes(preparedImage, config3, outputSize2); + tf9.dispose(preparedImage); + } else { + boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; + } + for (let i = 0; i < boxes.length; i++) { + const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); + cache.length = 0; + const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); + tf9.dispose(preparedBox); + if (!bodyResult) + continue; + bodyResult.id = i; + cache.push(bodyResult); + } + lastTime = now(); + skipped = 0; + } + return cache; +} + +// src/object/centernet.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/object/labels.ts +var labels2 = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/centernet.ts +var model3; +var inputSize3 = 0; +var last2 = []; +var lastTime2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +async function load(config3) { + if (env.initial) + model3 = null; + if (!model3) { + model3 = await loadModel(config3.object.modelPath); + const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; + inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} +async function process3(res, outputShape, config3) { + if (!res) + return []; + const t2 = {}; + const results = []; + const detections = await res.array(); + t2.squeeze = tf10.squeeze(res); + const arr = tf10.split(t2.squeeze, 6, 1); + t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); + t2.boxes = tf10.squeeze(t2.stack); + t2.scores = tf10.squeeze(arr[4]); + t2.classes = tf10.squeeze(arr[5]); + tf10.dispose([res, ...arr]); + t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t2.nms.data(); + let i = 0; + for (const id of Array.from(nms)) { + const score = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + if (Number.isNaN(classVal)) + continue; + const label = labels2[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize3, + detections[0][id][1] / inputSize3 + ]; + const boxRaw = [ + x, + y, + detections[0][id][2] / inputSize3 - x, + detections[0][id][3] / inputSize3 - y + ]; + const box = [ + Math.trunc(boxRaw[0] * outputShape[0]), + Math.trunc(boxRaw[1] * outputShape[1]), + Math.trunc(boxRaw[2] * outputShape[0]), + Math.trunc(boxRaw[3] * outputShape[1]) + ]; + results.push({ id: i++, score, class: classVal, label, box, boxRaw }); + } + Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); + return results; +} +async function predict2(input, config3) { + if (!(model3 == null ? void 0 : model3["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; + const skipFrame = skipped2 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { + skipped2++; + return last2; + } + skipped2 = 0; + return new Promise(async (resolve) => { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); + const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; + lastTime2 = now(); + tf10.dispose(resize); + const obj = await process3(objectT, outputSize2, config3); + last2 = obj; + resolve(obj); + }); +} + +// src/body/efficientpose.ts +var tf11 = __toESM(require_tfjs_esm()); + +// src/body/efficientposecoords.ts +var efficientposecoords_exports = {}; +__export(efficientposecoords_exports, { + connected: () => connected2, + kpt: () => kpt2 +}); +var kpt2 = [ + "head", + "neck", + "rightShoulder", + "rightElbow", + "rightWrist", + "chest", + "leftShoulder", + "leftElbow", + "leftWrist", + "bodyCenter", + "rightHip", + "rightKnee", + "rightAnkle", + "leftHip", + "leftKnee", + "leftAnkle" +]; +var connected2 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/efficientpose.ts +var model4; +var lastTime3 = 0; +var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; +var skipped3 = Number.MAX_SAFE_INTEGER; +async function load2(config3) { + if (env.initial) + model4 = null; + if (!model4) + model4 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + const reshaped = tf11.reshape(inputs, [height * width]); + const max5 = tf11.max(reshaped, 0); + const newScore = (await max5.data())[0]; + if (newScore > minScore) { + const coordinates = tf11.argMax(reshaped, 0); + const mod3 = tf11.mod(coordinates, width); + const x = (await mod3.data())[0]; + const div15 = tf11.div(coordinates, width); + const y = (await div15.data())[0]; + tf11.dispose([reshaped, max5, coordinates, mod3, div15]); + return [x, y, newScore]; + } + tf11.dispose([reshaped, max5]); + return [0, 0, newScore]; +} +async function predict3(image28, config3) { + if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) + return []; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; + const skipFrame = skipped3 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { + skipped3++; + return [cache2]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + const tensor6 = tf11.tidy(() => { + var _a, _b; + const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); + const enhance2 = tf11.mul(resize, constants.tf2); + const norm = tf11.sub(enhance2, constants.tf1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = model4 == null ? void 0 : model4.execute(tensor6); + lastTime3 = now(); + tf11.dispose(tensor6); + if (resT) { + cache2.keypoints.length = 0; + const squeeze14 = tf11.squeeze(resT); + tf11.dispose(resT); + const stack5 = tf11.unstack(squeeze14, 2); + tf11.dispose(squeeze14); + for (let id = 0; id < stack5.length; id++) { + const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); + if (partScore > (config3.body.minConfidence || 0)) { + cache2.keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: kpt2[id], + positionRaw: [ + x2 / model4.inputs[0].shape[2], + y2 / model4.inputs[0].shape[1] + ], + position: [ + Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), + Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) + ] + }); + } + } + stack5.forEach((s) => tf11.dispose(s)); + } + cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = cache2.keypoints.map((a) => a.position[0]); + const y = cache2.keypoints.map((a) => a.position[1]); + cache2.box = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); + const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); + cache2.boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + for (const [name, indexes] of Object.entries(connected2)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + cache2.annotations[name] = pt; + } + resolve([cache2]); + }); +} + +// src/face/face.ts +var tf25 = __toESM(require_tfjs_esm()); + +// src/face/facemesh.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/face/blazeface.ts +var tf13 = __toESM(require_tfjs_esm()); + +// src/face/facemeshutil.ts +var tf12 = __toESM(require_tfjs_esm()); +var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; +var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; +var clampBox = (box, input) => box ? [ + Math.trunc(Math.max(0, box.startPoint[0])), + Math.trunc(Math.max(0, box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) +] : [0, 0, 0, 0]; +var getRawBox = (box, input) => box ? [ + box.startPoint[0] / (input.shape[2] || 0), + box.startPoint[1] / (input.shape[1] || 0), + (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), + (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) +] : [0, 0, 0, 0]; +var scaleBoxCoordinates = (box, factor) => { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; +}; +var cutAndResize = (box, image28, cropSize) => { + const h = image28.shape[1]; + const w = image28.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; + const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); + const norm = tf12.div(crop, constants.tf255); + tf12.dispose(crop); + return norm; +}; +var enlargeBox = (box, factor) => { + const center = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; +}; +var squarifyBox = (box) => { + const centers = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = Math.max(...size2) / 2; + return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; +}; +var calculateLandmarksBoundingBox = (landmarks) => { + const x = landmarks.map((d) => d[0]); + const y = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; +}; +var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); +var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +var dot = (v1, v2) => { + let product = 0; + for (let i = 0; i < v1.length; i++) + product += v1[i] * v2[i]; + return product; +}; +var getColumnFrom2DArr = (arr, columnIndex) => { + const column = []; + for (let i = 0; i < arr.length; i++) + column.push(arr[i][columnIndex]); + return column; +}; +var multiplyTransformMatrices = (mat1, mat2) => { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + return product; +}; +var buildRotationMatrix = (rotation, center) => { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +}; +var invertTransformMatrix = (matrix) => { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; + return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; +}; +var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; +function generateAnchors(inputSize10) { + const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize10 + stride - 1) / stride); + const gridCols = Math.floor((inputSize10 + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) + anchors3.push([anchorX, anchorY]); + } + } + } + return anchors3; +} +function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { + const boxSize = getBoxSize(box); + const coordsScaled = coordsRaw.map((coord) => [ + boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), + boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), + coord[2] || 0 + ]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; + const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; + const boxCenter = getBoxCenter(box); + const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + offsets[0]), + Math.trunc(coord[1] + offsets[1]), + Math.trunc(coord[2] || 0) + ]); +} +function correctFaceRotation(rotate, box, input, inputSize10) { + const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + let angle = 0; + let rotationMatrix = fixedRotationMatrix; + let face4; + if (rotate && env.kernels.includes("rotatewithoffset")) { + angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + if (largeAngle) { + const center = getBoxCenter(box); + const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; + const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + rotationMatrix = buildRotationMatrix(-angle, center); + face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); + tf12.dispose(rotated); + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + return [angle, rotationMatrix, face4]; +} +var findFaceCenter = (mesh) => { + const x = mesh.map((m) => m[0]); + const y = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; +}; +var calculateFaceBox = (mesh, previousBox) => { + const center = findFaceCenter(mesh); + const boxSize = getBoxSize(previousBox); + const calculatedBox = { + startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], + endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] + }; + return calculatedBox; +}; + +// src/face/blazeface.ts +var keypointsCount = 6; +var faceBoxScaleFactor = 1.4; +var model5; +var anchors = null; +var inputSize4 = 0; +var inputSizeT = null; +var size = () => inputSize4; +async function load3(config3) { + var _a; + if (env.initial) + model5 = null; + if (!model5) + model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model5["modelUrl"]); + inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; + inputSizeT = tf13.scalar(inputSize4, "int32"); + anchors = tf13.tensor2d(generateAnchors(inputSize4)); + return model5; +} +function decodeBoxes2(boxOutputs) { + if (!anchors || !inputSizeT) + return tf13.zeros([0, 0]); + const t2 = {}; + t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); + t2.centers = tf13.add(t2.boxStarts, anchors); + t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); + t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); + t2.centersNormalized = tf13.div(t2.centers, inputSizeT); + t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); + t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); + t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); + t2.startNormalized = tf13.mul(t2.starts, inputSizeT); + t2.endNormalized = tf13.mul(t2.ends, inputSizeT); + const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} +async function getBoxes(inputImage, config3) { + var _a, _b, _c, _d; + if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return []; + const t2 = {}; + t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t2.div = tf13.div(t2.resized, constants.tf127); + t2.normalized = tf13.sub(t2.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t2.normalized); + if (Array.isArray(res) && res.length > 2) { + const sorted = res.sort((a, b) => a.size - b.size); + t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); + t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); + t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); + t2.batch = tf13.squeeze(t2.concat, [0]); + } else if (Array.isArray(res)) { + t2.batch = tf13.squeeze(res[0]); + } else { + t2.batch = tf13.squeeze(res); + } + tf13.dispose(res); + t2.boxes = decodeBoxes2(t2.batch); + t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); + t2.sigmoid = tf13.sigmoid(t2.logits); + t2.scores = tf13.squeeze(t2.sigmoid); + t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); + const nms = await t2.nms.array(); + const boxes = []; + const scores = await t2.scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scores[nms[i]]; + if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { + const b = {}; + b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); + b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = tf13.squeeze(b.slice); + b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); + const points = await b.bbox.data(); + const rawBox = { + startPoint: [points[0], points[1]], + endPoint: [points[2], points[3]], + landmarks: await b.landmarks.array(), + confidence + }; + const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); + const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); + const squaredBox = squarifyBox(enlargedBox); + boxes.push(squaredBox); + Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); + } + } + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} + +// src/face/iris.ts +var tf14 = __toESM(require_tfjs_esm()); +var model6; +var inputSize5 = 0; +var irisEnlarge = 2.3; +var leftOutline = meshAnnotations.leftEyeLower0; +var rightOutline = meshAnnotations.rightEyeLower0; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +async function load4(config3) { + var _a, _b; + if (env.initial) + model6 = null; + if (!model6) + model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model6["modelUrl"]); + inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; + if (inputSize5 === -1) + inputSize5 = 64; + return model6; +} +function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < irisIndices.length; i++) { + const { key, indices } = irisIndices[i]; + const originalIndices = meshAnnotations[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index2 = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index2][0], + newCoords[index2][1], + (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var getLeftToRightEyeDepthDifference = (rawCoords) => { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; +}; +var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { + const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); + const boxSize = getBoxSize(box); + let crop = tf14.image.cropAndResize(face4, [[ + box.startPoint[1] / meshSize, + box.startPoint[0] / meshSize, + box.endPoint[1] / meshSize, + box.endPoint[0] / meshSize + ]], [0], [inputSize5, inputSize5]); + if (flip && env.kernels.includes("flipleftright")) { + const flipped = tf14.image.flipLeftRight(crop); + tf14.dispose(crop); + crop = flipped; + } + return { box, boxSize, crop }; +}; +var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; +}; +var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { + const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); +}; +async function augmentIris(rawCoords, face4, meshSize) { + if (!(model6 == null ? void 0 : model6["executor"])) + return rawCoords; + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); + const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); + tf14.dispose(leftEyeCrop); + tf14.dispose(rightEyeCrop); + const eyePredictions = model6.execute(combined); + tf14.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tf14.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); + const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; +} + +// src/face/attention.ts +async function augment(rawCoords, results) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + const t2 = { + lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), + irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), + eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), + irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), + eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) + }; + for (const val of Object.values(t2)) { + if (!val) + return rawCoords; + } + const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisL.length / 2; i++) + rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); + const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisR.length / 2; i++) + rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t2.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + return rawCoords; +} + +// src/face/facemesh.ts +var cache3 = { + boxes: [], + skipped: Number.MAX_SAFE_INTEGER, + timestamp: 0 +}; +var model7 = null; +var inputSize6 = 0; +async function predict4(input, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + if (!(model7 == null ? void 0 : model7["executor"])) + return []; + const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; + const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); + if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { + cache3.boxes = await getBoxes(input, config3); + cache3.timestamp = now(); + cache3.skipped = 0; + } else { + cache3.skipped++; + } + const faces = []; + const newCache = []; + let id = 0; + const size2 = inputSize6; + for (let i = 0; i < cache3.boxes.length; i++) { + const box = cache3.boxes[i]; + let angle = 0; + let rotationMatrix; + const face4 = { + id: id++, + mesh: [], + meshRaw: [], + box: [0, 0, 0, 0], + boxRaw: [0, 0, 0, 0], + score: 0, + boxScore: 0, + faceScore: 0, + annotations: {} + }; + [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); + if (config3.filter.equalization) { + const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; + tf15.dispose(face4.tensor); + if (equilized) + face4.tensor = equilized; + } + face4.boxScore = Math.round(100 * box.confidence) / 100; + if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } else if (!model7) { + if (config3.debug) + log("face mesh detection requested, but model is not loaded"); + } else { + if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { + config3.face.attention.enabled = false; + tf15.dispose(face4.tensor); + return faces; + } + const results = model7.execute(face4.tensor); + const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); + const faceConfidence = await confidenceT.data(); + face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; + if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { + box.confidence = face4.faceScore; + if (config3.face.mesh.keepInvalid) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } + } else { + const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); + const coordsReshaped = tf15.reshape(meshT, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + tf15.dispose(coordsReshaped); + if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { + rawCoords = await augment(rawCoords, results); + } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); + } + face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(meshAnnotations)) + face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); + face4.score = face4.faceScore; + const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; + face4.box = clampBox(calculatedBox, input); + face4.boxRaw = getRawBox(calculatedBox, input); + newCache.push(calculatedBox); + } + tf15.dispose(results); + } + if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) + faces.push(face4); + else + tf15.dispose(face4.tensor); + } + cache3.boxes = newCache; + return faces; +} +async function load5(config3) { + var _a, _b, _c, _d, _e, _f; + if (env.initial) + model7 = null; + if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { + if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) + model7 = null; + } + if (!model7) { + if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) + model7 = await loadModel(config3.face.attention.modelPath); + else + model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); + } else if (config3.debug) { + log("cached model:", model7["modelUrl"]); + } + inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; + return model7; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/gear/emotion.ts +var tf16 = __toESM(require_tfjs_esm()); +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model8; +var last3 = []; +var lastCount = 0; +var lastTime4 = 0; +var skipped4 = Number.MAX_SAFE_INTEGER; +async function load6(config3) { + var _a; + if (env.initial) + model8 = null; + if (!model8) + model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model8["modelUrl"]); + return model8; +} +async function predict5(image28, config3, idx, count2) { + var _a, _b; + if (!model8) + return []; + const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; + if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { + skipped4++; + return last3[idx]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + var _a2; + const obj = []; + if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { + const t2 = {}; + const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; + t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); + t2.channels = tf16.mul(t2.resize, constants.rgb); + t2.grayscale = tf16.sum(t2.channels, 3, true); + t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); + t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); + t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); + lastTime4 = now(); + const data = await t2.emotion.data(); + for (let i = 0; i < data.length; i++) { + if (data[i] > (config3.face.emotion.minConfidence || 0)) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); + } + last3[idx] = obj; + lastCount = count2; + resolve(obj); + }); +} + +// src/face/faceres.ts +var tf17 = __toESM(require_tfjs_esm()); +var model9; +var last4 = []; +var lastTime5 = 0; +var lastCount2 = 0; +var skipped5 = Number.MAX_SAFE_INTEGER; +async function load7(config3) { + var _a; + if (env.initial) + model9 = null; + if (!model9) + model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +function enhance(input) { + const tensor6 = input.image || input.tensor || input; + if (!(model9 == null ? void 0 : model9.inputs[0].shape)) + return tensor6; + const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + const norm = tf17.mul(crop, constants.tf255); + tf17.dispose(crop); + return norm; +} +async function predict6(image28, config3, idx, count2) { + var _a, _b, _c, _d; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if (!(model9 == null ? void 0 : model9["executor"])) + return obj; + const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; + if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped5++; + return last4[idx]; + } + skipped5 = 0; + return new Promise(async (resolve) => { + var _a2; + if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { + const enhanced = enhance(image28); + const resT = model9 == null ? void 0 : model9.execute(enhanced); + lastTime5 = now(); + tf17.dispose(enhanced); + const genderT = resT.find((t2) => t2.shape[1] === 1); + const gender2 = await genderT.data(); + const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; + if (confidence > (config3.face.description.minConfidence || 0)) { + obj.gender = gender2[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); + const ageIdx = (await argmax.data())[0]; + tf17.dispose(argmax); + const ageT = resT.find((t2) => t2.shape[1] === 100); + const all2 = await ageT.data(); + obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; + if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) + log("faceres error:", { model: model9, result: resT }); + const desc = resT.find((t2) => t2.shape[1] === 1024); + const descriptor = desc ? await desc.data() : []; + obj.descriptor = Array.from(descriptor); + resT.forEach((t2) => tf17.dispose(t2)); + } + last4[idx] = obj; + lastCount2 = count2; + resolve(obj); + }); +} + +// src/face/mask.ts +var expandFact = 0.1; +var alpha = 0.5; +function insidePoly(x, y, polygon) { + let inside = false; + let j = polygon.length - 1; + for (let i = 0; i < polygon.length; j = i++) { + if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + inside = !inside; + } + return inside; +} +async function mask(face4) { + if (!face4.tensor) + return face4.tensor; + if (!face4.mesh || face4.mesh.length < 100) + return face4.tensor; + const width = face4.tensor.shape[2] || 0; + const height = face4.tensor.shape[1] || 0; + const buffer = await face4.tensor.buffer(); + let silhouette = []; + for (const pt of meshAnnotations.silhouette) + silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); + if (expandFact && expandFact > 0) + silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); + for (let x = 0; x < width; x++) { + for (let y = 0; y < height; y++) { + const inside = insidePoly(x / width, y / width, silhouette); + if (!inside) { + buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); + buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); + buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); + } + } + } + const output = buffer.toTensor(); + return output; +} + +// src/face/antispoof.ts +var tf18 = __toESM(require_tfjs_esm()); +var model10; +var cached = []; +var skipped6 = Number.MAX_SAFE_INTEGER; +var lastCount3 = 0; +var lastTime6 = 0; +async function load8(config3) { + var _a; + if (env.initial) + model10 = null; + if (!model10) + model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model10["modelUrl"]); + return model10; +} +async function predict7(image28, config3, idx, count2) { + var _a, _b; + if (!(model10 == null ? void 0 : model10["executor"])) + return 0; + const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; + const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { + skipped6++; + return cached[idx]; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const res = model10 == null ? void 0 : model10.execute(resize); + const num = (await res.data())[0]; + cached[idx] = Math.round(100 * num) / 100; + lastCount3 = count2; + lastTime6 = now(); + tf18.dispose([resize, res]); + resolve(cached[idx]); + }); +} + +// src/face/liveness.ts +var tf19 = __toESM(require_tfjs_esm()); +var model11; +var cached2 = []; +var skipped7 = Number.MAX_SAFE_INTEGER; +var lastCount4 = 0; +var lastTime7 = 0; +async function load9(config3) { + var _a; + if (env.initial) + model11 = null; + if (!model11) + model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model11["modelUrl"]); + return model11; +} +async function predict8(image28, config3, idx, count2) { + var _a, _b; + if (!(model11 == null ? void 0 : model11["executor"])) + return 0; + const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; + const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { + skipped7++; + return cached2[idx]; + } + skipped7 = 0; + return new Promise(async (resolve) => { + const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const res = model11 == null ? void 0 : model11.execute(resize); + const num = (await res.data())[0]; + cached2[idx] = Math.round(100 * num) / 100; + lastCount4 = count2; + lastTime7 = now(); + tf19.dispose([resize, res]); + resolve(cached2[idx]); + }); +} + +// src/gear/gear.ts +var tf20 = __toESM(require_tfjs_esm()); +var model12; +var last5 = []; +var raceNames = ["white", "black", "asian", "indian", "other"]; +var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; +var lastCount5 = 0; +var lastTime8 = 0; +var skipped8 = Number.MAX_SAFE_INTEGER; +async function load10(config3) { + var _a; + if (env.initial) + model12 = null; + if (!model12) + model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model12["modelUrl"]); + return model12; +} +async function predict9(image28, config3, idx, count2) { + var _a, _b; + if (!model12) + return { age: 0, gender: "unknown", genderScore: 0, race: [] }; + const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; + if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { + skipped8++; + return last5[idx]; + } + skipped8 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + if (!(model12 == null ? void 0 : model12.inputs[0].shape)) + return; + const t2 = {}; + const box = [[0, 0.1, 0.9, 0.9]]; + t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; + if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) + [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t2.gender.data(); + obj.gender = gender2[0] > gender2[1] ? "male" : "female"; + obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; + const race = await t2.race.data(); + for (let i = 0; i < race.length; i++) { + if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) + obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); + } + obj.race.sort((a, b) => b.score - a.score); + const ageDistribution = Array.from(await t2.age.data()); + const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); + let age2 = ageSorted[0][0]; + for (let i = 1; i < ageSorted.length; i++) + age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); + obj.age = Math.round(10 * age2) / 10; + Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); + last5[idx] = obj; + lastCount5 = count2; + lastTime8 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-age.ts +var tf21 = __toESM(require_tfjs_esm()); +var model13; +var last6 = []; +var lastCount6 = 0; +var lastTime9 = 0; +var skipped9 = Number.MAX_SAFE_INTEGER; +async function load11(config3) { + if (env.initial) + model13 = null; + if (!model13) + model13 = await loadModel(config3.face["ssrnet"].modelPathAge); + else if (config3.debug) + log("cached model:", model13["modelUrl"]); + return model13; +} +async function predict10(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model13) + return { age: 0 }; + const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; + if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { + skipped9++; + return last6[idx]; + } + skipped9 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) + return; + const t2 = {}; + t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t2.enhance = tf21.mul(t2.resize, constants.tf255); + const obj = { age: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.age = model13.execute(t2.enhance); + if (t2.age) { + const data = await t2.age.data(); + obj.age = Math.trunc(10 * data[0]) / 10; + } + Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); + last6[idx] = obj; + lastCount6 = count2; + lastTime9 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-gender.ts +var tf22 = __toESM(require_tfjs_esm()); +var model14; +var last7 = []; +var lastCount7 = 0; +var lastTime10 = 0; +var skipped10 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load12(config3) { + var _a; + if (env.initial) + model14 = null; + if (!model14) + model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); + else if (config3.debug) + log("cached model:", model14["modelUrl"]); + return model14; +} +async function predict11(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model14) + return { gender: "unknown", genderScore: 0 }; + const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; + if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped10++; + return last7[idx]; + } + skipped10 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model14 == null ? void 0 : model14.inputs[0].shape)) + return; + const t2 = {}; + t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t2.enhance = tf22.tidy(() => { + const [red, green, blue] = tf22.split(t2.resize, 3, 3); + const redNorm = tf22.mul(red, rgb[0]); + const greenNorm = tf22.mul(green, rgb[1]); + const blueNorm = tf22.mul(blue, rgb[2]); + const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); + const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); + return normalize2; + }); + const obj = { gender: "unknown", genderScore: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.gender = model14.execute(t2.enhance); + const data = await t2.gender.data(); + obj.gender = data[0] > data[1] ? "female" : "male"; + obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; + Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); + last7[idx] = obj; + lastCount7 = count2; + lastTime10 = now(); + resolve(obj); + }); +} + +// src/face/mobilefacenet.ts +var tf23 = __toESM(require_tfjs_esm()); +var model15; +var last8 = []; +var lastCount8 = 0; +var lastTime11 = 0; +var skipped11 = Number.MAX_SAFE_INTEGER; +async function load13(config3) { + var _a; + if (env.initial) + model15 = null; + if (!model15) + model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model15["modelUrl"]); + return model15; +} +async function predict12(input, config3, idx, count2) { + var _a, _b; + if (!(model15 == null ? void 0 : model15["executor"])) + return []; + const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; + if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { + skipped11++; + return last8[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t2.data = model15.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); + } + last8[idx] = data; + lastCount8 = count2; + lastTime11 = now(); + resolve(data); + }); +} + +// src/face/insightface.ts +var tf24 = __toESM(require_tfjs_esm()); +var model16; +var last9 = []; +var lastCount9 = 0; +var lastTime12 = 0; +var skipped12 = Number.MAX_SAFE_INTEGER; +async function load14(config3) { + if (env.initial) + model16 = null; + if (!model16) + model16 = await loadModel(config3.face["insightface"].modelPath); + else if (config3.debug) + log("cached model:", model16["modelUrl"]); + return model16; +} +async function predict13(input, config3, idx, count2) { + var _a, _b; + if (!(model16 == null ? void 0 : model16["executor"])) + return []; + const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; + if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { + skipped12++; + return last9[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t2.data = model16.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); + } + last9[idx] = data; + lastCount9 = count2; + lastTime12 = now(); + resolve(data); + }); +} + +// src/face/angles.ts +var calculateGaze = (face4) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); + const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; + const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; + const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); + strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face4, imageSize) => { + const normalize2 = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + if (Number.isNaN(thetaX)) + thetaX = 0; + if (Number.isNaN(thetaY)) + thetaY = 0; + if (Number.isNaN(thetaZ)) + thetaZ = 0; + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const mesh = face4.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); + const yAxis = normalize2(subVectors(pts[1], pts[0])); + let xAxis = normalize2(subVectors(pts[3], pts[2])); + const zAxis = normalize2(crossVectors(xAxis, yAxis)); + xAxis = crossVectors(yAxis, zAxis); + const matrix = [ + xAxis[0], + xAxis[1], + xAxis[2], + yAxis[0], + yAxis[1], + yAxis[2], + zAxis[0], + zAxis[1], + zAxis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; + +// src/face/anthropometry.ts +function calculateCameraDistance(face4, width) { + const f = face4 == null ? void 0 : face4.annotations; + if (!f) + return 0; + const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; + const cameraDistance = Math.round(1.17 / irisSize) / 100; + return cameraDistance; +} + +// src/face/face.ts +var detectFace = async (instance, input) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + let timeStamp = now(); + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let mobilefacenetRes; + let insightfaceRes; + let antispoofRes; + let livenessRes; + let descRes; + const faceRes = []; + instance.state = "run:face"; + const faces = await predict4(input, instance.config); + instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + instance.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { + const masked = await mask(faces[i]); + tf25.dispose(faces[i].tensor); + if (masked) + faces[i].tensor = masked; + } + const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; + instance.analyze("Start Emotion:"); + if (instance.config.async) { + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + } else { + instance.state = "run:emotion"; + timeStamp = now(); + emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Emotion:"); + instance.analyze("Start AntiSpoof:"); + if (instance.config.async) { + antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:antispoof"; + timeStamp = now(); + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End AntiSpoof:"); + instance.analyze("Start Liveness:"); + if (instance.config.async) { + livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:liveness"; + timeStamp = now(); + livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Liveness:"); + instance.analyze("Start GEAR:"); + if (instance.config.async) { + gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:gear"; + timeStamp = now(); + gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.gear = Math.trunc(now() - timeStamp); + } + instance.analyze("End GEAR:"); + instance.analyze("Start SSRNet:"); + if (instance.config.async) { + ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:ssrnet"; + timeStamp = now(); + ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.ssrnet = Math.trunc(now() - timeStamp); + } + instance.analyze("End SSRNet:"); + instance.analyze("Start MobileFaceNet:"); + if (instance.config.async) { + mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End MobileFaceNet:"); + instance.analyze("Start InsightFace:"); + if (instance.config.async) { + insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End InsightFace:"); + instance.analyze("Start Description:"); + if (instance.config.async) { + descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + } else { + instance.state = "run:description"; + timeStamp = now(); + descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Description:"); + if (instance.config.async) { + [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); + } + instance.analyze("Finish Face:"); + if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { + descRes = { + ...descRes, + age: ageRes.age, + gender: genderRes.gender, + genderScore: genderRes.genderScore + }; + } + if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { + descRes = { + ...descRes, + age: gearRes.age, + gender: gearRes.gender, + genderScore: gearRes.genderScore, + race: gearRes.race + }; + } + if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { + descRes.descriptor = mobilefacenetRes; + } + if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { + descRes.descriptor = insightfaceRes; + } + const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; + const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; + tf25.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + const res = { + ...faces[i], + id: i + }; + if (descRes.age) + res.age = descRes.age; + if (descRes.gender) + res.gender = descRes.gender; + if (descRes.genderScore) + res.genderScore = descRes.genderScore; + if (descRes.descriptor) + res.embedding = descRes.descriptor; + if (descRes.race) + res.race = descRes.race; + if (emotionRes) + res.emotion = emotionRes; + if (antispoofRes) + res.real = antispoofRes; + if (livenessRes) + res.live = livenessRes; + if (irisSize > 0) + res.distance = irisSize; + if (rotation) + res.rotation = rotation; + if (tensor6) + res.tensor = tensor6; + faceRes.push(res); + instance.analyze("End Face"); + } + instance.analyze("End FaceMesh:"); + if (instance.config.async) { + if (instance.performance.face) + delete instance.performance.face; + if (instance.performance.age) + delete instance.performance.age; + if (instance.performance.gender) + delete instance.performance.gender; + if (instance.performance.emotion) + delete instance.performance.emotion; + } + return faceRes; +}; + +// src/hand/fingerdef.ts +var Finger = { + thumb: 0, + index: 1, + middle: 2, + ring: 3, + pinky: 4, + all: [0, 1, 2, 3, 4], + nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, + pointsMapping: { + 0: [[0, 1], [1, 2], [2, 3], [3, 4]], + 1: [[0, 5], [5, 6], [6, 7], [7, 8]], + 2: [[0, 9], [9, 10], [10, 11], [11, 12]], + 3: [[0, 13], [13, 14], [14, 15], [15, 16]], + 4: [[0, 17], [17, 18], [18, 19], [19, 20]] + }, + getName: (value) => Finger.nameMapping[value], + getPoints: (value) => Finger.pointsMapping[value] +}; +var FingerCurl = { + none: 0, + half: 1, + full: 2, + nameMapping: { 0: "none", 1: "half", 2: "full" }, + getName: (value) => FingerCurl.nameMapping[value] +}; +var FingerDirection = { + verticalUp: 0, + verticalDown: 1, + horizontalLeft: 2, + horizontalRight: 3, + diagonalUpRight: 4, + diagonalUpLeft: 5, + diagonalDownRight: 6, + diagonalDownLeft: 7, + nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, + getName: (value) => FingerDirection.nameMapping[value] +}; +var FingerGesture = class { + constructor(name) { + __publicField(this, "name"); + __publicField(this, "curls"); + __publicField(this, "directions"); + __publicField(this, "weights"); + __publicField(this, "weightsRelative"); + this.name = name; + this.curls = {}; + this.directions = {}; + this.weights = [1, 1, 1, 1, 1]; + this.weightsRelative = [1, 1, 1, 1, 1]; + } + curl(finger, curl, confidence) { + if (typeof this.curls[finger] === "undefined") + this.curls[finger] = []; + this.curls[finger].push([curl, confidence]); + } + direction(finger, position, confidence) { + if (!this.directions[finger]) + this.directions[finger] = []; + this.directions[finger].push([position, confidence]); + } + weight(finger, weight) { + this.weights[finger] = weight; + const total = this.weights.reduce((a, b) => a + b, 0); + this.weightsRelative = this.weights.map((el) => el * 5 / total); + } + matchAgainst(detectedCurls, detectedDirections) { + let confidence = 0; + for (const fingerIdx in detectedCurls) { + const detectedCurl = detectedCurls[fingerIdx]; + const expectedCurls = this.curls[fingerIdx]; + if (typeof expectedCurls === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedCurl, score] of expectedCurls) { + if (detectedCurl === expectedCurl) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + for (const fingerIdx in detectedDirections) { + const detectedDirection = detectedDirections[fingerIdx]; + const expectedDirections = this.directions[fingerIdx]; + if (typeof expectedDirections === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedDirection, score] of expectedDirections) { + if (detectedDirection === expectedDirection) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + return confidence / 10; + } +}; + +// src/hand/fingergesture.ts +var { thumb, index, middle, ring, pinky } = Finger; +var { none, half, full } = FingerCurl; +var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; +var ThumbsUp = new FingerGesture("thumbs up"); +ThumbsUp.curl(thumb, none, 1); +ThumbsUp.direction(thumb, verticalUp, 1); +ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); +ThumbsUp.direction(thumb, diagonalUpRight, 0.25); +for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { + ThumbsUp.curl(finger, full, 1); + ThumbsUp.direction(finger, horizontalLeft, 1); + ThumbsUp.direction(finger, horizontalRight, 1); +} +var Victory = new FingerGesture("victory"); +Victory.curl(thumb, half, 0.5); +Victory.curl(thumb, none, 0.5); +Victory.direction(thumb, verticalUp, 1); +Victory.direction(thumb, diagonalUpLeft, 1); +Victory.curl(index, none, 1); +Victory.direction(index, verticalUp, 0.75); +Victory.direction(index, diagonalUpLeft, 1); +Victory.curl(middle, none, 1); +Victory.direction(middle, verticalUp, 1); +Victory.direction(middle, diagonalUpLeft, 0.75); +Victory.curl(ring, full, 1); +Victory.direction(ring, verticalUp, 0.2); +Victory.direction(ring, diagonalUpLeft, 1); +Victory.direction(ring, horizontalLeft, 0.2); +Victory.curl(pinky, full, 1); +Victory.direction(pinky, verticalUp, 0.2); +Victory.direction(pinky, diagonalUpLeft, 1); +Victory.direction(pinky, horizontalLeft, 0.2); +Victory.weight(index, 2); +Victory.weight(middle, 2); +var Point = new FingerGesture("point"); +Point.curl(thumb, full, 1); +Point.curl(index, none, 0.5); +Point.curl(middle, full, 0.5); +Point.curl(ring, full, 0.5); +Point.curl(pinky, full, 0.5); +Point.weight(index, 2); +Point.weight(middle, 2); +var MiddleFinger = new FingerGesture("middle finger"); +MiddleFinger.curl(thumb, none, 1); +MiddleFinger.curl(index, full, 0.5); +MiddleFinger.curl(middle, full, 0.5); +MiddleFinger.curl(ring, full, 0.5); +MiddleFinger.curl(pinky, full, 0.5); +MiddleFinger.weight(index, 2); +MiddleFinger.weight(middle, 2); +var OpenPalm = new FingerGesture("open palm"); +OpenPalm.curl(thumb, none, 0.75); +OpenPalm.curl(index, none, 0.75); +OpenPalm.curl(middle, none, 0.75); +OpenPalm.curl(ring, none, 0.75); +OpenPalm.curl(pinky, none, 0.75); +var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; + +// src/hand/fingerpose.ts +var minConfidence = 0.7; +var options3 = { + HALF_CURL_START_LIMIT: 60, + NO_CURL_START_LIMIT: 130, + DISTANCE_VOTE_POWER: 1.1, + SINGLE_ANGLE_VOTE_POWER: 0.9, + TOTAL_ANGLE_VOTE_POWER: 1.6 +}; +function calculateSlope(point1x, point1y, point2x, point2y) { + const value = (point1y - point2y) / (point1x - point2x); + let slope = Math.atan(value) * 180 / Math.PI; + if (slope <= 0) + slope = -slope; + else if (slope > 0) + slope = 180 - slope; + return slope; +} +function getSlopes(point1, point2) { + if (!point1 || !point2) + return [0, 0]; + const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); + if (point1.length === 2) + return slopeXY; + const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); + return [slopeXY, slopeYZ]; +} +function angleOrientationAt(angle, weightageAt = 1) { + let isVertical = 0; + let isDiagonal = 0; + let isHorizontal = 0; + if (angle >= 75 && angle <= 105) + isVertical = 1 * weightageAt; + else if (angle >= 25 && angle <= 155) + isDiagonal = 1 * weightageAt; + else + isHorizontal = 1 * weightageAt; + return [isVertical, isDiagonal, isHorizontal]; +} +function estimateFingerCurl(startPoint, midPoint, endPoint) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const start_mid_z_dist = startPoint[2] - midPoint[2]; + const start_end_z_dist = startPoint[2] - endPoint[2]; + const mid_end_z_dist = midPoint[2] - endPoint[2]; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); + let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); + if (cos_in > 1) + cos_in = 1; + else if (cos_in < -1) + cos_in = -1; + let angleOfCurve = Math.acos(cos_in); + angleOfCurve = 57.2958 * angleOfCurve % 180; + let fingerCurl; + if (angleOfCurve > options3.NO_CURL_START_LIMIT) + fingerCurl = FingerCurl.none; + else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) + fingerCurl = FingerCurl.half; + else + fingerCurl = FingerCurl.full; + return fingerCurl; +} +function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + if (max_dist_x === Math.abs(start_end_x_dist)) { + if (start_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else if (max_dist_x === Math.abs(start_mid_x_dist)) { + if (start_mid_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else { + if (mid_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } + return estimatedDirection; +} +function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { + let estimatedDirection; + if (max_dist_y === Math.abs(start_end_y_dist)) { + if (start_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else if (max_dist_y === Math.abs(start_mid_y_dist)) { + if (start_mid_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else { + if (mid_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } + return estimatedDirection; +} +function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + if (reqd_vertical_direction === FingerDirection.verticalUp) { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalUpLeft; + else + estimatedDirection = FingerDirection.diagonalUpRight; + } else { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalDownLeft; + else + estimatedDirection = FingerDirection.diagonalDownRight; + } + return estimatedDirection; +} +function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); + const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); + let voteVertical = 0; + let voteDiagonal = 0; + let voteHorizontal = 0; + const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); + if (start_end_x_y_dist_ratio > 1.5) + voteVertical += options3.DISTANCE_VOTE_POWER; + else if (start_end_x_y_dist_ratio > 0.66) + voteDiagonal += options3.DISTANCE_VOTE_POWER; + else + voteHorizontal += options3.DISTANCE_VOTE_POWER; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); + const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); + let calc_start_point_x = startPoint[0]; + let calc_start_point_y = startPoint[1]; + let calc_end_point_x = endPoint[0]; + let calc_end_point_y = endPoint[1]; + if (max_dist === start_mid_dist) { + calc_end_point_x = endPoint[0]; + calc_end_point_y = endPoint[1]; + } else if (max_dist === mid_end_dist) { + calc_start_point_x = midPoint[0]; + calc_start_point_y = midPoint[1]; + } + const calcStartPoint = [calc_start_point_x, calc_start_point_y]; + const calcEndPoint = [calc_end_point_x, calc_end_point_y]; + const totalAngle = getSlopes(calcStartPoint, calcEndPoint); + const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); + voteVertical += votes[0]; + voteDiagonal += votes[1]; + voteHorizontal += votes[2]; + for (const fingerSlope of fingerSlopes) { + const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); + voteVertical += fingerVotes[0]; + voteDiagonal += fingerVotes[1]; + voteHorizontal += fingerVotes[2]; + } + let estimatedDirection; + if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } else { + estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } + return estimatedDirection; +} +function estimate(landmarks) { + const slopesXY = []; + const slopesYZ = []; + const fingerCurls = []; + const fingerDirections = []; + if (!landmarks) + return { curls: fingerCurls, directions: fingerDirections }; + for (const finger of Finger.all) { + const points = Finger.getPoints(finger); + const slopeAtXY = []; + const slopeAtYZ = []; + for (const point2 of points) { + const point1 = landmarks[point2[0]]; + const point22 = landmarks[point2[1]]; + const slopes = getSlopes(point1, point22); + const slopeXY = slopes[0]; + const slopeYZ = slopes[1]; + slopeAtXY.push(slopeXY); + slopeAtYZ.push(slopeYZ); + } + slopesXY.push(slopeAtXY); + slopesYZ.push(slopeAtYZ); + } + for (const finger of Finger.all) { + const pointIndexAt = finger === Finger.thumb ? 1 : 0; + const fingerPointsAt = Finger.getPoints(finger); + const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; + const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; + const endPoint = landmarks[fingerPointsAt[3][1]]; + const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); + const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); + fingerCurls[finger] = fingerCurled; + fingerDirections[finger] = fingerPosition; + } + return { curls: fingerCurls, directions: fingerDirections }; +} +function analyze(keypoints) { + if (!keypoints || keypoints.length === 0) + return null; + const estimatorRes = estimate(keypoints); + const landmarks = {}; + for (const fingerIdx of Finger.all) { + landmarks[Finger.getName(fingerIdx)] = { + curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), + direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) + }; + } + return landmarks; +} +function match(keypoints) { + const poses = []; + if (!keypoints || keypoints.length === 0) + return poses; + const estimatorRes = estimate(keypoints); + for (const gesture2 of fingergesture_default) { + const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); + if (confidence >= minConfidence) + poses.push({ name: gesture2.name, confidence }); + } + return poses; +} + +// src/gesture/gesture.ts +var body2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); + } + } + return gestures; +}; +var face2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 450) { + const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); + const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; + if (Math.abs(zDiff / xDiff) <= 0.15) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); + const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2] || 0; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris2 = (res) => { + var _a, _b, _c, _d; + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > rightIrisCenterX) { + if (leftIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking right" }); + } else { + if (rightIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking left" }); + } + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + if (res[i].annotations) { + for (const [finger, pos] of Object.entries(res[i].annotations)) { + if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + if (res[i].keypoints) { + const poses = match(res[i].keypoints); + for (const pose of poses) + gestures.push({ hand: i, gesture: pose.name }); + } + } + return gestures; +}; + +// src/hand/handposedetector.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/hand/handposeutil.ts +var tf26 = __toESM(require_tfjs_esm()); +function getBoxSize2(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; +} +function getBoxCenter2(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box, image28, cropSize) { + const h = image28.shape[1]; + const w = image28.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tf26.image.cropAndResize(image28, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; +} +function enlargeBox2(box, factor = 1.5) { + const center = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function squarifyBox2(box) { + const centers = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const maxEdge = Math.max(...size2); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/hand/handposeanchors.ts +var anchors2 = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 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+ { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/hand/handposedetector.ts +var HandDetector = class { + constructor(model23) { + __publicField(this, "model"); + __publicField(this, "anchors"); + __publicField(this, "anchorsTensor"); + __publicField(this, "inputSize"); + __publicField(this, "inputSizeTensor"); + __publicField(this, "doubleInputSizeTensor"); + var _a, _b, _c, _d; + this.model = model23; + this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tf27.tensor2d(this.anchors); + this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; + this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + const t2 = {}; + t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); + t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); + t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); + t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); + t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); + t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); + t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); + t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); + t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); + const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + normalizeLandmarks(rawPalmLandmarks, index2) { + const t2 = {}; + t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); + t2.div = tf27.div(t2.reshape, this.inputSizeTensor); + t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = tf27.mul(t2.landmarks, this.inputSizeTensor); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + async predict(input, config3) { + var _a; + const t2 = {}; + t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); + t2.div = tf27.div(t2.resize, constants.tf127); + t2.image = tf27.sub(t2.div, constants.tf1); + t2.batched = this.model.execute(t2.image); + t2.predictions = tf27.squeeze(t2.batched); + t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); + t2.sigmoid = tf27.sigmoid(t2.slice); + t2.scores = tf27.squeeze(t2.sigmoid); + const scores = await t2.scores.data(); + t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); + t2.norm = this.normalizeBoxes(t2.boxes); + t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t2.nms.array(); + const hands = []; + for (const index2 of nms) { + const p = {}; + p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); + p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); + p.norm = this.normalizeLandmarks(p.slice, index2); + p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); + const box = await p.box.data(); + const startPoint = box.slice(0, 2); + const endPoint = box.slice(2, 4); + const palmLandmarks = await p.palmLandmarks.array(); + const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; + const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); + hands.push(scaled); + Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); + } + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return hands; + } +}; + +// src/hand/handposepipeline.ts +var tf28 = __toESM(require_tfjs_esm()); +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var lastTime13 = 0; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + __publicField(this, "handDetector"); + __publicField(this, "handPoseModel"); + __publicField(this, "inputSize"); + __publicField(this, "storedBoxes"); + __publicField(this, "skipped"); + __publicField(this, "detectedHands"); + var _a, _b, _c; + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; + this.storedBoxes = []; + this.skipped = Number.MAX_SAFE_INTEGER; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box2, angle, rotationMatrix) { + const boxSize = getBoxSize2(box2); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box2), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image28, config3) { + let useFreshBox = false; + let boxes; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; + const skipFrame = this.skipped < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + boxes = await this.handDetector.predict(image28, config3); + this.skipped = 0; + } + if (config3.skipAllowed) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tf28.div(croppedInput, constants.tf255); + tf28.dispose(croppedInput); + tf28.dispose(rotatedImage); + const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); + lastTime13 = now(); + tf28.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tf28.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence / 4) { + const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tf28.dispose(keypoints); + tf28.dispose(keypointsReshaped); + const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords, + confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tf28.dispose(keypoints); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: 0, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, + landmarks: [] + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + if (hands.length > config3.hand.maxDetected) + hands.length = config3.hand.maxDetected; + return hands; + } +}; + +// src/hand/handpose.ts +var meshAnnotations2 = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palm: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict14(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations2 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations2)) { + annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); + } + } + const keypoints = predictions[i].landmarks; + let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw = [0, 0, 0, 0]; + if (keypoints && keypoints.length > 0) { + for (const pt of keypoints) { + if (pt[0] < box[0]) + box[0] = pt[0]; + if (pt[1] < box[1]) + box[1] = pt[1]; + if (pt[0] > box[2]) + box[2] = pt[0]; + if (pt[1] > box[3]) + box[3] = pt[1]; + } + box[2] -= box[0]; + box[3] -= box[1]; + boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; + } else { + box = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + const landmarks = analyze(keypoints); + hands.push({ + id: i, + score: Math.round(100 * predictions[i].confidence) / 100, + boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, + fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, + label: "hand", + box, + boxRaw, + keypoints, + annotations: annotations2, + landmarks + }); + } + return hands; +} +async function load15(config3) { + var _a, _b; + if (env.initial) { + handDetectorModel = null; + handPoseModel = null; + } + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, + config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null + ]); + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; + if (handDetector && handPoseModel) + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/hand/handtrack.ts +var tf29 = __toESM(require_tfjs_esm()); +var models2 = [null, null]; +var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; +var inputSize7 = [[0, 0], [0, 0]]; +var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; +var faceIndex = 4; +var boxExpandFact = 1.6; +var maxDetectorResolution = 512; +var detectorExpandFact = 1.4; +var skipped13 = Number.MAX_SAFE_INTEGER; +var lastTime14 = 0; +var outputSize = [0, 0]; +var cache4 = { + boxes: [], + hands: [] +}; +var fingerMap = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + base: [0], + palm: [0, 17, 13, 9, 5, 1, 0] +}; +async function loadDetect2(config3) { + var _a; + if (env.initial) + models2[0] = null; + if (!models2[0]) { + fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); + models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); + const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; + inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[0]["modelUrl"]); + return models2[0]; +} +async function loadSkeleton(config3) { + var _a; + if (env.initial) + models2[1] = null; + if (!models2[1]) { + models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); + const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; + inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[1]["modelUrl"]); + return models2[1]; +} +async function detectHands(input, config3) { + const hands = []; + if (!input || !models2[0]) + return hands; + const t2 = {}; + const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); + const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); + const width = Math.round(height * ratio2 / 8) * 8; + t2.resize = tf29.image.resizeBilinear(input, [height, width]); + t2.cast = tf29.cast(t2.resize, "int32"); + [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); + t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); + t2.scores = tf29.squeeze(t2.rawScores, [0]); + const classScores = tf29.unstack(t2.scores, 1); + tf29.dispose(classScores[faceIndex]); + classScores.splice(faceIndex, 1); + t2.filtered = tf29.stack(classScores, 1); + tf29.dispose(classScores); + t2.max = tf29.max(t2.filtered, 1); + t2.argmax = tf29.argMax(t2.filtered, 1); + let id = 0; + t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t2.nms.data(); + const scores = await t2.max.data(); + const classNum = await t2.argmax.data(); + for (const nmsIndex of Array.from(nms)) { + const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); + const boxYX = await boxSlice.data(); + tf29.dispose(boxSlice); + const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; + const boxRaw = scale(boxData, detectorExpandFact); + const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; + const score = scores[nmsIndex]; + const label = classes[classNum[nmsIndex]]; + const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; + hands.push(hand3); + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + hands.sort((a, b) => b.score - a.score); + if (hands.length > (config3.hand.maxDetected || 1)) + hands.length = config3.hand.maxDetected || 1; + return hands; +} +async function detectFingers(input, h, config3) { + const hand3 = { + id: h.id, + score: Math.round(100 * h.score) / 100, + boxScore: Math.round(100 * h.score) / 100, + fingerScore: 0, + box: h.box, + boxRaw: h.boxRaw, + label: h.label, + keypoints: [], + landmarks: {}, + annotations: {} + }; + if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { + const t2 = {}; + const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; + t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t2.div = tf29.div(t2.crop, constants.tf255); + [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); + const rawScore = (await t2.score.data())[0]; + const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; + if (score >= (config3.hand.minConfidence || 0)) { + hand3.fingerScore = score; + t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); + const coordsData = await t2.reshaped.array(); + const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); + const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); + hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); + hand3.landmarks = analyze(hand3.keypoints); + for (const key of Object.keys(fingerMap)) { + hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); + } + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + } + return hand3; +} +async function predict15(input, config3) { + var _a, _b; + if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) + return []; + outputSize = [input.shape[2] || 0, input.shape[1] || 0]; + skipped13++; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrame = skipped13 < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache4.hands; + } + return new Promise(async (resolve) => { + const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); + if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else { + cache4.boxes = await detectHands(input, config3); + lastTime14 = now(); + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + skipped13 = 0; + } + const oldCache = [...cache4.boxes]; + cache4.boxes.length = 0; + if (config3.cacheSensitivity > 0) { + for (let i = 0; i < cache4.hands.length; i++) { + const boxKpt = square(cache4.hands[i].keypoints, outputSize); + if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { + const boxScale = scale(boxKpt.box, boxExpandFact); + const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); + cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); + } + } + } + for (let i = 0; i < cache4.hands.length; i++) { + const bbox = calc(cache4.hands[i].keypoints, outputSize); + cache4.hands[i].box = bbox.box; + cache4.hands[i].boxRaw = bbox.boxRaw; + } + resolve(cache4.hands); + }); +} + +// src/result.ts +var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); + +// src/body/movenetcoords.ts +var movenetcoords_exports = {}; +__export(movenetcoords_exports, { + connected: () => connected3, + horizontal: () => horizontal, + kpt: () => kpt3, + relative: () => relative, + vertical: () => vertical +}); +var kpt3 = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var horizontal = [ + ["leftEye", "rightEye"], + ["leftEar", "rightEar"], + ["leftShoulder", "rightShoulder"], + ["leftElbow", "rightElbow"], + ["leftWrist", "rightWrist"], + ["leftHip", "rightHip"], + ["leftKnee", "rightKnee"], + ["leftAnkle", "rightAnkle"] +]; +var vertical = [ + ["leftKnee", "leftShoulder"], + ["rightKnee", "rightShoulder"], + ["leftAnkle", "leftKnee"], + ["rightAnkle", "rightKnee"] +]; +var relative = [ + [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], + [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] +]; +var connected3 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/util/interpolate.ts +var bufferedResult = empty(); +var interpolateTime = 0; +function calc2(newResult, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + const t0 = now(); + if (!newResult) + return empty(); + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; + if (newResult.canvas) + bufferedResult.canvas = newResult.canvas; + if (newResult.error) + bufferedResult.error = newResult.error; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { + var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + return { + score: newKpt.score, + part: newKpt.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + ], + distance: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + ] + }; + }); + const annotations2 = {}; + let coords = { connected: {} }; + if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) + coords = efficientposecoords_exports; + else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + coords = blazeposecoords_exports; + else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) + coords = movenetcoords_exports; + for (const [name, indexes] of Object.entries(coords.connected)) { + const pt = []; + for (let j = 0; j < indexes.length - 1; j++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[j]); + const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) + bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + let annotations2 = {}; + if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { + bufferedResult.hand[i].annotations = newResult.hand[i].annotations; + annotations2 = bufferedResult.hand[i].annotations; + } else if (newResult.hand[i].annotations) { + for (const key of Object.keys(newResult.hand[i].annotations)) { + annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + } + } + bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + if (newResult.face[i].rotation) { + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; + } else { + bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; + } + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + bufferedResult.width = newResult.width; + bufferedResult.height = newResult.height; + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); + if (newResult.performance) + bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; + return bufferedResult; +} + +// src/segmentation/meet.ts +var tf30 = __toESM(require_tfjs_esm()); +var model17; +async function load16(config3) { + if (!model17 || env.initial) + model17 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model17["modelUrl"]); + return model17; +} +async function predict16(input, config3) { + var _a; + if (!model17) + model17 = await load16(config3); + if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t2.norm = tf30.div(t2.resize, constants.tf255); + t2.res = model17.execute(t2.norm); + t2.squeeze = tf30.squeeze(t2.res, [0]); + [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); + t2.fg = tf30.softmax(t2.fgRaw); + t2.mul = tf30.mul(t2.fg, constants.tf255); + t2.expand = tf30.expandDims(t2.mul, 2); + t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf30.squeeze(input); + t2.concat = tf30.concat([t2.input, t2.output], -1); + rgba = tf30.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf30.cast(t2.output, "int32"); + break; + default: + rgba = tf30.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); + return rgba; +} + +// src/face/match.ts +var match_exports = {}; +__export(match_exports, { + distance: () => distance, + find: () => find, + similarity: () => similarity +}); +function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { + if (!descriptor1 || !descriptor1) + return Number.MAX_SAFE_INTEGER; + let sum3 = 0; + for (let i = 0; i < descriptor1.length; i++) { + const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); + sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; + } + return (options4.multiplier || 20) * sum3; +} +var normalizeDistance = (dist, order, min2, max5) => { + if (dist === 0) + return 1; + const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); + const norm = (1 - root / 100 - min2) / (max5 - min2); + const clamp2 = Math.max(Math.min(norm, 1), 0); + return clamp2; +}; +function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { + const dist = distance(descriptor1, descriptor2, options4); + return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); +} +function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { + if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { + return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; + } + let lowestDistance = Number.MAX_SAFE_INTEGER; + let index2 = -1; + for (let i = 0; i < descriptors.length; i++) { + const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; + if (res < lowestDistance) { + lowestDistance = res; + index2 = i; + } + if (lowestDistance < (options4.threshold || 0)) + break; + } + const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); + return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; +} + +// src/models.ts +var models_exports2 = {}; +__export(models_exports2, { + Models: () => Models, + validateModel: () => validateModel +}); + +// src/body/movenet.ts +var tf32 = __toESM(require_tfjs_esm()); + +// src/body/movenetfix.ts +var tf31 = __toESM(require_tfjs_esm()); +var maxJitter = 5e-3; +var cache5 = { + keypoints: [], + padding: [[0, 0], [0, 0], [0, 0], [0, 0]] +}; +function bodyParts(body4) { + for (const pair of horizontal) { + const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); + if (body4.keypoints[left] && body4.keypoints[right]) { + if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } + } + for (const pair of vertical) { + const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + if (body4.keypoints[lower] && body4.keypoints[higher]) { + if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { + body4.keypoints.splice(lower, 1); + } + } + } + for (const [pair, compare2] of relative) { + const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); + const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); + if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) + continue; + const distanceLeft = body4.keypoints[left] ? [ + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) + ] : [0, 0]; + const distanceRight = body4.keypoints[right] ? [ + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) + ] : [0, 0]; + if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } +} +function jitter(keypoints) { + for (let i = 0; i < keypoints.length; i++) { + if (keypoints[i] && cache5.keypoints[i]) { + const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; + if (diff[0] < maxJitter && diff[1] < maxJitter) { + keypoints[i] = cache5.keypoints[i]; + } else { + cache5.keypoints[i] = keypoints[i]; + } + } else { + cache5.keypoints[i] = keypoints[i]; + } + } + return keypoints; +} +function padInput(input, inputSize10) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + cache5.padding = [ + [0, 0], + [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], + [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], + [0, 0] + ]; + t2.pad = tf31.pad(input, cache5.padding); + t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); + const final = tf31.cast(t2.resize, "int32"); + Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); + return final; +} +function rescaleBody(body4, outputSize2) { + body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); + for (const kpt4 of body4.keypoints) { + kpt4.position = [ + kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], + kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] + ]; + kpt4.positionRaw = [ + kpt4.position[0] / outputSize2[0], + kpt4.position[1] / outputSize2[1] + ]; + } + const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); + body4.box = rescaledBoxes.box; + body4.boxRaw = rescaledBoxes.boxRaw; + return body4; +} + +// src/body/movenet.ts +var model18; +var inputSize8 = 0; +var skipped14 = Number.MAX_SAFE_INTEGER; +var cache6 = { + boxes: [], + bodies: [], + last: 0 +}; +async function load17(config3) { + var _a; + if (env.initial) + model18 = null; + if (!model18) { + fakeOps(["size"], config3); + model18 = await loadModel(config3.body.modelPath); + } else if (config3.debug) + log("cached model:", model18["modelUrl"]); + inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; + if (inputSize8 < 64) + inputSize8 = 256; + return model18; +} +function parseSinglePose(res, config3, image28) { + const kpt4 = res[0][0]; + const keypoints = []; + let score = 0; + for (let id = 0; id < kpt4.length; id++) { + score = kpt4[id][2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[id][1], kpt4[id][0]]; + keypoints.push({ + score: Math.round(100 * score) / 100, + part: kpt3[id], + positionRaw, + position: [ + Math.round((image28.shape[2] || 0) * positionRaw[0]), + Math.round((image28.shape[1] || 0) * positionRaw[1]) + ] + }); + } + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const bodies = []; + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + return bodies; +} +function parseMultiPose(res, config3, image28) { + const bodies = []; + for (let id = 0; id < res[0].length; id++) { + const kpt4 = res[0][id]; + const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; + if (totalScore > config3.body.minConfidence) { + const keypoints = []; + for (let i = 0; i < 17; i++) { + const score = kpt4[3 * i + 2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; + keypoints.push({ + part: kpt3[i], + score: Math.round(100 * score) / 100, + positionRaw, + position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] + }); + } + } + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + } + } + bodies.sort((a, b) => b.score - a.score); + if (bodies.length > config3.body.maxDetected) + bodies.length = config3.body.maxDetected; + return bodies; +} +async function predict17(input, config3) { + var _a; + if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) + return []; + if (!config3.skipAllowed) + cache6.boxes.length = 0; + skipped14++; + const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; + const skipFrame = skipped14 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache6.bodies; + } + return new Promise(async (resolve) => { + const t2 = {}; + skipped14 = 0; + t2.input = padInput(input, inputSize8); + t2.res = model18 == null ? void 0 : model18.execute(t2.input); + cache6.last = now(); + const res = await t2.res.array(); + cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + for (const body4 of cache6.bodies) { + rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); + jitter(body4.keypoints); + } + Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); + resolve(cache6.bodies); + }); +} + +// src/object/nanodet.ts +var tf33 = __toESM(require_tfjs_esm()); +var model19; +var last10 = []; +var lastTime15 = 0; +var skipped15 = Number.MAX_SAFE_INTEGER; +var inputSize9 = 0; +var scaleBox = 2.5; +async function load18(config3) { + if (!model19 || env.initial) { + model19 = await loadModel(config3.object.modelPath); + const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; + inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; + } else if (config3.debug) + log("cached model:", model19["modelUrl"]); + return model19; +} +async function process4(res, outputShape, config3) { + var _a, _b; + let id = 0; + let results = []; + const size2 = inputSize9; + for (const strideSize of [1, 2, 4]) { + const baseSize = strideSize * 13; + const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scores = await scoresT.array(); + const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); + const boxIdxT = tf33.argMax(boxesMaxT, 2); + const boxIdx = await boxIdxT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { + const score = scores[i][j]; + if (score > (config3.object.minConfidence || 0) && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw = [x, y, w, h]; + boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); + const box = [ + boxRaw[0] * outputShape[0], + boxRaw[1] * outputShape[1], + boxRaw[2] * outputShape[0], + boxRaw[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score) / 100, + class: j + 1, + label: labels2[j].label, + box: box.map((a) => Math.trunc(a)), + boxRaw + }; + results.push(result); + } + } + } + tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); + } + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = Array.from(await nms.data()); + tf33.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict18(image28, config3) { + if (!(model19 == null ? void 0 : model19["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; + const skipFrame = skipped15 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { + skipped15++; + return last10; + } + skipped15 = 0; + if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) + return last10; + return new Promise(async (resolve) => { + const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; + const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); + const normT = tf33.div(resizeT, constants.tf255); + const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); + let objectT; + if (config3.object.enabled) + objectT = model19.execute(transposeT); + lastTime15 = now(); + const obj = await process4(objectT, outputSize2, config3); + last10 = obj; + tf33.dispose([resizeT, normT, transposeT, ...objectT]); + resolve(obj); + }); +} + +// src/body/posenet.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/body/posenetutils.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; +function getBoundingBox(keypoints) { + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score, part, position }) => ({ + score, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })), + annotations: {} + }); + const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + __publicField(this, "priorityQueue"); + __publicField(this, "numberOfElements"); + __publicField(this, "getElementValue"); + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max5 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max5; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t2 = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t2; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min2, max5) { + if (a < min2) + return min2; + if (a > max5) + return max5; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/body/posenet.ts +var model20; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors( + { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, + { x: offsetPoint.x, y: offsetPoint.y } + ); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints; +} +function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence2, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < minConfidence2) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) + queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses, { x, y }, keypointId) { + return poses.some(({ keypoints }) => { + var _a; + const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints) { + const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { + const poses = []; + const queue = buildPartWithScoreQueue(minConfidence2, scores); + while (poses.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses, rootImageCoords, root.part.id)) + continue; + let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints = keypoints.filter((a) => a.score > minConfidence2); + const score = getInstanceScore(poses, keypoints); + const box = getBoundingBox(keypoints); + if (score > minConfidence2) + poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); + } + return poses; +} +async function predict19(input, config3) { + if (!(model20 == null ? void 0 : model20["executor"])) + return []; + const res = tf34.tidy(() => { + if (!model20.inputs[0].shape) + return []; + const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); + const results = model20.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tf34.squeeze(y, [0])); + results3d[1] = tf34.sigmoid(results3d[1]); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); + for (const t2 of res) + tf34.dispose(t2); + const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model20.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + return scaled; +} +async function load19(config3) { + if (!model20 || env.initial) + model20 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model20["modelUrl"]); + return model20; +} + +// src/segmentation/rvm.ts +var tf35 = __toESM(require_tfjs_esm()); +var model21; +var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; +var t = {}; +var ratio = 0; +function init3(config3) { + tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + t.r1i = tf35.tensor(0); + t.r2i = tf35.tensor(0); + t.r3i = tf35.tensor(0); + t.r4i = tf35.tensor(0); + ratio = config3.segmentation.ratio || 0.5; + t.downsample_ratio = tf35.tensor(ratio); +} +async function load20(config3) { + if (!model21 || env.initial) + model21 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model21["modelUrl"]); + init3(config3); + return model21; +} +var normalize = (r) => tf35.tidy(() => { + const squeeze14 = tf35.squeeze(r, [0]); + const mul15 = tf35.mul(squeeze14, constants.tf255); + const cast8 = tf35.cast(mul15, "int32"); + return cast8; +}); +function getRGBA(fgr, pha) { + const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = tf35.concat([rgb2, a], -1); + tf35.dispose([rgb2, a]); + return rgba; +} +function getState(state) { + return tf35.tidy(() => { + const r = {}; + r.unstack = tf35.unstack(state, -1); + r.concat = tf35.concat(r.unstack, 1); + r.split = tf35.split(r.concat, 4, 1); + r.stack = tf35.concat(r.split, 2); + r.squeeze = tf35.squeeze(r.stack, [0]); + r.expand = tf35.expandDims(r.squeeze, -1); + r.add = tf35.add(r.expand, 1); + r.mul = tf35.mul(r.add, 127.5); + r.cast = tf35.cast(r.mul, "int32"); + r.tile = tf35.tile(r.cast, [1, 1, 3]); + r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return tf35.concat([r.tile, r.alpha], -1); + }); +} +async function predict20(input, config3) { + if (!model21) + model21 = await load20(config3); + if (!(model21 == null ? void 0 : model21["executor"])) + return null; + t.src = tf35.div(input, 255); + if (ratio !== config3.segmentation.ratio) + init3(config3); + const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + rgba = getRGBA(fgr, pha); + break; + case "alpha": + rgba = getRGBA(null, pha); + break; + case "foreground": + rgba = getRGBA(fgr, null); + break; + case "state": + rgba = getState(r1o); + break; + default: + rgba = tf35.tensor(0); + } + tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; + return rgba; +} + +// src/segmentation/selfie.ts +var tf36 = __toESM(require_tfjs_esm()); +var model22; +async function load21(config3) { + if (!model22 || env.initial) + model22 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model22["modelUrl"]); + return model22; +} +async function predict21(input, config3) { + var _a; + if (!model22) + model22 = await load21(config3); + if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t2.norm = tf36.div(t2.resize, constants.tf255); + t2.res = model22.execute(t2.norm); + t2.squeeze = tf36.squeeze(t2.res, [0]); + t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t2.mul = tf36.mul(t2.alpha, constants.tf255); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf36.squeeze(input); + t2.concat = tf36.concat([t2.input, t2.mul], -1); + rgba = tf36.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf36.cast(t2.mul, "int32"); + break; + default: + rgba = tf36.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); + return rgba; +} + +// src/models.ts +function validateModel(instance, model23, name) { + var _a, _b; + if (!model23) + return null; + if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) + return null; + const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; + const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; + const ops = []; + const missing = []; + const url = model23["modelUrl"]; + const executor = model23["executor"]; + if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { + for (const kernel of Object.values(executor.graph.nodes)) { + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); + } + } else { + if (!executor && instance.config.debug) { + log("model not loaded", name); + } + } + for (const op of ops) { + if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); + } + } + if (instance.config.debug && missing.length > 0) + log("model validation failed:", name, missing); + return missing.length > 0 ? { name, missing, ops, url } : null; +} +var Models = class { + constructor(currentInstance) { + __publicField(this, "instance"); + __publicField(this, "models", {}); + this.models = {}; + this.instance = currentInstance; + } + stats() { + let totalSizeFromManifest = 0; + let totalSizeWeights = 0; + let totalSizeLoading = 0; + for (const m of Object.values(modelStats)) { + totalSizeFromManifest += m.sizeFromManifest; + totalSizeWeights += m.sizeLoadedWeights; + totalSizeLoading += m.sizeDesired; + } + const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; + return { + numLoadedModels: Object.values(modelStats).length, + numDefinedModels: Object.keys(this.models).length, + percentageLoaded, + totalSizeFromManifest, + totalSizeWeights, + totalSizeLoading, + modelStats: Object.values(modelStats) + }; + } + reset() { + for (const model23 of Object.keys(this.models)) + this.models[model23] = null; + } + async load(instance) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; + if (env.initial) + this.reset(); + if (instance) + this.instance = instance; + const m = {}; + m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; + m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; + m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; + m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; + m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; + m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; + m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; + m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; + m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; + m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; + m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; + m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; + m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; + m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; + m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; + m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; + m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; + if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) + [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; + m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; + m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; + m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; + m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; + for (const [model23, promise] of Object.entries(m)) { + if (promise == null ? void 0 : promise["then"]) + promise["then"]((val) => this.models[model23] = val); + } + await Promise.all(Object.values(m)); + } + list() { + const models3 = Object.keys(this.models).map((model23) => { + var _a; + return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; + }); + for (const m of models3) { + const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); + if (!stats) + continue; + m.size = modelStats[stats].sizeLoadedWeights; + m.url = modelStats[stats].url; + } + return models3; + } + loaded() { + const list = this.list(); + const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); + return loaded; + } + validate() { + const missing = []; + for (const defined of Object.keys(this.models)) { + const model23 = this.models[defined]; + if (!model23) + continue; + const res = validateModel(this.instance, model23, defined); + if (res) + missing.push(res); + } + return missing; + } +}; + +// src/util/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f; + let id = 0; + const persons = []; + for (const face4 of faces) { + const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture2 of gestures) { + if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) + person2.gestures.push(gesture2); + } + const x = []; + const y = []; + const extractXY = (box) => { + if (box && box.length === 4) { + x.push(box[0], box[0] + box[2]); + y.push(box[1], box[1] + box[3]); + } + }; + extractXY(person2.face.box); + extractXY((_d = person2.body) == null ? void 0 : _d.box); + extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); + extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons.push(person2); + } + return persons; +} + +// src/warmup.ts +var tf37 = __toESM(require_tfjs_esm()); + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -269,7 +14136,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Vt=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -837,4 +14705,563 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Gt);break;case"body":case"full":n=await t(Vt);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Aa(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Gt;break;case"full":case"body":n="data:image/jpeg;base64,"+Vt;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=te(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function aa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Gt):n=t(Vt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ia(e){let t;return typeof createImageBitmap=="function"?t=await sa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await Aa(e):t=await aa(e),t}async function la(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let d=(y==null?void 0:y.modelSignature)&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])==null?void 0:l.shape)?[...y.inputs[0].shape]:[1,64,64,3],p=(y==null?void 0:y.modelSignature)&&((x=(c=y==null?void 0:y.inputs)==null?void 0:c[0])==null?void 0:x.dtype)?y.inputs[0].dtype:"float32";for(let 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this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return no(t,this.config)}async warmup(t){let n=g(),o=await No(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return 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h5(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await c1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((t0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?_5(a.tensor,d):[]:($=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?t1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(v=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&v.includes("handdetect")?x=this.config.hand.enabled?await _5(a.tensor,d):[]:(n0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&n0.includes("handtrack")&&(x=this.config.hand.enabled?await t1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?i=this.config.object.enabled?x1(a.tensor,this.config):[]:(g0=this.config.object.modelPath)!=null&&g0.includes("centernet")&&(i=this.config.object.enabled?f5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(m0=this.config.object.modelPath)!=null&&m0.includes("nanodet")?i=this.config.object.enabled?await x1(a.tensor,this.config):[]:(B=this.config.object.modelPath)!=null&&B.includes("centernet")&&(i=this.config.object.enabled?await f5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...jn(l),...Sn(c),...In(x),...Nn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let f=((X=this.process.tensor)==null?void 0:X.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return jo(l,c,x,p,f)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,Ue)[t.id]||(this.config.debug&&u("video start",t.id),G0(this,Ue)[t.id]=!0),!t.paused&&G0(this,Ue)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,Ue)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),G0(this,Ue)[t.id]=!1)}};M2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Zt=new WeakMap,Ue=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); +2Q==`; + +// src/warmup.ts +async function warmupBitmap(instance) { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (instance.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "body": + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await instance.detect(bitmap, instance.config); + bitmap.close(); + } + return res; +} +async function warmupCanvas(instance) { + return new Promise((resolve) => { + let src; + switch (instance.config.warmup) { + case "face": + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + src = "data:image/jpeg;base64," + body3; + break; + default: + src = ""; + } + let img; + if (typeof Image !== "undefined") + img = new Image(); + else if (env.Image) + img = new env.Image(); + else { + resolve(void 0); + return; + } + img.onload = async () => { + const canvas3 = canvas(img.naturalWidth, img.naturalHeight); + if (!canvas3) { + log("Warmup: Canvas not found"); + resolve(void 0); + } else { + const ctx = canvas3.getContext("2d"); + if (ctx) + ctx.drawImage(img, 0, 0); + const tensor6 = await instance.image(canvas3, true); + const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; + resolve(res); + } + }; + if (src) + img.src = src; + else + resolve(void 0); + }); +} +async function warmupNode(instance) { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (instance.config.warmup === "face") + img = atob(face3); + else + img = atob(body3); + let res; + if ("node" in tf37 && tf37.getBackend() === "tensorflow") { + const data = tf37["node"].decodeJpeg(img); + const expanded = tf37.expandDims(data, 0); + instance.tf.dispose(data); + res = await instance.detect(expanded, instance.config); + instance.tf.dispose(expanded); + } else { + if (instance.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; +} +async function runInference(instance) { + let res; + if (typeof createImageBitmap === "function") + res = await warmupBitmap(instance); + else if (typeof Image !== "undefined" || env.Canvas !== void 0) + res = await warmupCanvas(instance); + else + res = await warmupNode(instance); + return res; +} +async function runCompile(instance) { + var _a, _b, _c, _d; + if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) + return; + const backendType = tf37.getBackend(); + const webGLBackend = tf37.backend(); + if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { + return; + } + tf37.env().set("ENGINE_COMPILE_ONLY", true); + const numTensorsStart = tf37.engine().state.numTensors; + const compiledModels = []; + for (const [modelName, model23] of Object.entries(instance.models.models)) { + if (!model23) + continue; + const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; + const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; + for (let dim = 0; dim < shape.length; dim++) { + if (shape[dim] === -1) + shape[dim] = dim === 0 ? 1 : 64; + } + const tensor6 = tf37.zeros(shape, dtype); + try { + const res = model23.execute(tensor6); + compiledModels.push(modelName); + if (Array.isArray(res)) + res.forEach((t2) => tf37.dispose(t2)); + else + tf37.dispose(res); + } catch (e) { + if (instance.config.debug) + log("compile fail model:", modelName); + } + tf37.dispose(tensor6); + } + const kernels = await webGLBackend["checkCompileCompletionAsync"](); + webGLBackend["getUniformLocations"](); + if (instance.config.debug) + log("compile pass:", { models: compiledModels, kernels: kernels.length }); + tf37.env().set("ENGINE_COMPILE_ONLY", false); + const numTensorsEnd = tf37.engine().state.numTensors; + if (numTensorsEnd - numTensorsStart > 0) + log("tensor leak:", numTensorsEnd - numTensorsStart); +} +async function warmup(instance, userConfig) { + await check(instance, false); + const t0 = now(); + instance.state = "warmup"; + if (userConfig) + instance.config = mergeDeep(instance.config, userConfig); + if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { + return empty(); + } + return new Promise(async (resolve) => { + await instance.models.load(); + await runCompile(instance); + const res = await runInference(instance); + const t1 = now(); + if (instance.config.debug) + log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); + instance.emit("warmup"); + resolve(res); + }); +} + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; +var Human = class { + constructor(userConfig) { + __publicField(this, "version"); + __publicField(this, "config"); + __publicField(this, "result"); + __publicField(this, "state"); + __publicField(this, "process"); + __publicField(this, "tf"); + __publicField(this, "env", env); + __publicField(this, "draw", draw_exports); + __publicField(this, "match", match_exports); + __publicField(this, "models"); + __publicField(this, "events"); + __publicField(this, "faceTriangulation"); + __publicField(this, "faceUVMap"); + __publicField(this, "performance"); + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __publicField(this, "analyze", (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }); + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.env.node && !(input instanceof tf38.Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __publicField(this, "webcam", new WebCam()); + __publicField(this, "emit", (event) => { + var _a; + if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) + this.events.dispatchEvent(new Event(event)); + }); + __privateAdd(this, _loops, {}); + const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); + config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; + config.modelBasePath = env.browser ? "../models/" : "file://models/"; + this.version = version2; + Object.defineProperty(this, "version", { value: version2 }); + this.config = JSON.parse(JSON.stringify(config)); + Object.seal(this.config); + this.config.cacheModels = typeof indexedDB !== "undefined"; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + setModelLoadOptions(this.config); + this.tf = tf38; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + this.performance = {}; + this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; + this.models = new Models(this); + init2(); + this.result = empty(); + this.process = { tensor: null, canvas: null }; + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + validateModel(this, null, ""); + this.emit("create"); + if (this.config.debug || this.env.browser) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version["tfjs-core"]}`); + const envTemp = JSON.parse(JSON.stringify(this.env)); + delete envTemp.kernels; + delete envTemp.initial; + delete envTemp.perfadd; + if (this.config.debug) + log("environment:", envTemp); + } + reset() { + const currentBackend = this.config.backend; + this.config = JSON.parse(JSON.stringify(config)); + this.config.backend = currentBackend; + reset(); + env.initial = true; + } + validate(userConfig) { + const msgs = validate(config, userConfig || this.config); + if (msgs.length === 0) + this.config = mergeDeep(this.config, userConfig); + return msgs; + } + now() { + return now(); + } + image(input, getTensor = false) { + return process2(input, this.config, getTensor); + } + async segmentation(input, userConfig) { + var _a, _b, _c; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (!this.config.segmentation.enabled) + return null; + const processed = await process2(input, this.config); + if (!processed.tensor) + return null; + let tensor6 = null; + if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) + tensor6 = await predict20(processed.tensor, this.config); + if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) + tensor6 = await predict16(processed.tensor, this.config); + if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) + tensor6 = await predict21(processed.tensor, this.config); + tf38.dispose(processed.tensor); + return tensor6; + } + compare(firstImageTensor, secondImageTensor) { + return compare(this.config, firstImageTensor, secondImageTensor); + } + async init() { + await check(this, true); + await this.tf.ready(); + reset(); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + const count2 = Object.values(this.models.models).filter((model23) => model23).length; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (this.env.initial) { + if (!await check(this, false)) + log("error: backend check failed"); + await tf38.ready(); + if (this.env.browser) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("tf flags:", this.tf.ENV.flags); + } + } + await this.models.load(this); + if (this.env.initial && this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + this.env.initial = false; + const loaded = Object.values(this.models.models).filter((model23) => model23).length; + if (loaded !== count2) { + this.models.validate(); + this.emit("load"); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.loadModels || 0)) + this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; + } + next(result = this.result) { + return calc2(result, this.config); + } + async warmup(userConfig) { + const t0 = now(); + const res = await warmup(this, userConfig); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t0); + return res; + } + async profile(input, userConfig) { + const profile = await this.tf.profile(() => this.detect(input, userConfig)); + const kernels = {}; + let total = 0; + for (const kernel of profile.kernels) { + const ms = Number(kernel.kernelTimeMs) || 0; + if (kernels[kernel.name]) + kernels[kernel.name] += ms; + else + kernels[kernel.name] = ms; + total += ms; + } + const kernelArr = []; + Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); + for (const kernel of kernelArr) { + kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; + kernel.time = Math.round(1e3 * kernel.time) / 1e3; + } + kernelArr.sort((a, b) => b.time - a.time); + kernelArr.length = 20; + return kernelArr; + } + async detect(input, userConfig) { + this.state = "detect"; + return new Promise(async (resolve) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; + this.state = "config"; + let timeStamp; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + this.emit("error"); + resolve(empty(error)); + } + const timeStart = now(); + await this.load(); + timeStamp = now(); + this.state = "image"; + const img = await process2(input, this.config); + this.process = img; + this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (!img.tensor) { + if (this.config.debug) + log("could not convert input to tensor"); + this.emit("error"); + resolve(empty("could not convert input to tensor")); + return; + } + this.emit("image"); + timeStamp = now(); + this.config.skipAllowed = await skip(this.config, img.tensor); + this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; + if (!this.performance.totalFrames) + this.performance.totalFrames = 0; + if (!this.performance.cachedFrames) + this.performance.cachedFrames = 0; + this.performance.totalFrames++; + if (this.config.skipAllowed) + this.performance.cachedFrames++; + this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes = []; + let bodyRes = []; + let handRes = []; + let objectRes = []; + this.state = "detect:face"; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; + this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) + faceRes = await faceRes; + this.analyze("Start Body:"); + this.state = "detect:body"; + const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) + bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; + else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; + else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; + else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) + bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; + if (this.performance.body) + delete this.performance.body; + } else { + timeStamp = now(); + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; + else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; + else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; + else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; + this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + this.state = "detect:hand"; + const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) + handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; + else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) + handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + timeStamp = now(); + if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) + handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; + else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) + handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; + this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + this.state = "detect:object"; + if (this.config.async) { + if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) + objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; + else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) + objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + timeStamp = now(); + if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; + else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) + objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; + this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Object:"); + this.state = "detect:await"; + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + this.state = "detect:gesture"; + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; + if (!this.config.async) + this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); + const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: this.process.canvas, + timestamp: Date.now(), + error: null, + width: shape[2], + height: shape[1], + get persons() { + return join2(faceRes, bodyRes, handRes, gestureRes, shape); + } + }; + tf38.dispose(img.tensor); + this.emit("detect"); + this.state = "idle"; + resolve(this.result); + }); + } + async sleep(ms) { + return new Promise((resolve) => { + setTimeout(resolve, ms); + }); + } + async video(element, run = true, delay = 0) { + if (run) { + if (!__privateGet(this, _loops)[element.id]) { + if (this.config.debug) + log("video start", element.id); + __privateGet(this, _loops)[element.id] = true; + } + if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) + await this.detect(element); + if (delay > 0) + await this.sleep(delay); + if (__privateGet(this, _loops)[element.id]) + requestAnimationFrame(() => this.video(element, run, delay)); + } else { + if (this.config.debug) + log("video stop", element.id); + __privateGet(this, _loops)[element.id] = false; + } + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_sanity = new WeakMap(); +_loops = new WeakMap(); +// Annotate the CommonJS export names for ESM import in node: +0 && (module.exports = { + Env, + Human, + defaults, + draw, + empty, + env, + match, + models +}); diff --git a/dist/human.node.js b/dist/human.node.js index 76c2c68f..41bccddb 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -4,7 +4,316 @@ author: ' */ -"use strict";var No=Object.create;var k2=Object.defineProperty;var Io=Object.getOwnPropertyDescriptor;var Oo=Object.getOwnPropertyNames;var 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H=Do((ya,qt)=>{"use strict";var Zt=Object.defineProperty,Bo=Object.getOwnPropertyDescriptor,Ho=Object.getOwnPropertyNames,Go=Object.prototype.hasOwnProperty,Vo=(e,t)=>{for(var n in t)Zt(e,n,{get:t[n],enumerable:!0})},Vt=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Ho(t))!Go.call(e,r)&&r!==n&&Zt(e,r,{get:()=>t[r],enumerable:!(o=Bo(t,r))||o.enumerable});return e},Zo=(e,t,n)=>(Vt(e,t,"default"),n&&Vt(n,t,"default")),Xo=e=>Vt(Zt({},"__esModule",{value:!0}),e),Xt={};Vo(Xt,{version:()=>Qo});qt.exports=Xo(Xt);Zo(Xt,require("@tensorflow/tfjs-node"),qt.exports);var E1="4.1.0",qo="4.1.0",Uo="4.1.0",Yo="4.1.0",Ko="4.1.0",Jo="0.0.1-alpha.16",Qo={tfjs:E1,"tfjs-core":E1,"tfjs-converter":qo,"tfjs-backend-cpu":Uo,"tfjs-backend-webgl":Yo,"tfjs-backend-wasm":Ko,"tfjs-backend-webgpu":Jo}});var da={};ze(da,{Env:()=>w2,Human:()=>v1,default:()=>v1,defaults:()=>Ye,draw:()=>et,empty:()=>he,env:()=>R,match:()=>Nt,models:()=>T1});module.exports=Fo(da);var ae=Z(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function z1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Ut(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Ut(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ye={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var I0=Z(H());var N=Z(H());var S1=` +"use strict"; +var __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; +var __commonJS = (cb, mod3) => function __require() { + return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; +}; +var __export = (target, all2) => { + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( + isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, + mod3 +)); +var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); +var __publicField = (obj, key, value) => { + __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); + return value; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all2) => { + for (var name in all2) + __defProp2(target, name, { get: all2[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); + var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); + var tf_node_exports = {}; + __export2(tf_node_exports, { + version: () => version7 + }); + module2.exports = __toCommonJS2(tf_node_exports); + __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports); + var version4 = "4.2.0"; + var version22 = "4.2.0"; + var version32 = "4.2.0"; + var version42 = "4.2.0"; + var version5 = "4.2.0"; + var version6 = "0.0.1-alpha.17"; + var version7 = { + tfjs: version4, + "tfjs-core": version4, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version42, + "tfjs-backend-wasm": version5, + "tfjs-backend-webgpu": version6 + }; + } +}); + +// src/human.ts +var human_exports = {}; +__export(human_exports, { + Env: () => Env, + Human: () => Human, + default: () => Human, + defaults: () => config, + draw: () => draw_exports, + empty: () => empty, + env: () => env, + match: () => match_exports, + models: () => models_exports2 +}); +module.exports = __toCommonJS(human_exports); +var tf38 = __toESM(require_tfjs_esm()); + +// src/util/util.ts +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`modelpath error: expecting json file: ${path}`); + return path; +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function validate(defaults, config3, parent = "config", msgs = []) { + for (const key of Object.keys(config3)) { + if (typeof config3[key] === "object") { + validate(defaults[key], config3[key], key, msgs); + } else { + const defined = defaults && typeof defaults[key] !== "undefined"; + if (!defined) + msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); + const same = defaults && typeof defaults[key] === typeof config3[key]; + if (defined && !same) + msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); + } + } + if (config3.debug && parent === "config" && msgs.length > 0) + log("invalid configuration", msgs); + return msgs; +} +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "", + modelBasePath: "", + cacheModels: true, + validateModels: true, + wasmPath: "", + wasmPlatformFetch: false, + debug: false, + async: true, + warmup: "full", + cacheSensitivity: 0.7, + skipAllowed: false, + deallocate: false, + flags: {}, + softwareKernels: false, + filter: { + enabled: true, + equalization: false, + width: 0, + height: 0, + flip: false, + return: true, + autoBrightness: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: false, + maxDetected: 1, + skipFrames: 99, + skipTime: 2500, + minConfidence: 0.2, + iouThreshold: 0.1, + mask: false, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json", + keepInvalid: false + }, + attention: { + enabled: false, + modelPath: "facemesh-attention.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 99, + skipTime: 1500, + modelPath: "emotion.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 99, + skipTime: 3e3, + minConfidence: 0.1 + }, + antispoof: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "antispoof.json" + }, + liveness: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "liveness.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: -1, + minConfidence: 0.3, + skipFrames: 1, + skipTime: 200 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 99, + skipTime: 1e3, + minConfidence: 0.5, + iouThreshold: 0.2, + maxDetected: -1, + landmarks: true, + detector: { + modelPath: "handtrack.json" + }, + skeleton: { + modelPath: "handlandmark-lite.json" + } + }, + object: { + enabled: false, + modelPath: "centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 99, + skipTime: 2e3 + }, + segmentation: { + enabled: false, + modelPath: "rvm.json", + ratio: 0.5, + mode: "default" + } +}; + +// src/util/env.ts +var tf3 = __toESM(require_tfjs_esm()); + +// src/image/image.ts +var tf2 = __toESM(require_tfjs_esm()); + +// src/image/imagefxshaders.ts +var vertexIdentity = ` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -14,7 +323,8 @@ vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var j1=` +`; +var colorMatrixWithAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -26,7 +336,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,N1=` +`; +var colorMatrixWithoutAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -38,7 +349,8 @@ gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,I1=` +`; +var pixelate = ` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -51,7 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m=new Float32Array(T),h=1/l.width,S=1/l.height,P=b(L1);!P||(i.uniform1fv(P.uniform.m,m),i.uniform2f(P.uniform.px,h,S),f())},detectEdges:()=>{M.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{M.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{M.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:T=>{let m=T||1;M.convolution.call(this,[0,-1*m,0,-1*m,1+4*m,-1*m,0,-1*m,0])},emboss:T=>{let m=T||1;M.convolution.call(this,[-2*m,-1*m,0,-1*m,1,1*m,0,1*m,2*m])},blur:T=>{let m=T/7/l.width,h=T/7/l.height,S=b(O1);!S||(i.uniform2f(S.uniform.px,0,h),f(x.INTERMEDIATE),i.uniform2f(S.uniform.px,m,0),f())},pixelate:T=>{let m=T/l.width,h=T/l.height,S=b(I1);!S||(i.uniform2f(S.uniform.size,m,h),f())}};this.add=function(T){let m=Array.prototype.slice.call(arguments,1),h=M[T];s.push({func:h,args:m})},this.reset=function(){s=[]},this.get=function(){return 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Image(){return G0(this,z2)}set Image(t){ge(this,z2,t),globalThis.Image=t}get ImageData(){return G0(this,S2)}set ImageData(t){ge(this,S2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(I0.engine().registryFactory);try{this.tensorflow={version:I0.backend().binding?I0.backend().binding.TF_Version:void 0,gpu:I0.backend().binding?I0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await I0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await I0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=te(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=I0.getKernelsForBackend(I0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};E2=new WeakMap,z2=new WeakMap,S2=new WeakMap;var R=new w2;var 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vs=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Rs=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Ms=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Ps=[[474,475],[475,476],[476,477],[477,474]],ks=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],ws=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Es=[[469,470],[470,471],[471,472],[472,469]],zs=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function Ne(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Ss={lips:Ne(vs),leftEye:Ne(Rs),leftEyebrow:Ne(Ms),leftIris:Ne(Ps),rightEye:Ne(ks),rightEyebrow:Ne(ws),rightIris:Ne(Es),faceOval:Ne(zs)},js=Object.entries(Ss).map(([e,t])=>t.map(n=>[n,e])).flat(),Da=new Map(js),I2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],$e=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],e2=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];var K;function Ns(e,t){var o,r,s,A,a,l,c,x,i;if(!K.drawLabels||((o=K.faceLabels)==null?void 0:o.length)===0)return;let n=K.faceLabels.slice();if(e.score&&(n=_(n,"[score]",100*e.score)),e.gender&&(n=_(n,"[gender]",e.gender)),e.genderScore&&(n=_(n,"[genderScore]",100*e.genderScore)),e.age&&(n=_(n,"[age]",e.age)),e.distance&&(n=_(n,"[distance]",100*e.distance)),e.real&&(n=_(n,"[real]",100*e.real)),e.live&&(n=_(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let y=e.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);y.length>3&&(y.length=3),n=_(n,"[emotions]",y.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(n=_(n,"[roll]",Ke(e.rotation.angle.roll))),(a=(A=e.rotation)==null?void 0:A.angle)!=null&&a.yaw&&(n=_(n,"[yaw]",Ke(e.rotation.angle.yaw))),(c=(l=e.rotation)==null?void 0:l.angle)!=null&&c.pitch&&(n=_(n,"[pitch]",Ke(e.rotation.angle.pitch))),(i=(x=e.rotation)==null?void 0:x.gaze)!=null&&i.bearing&&(n=_(n,"[gaze]",Ke(e.rotation.gaze.bearing))),ne(t,n,e.box[0],e.box[1],K)}function Is(e,t){var n,o,r,s;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}}function Os(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ke(e.rotation.angle.pitch)/90,s=new Path2D(` - M ${e.box[0]+e.box[2]/2} ${e.box[1]} +`; + +// src/image/imagefx.ts +var collect = (source, prefix, collection) => { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match2, name) => { + collection[name] = 0; + return match2; + }); +}; +var GLProgram = class { + constructor(gl, vertexSource, fragmentSource) { + __publicField(this, "uniform", {}); + __publicField(this, "attribute", {}); + __publicField(this, "gl"); + __publicField(this, "id"); + __publicField(this, "compile", (source, type) => { + const shader = this.gl.createShader(type); + if (!shader) { + log("filter: could not create shader"); + return null; + } + this.gl.shaderSource(shader, source); + this.gl.compileShader(shader); + if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { + log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); + return null; + } + return shader; + }); + this.gl = gl; + const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); + const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); + this.id = this.gl.createProgram(); + if (!vertexShader || !fragmentShader) + return; + if (!this.id) { + log("filter: could not create webgl program"); + return; + } + this.gl.attachShader(this.id, vertexShader); + this.gl.attachShader(this.id, fragmentShader); + this.gl.linkProgram(this.id); + if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { + log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); + return; + } + this.gl.useProgram(this.id); + collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = this.gl.getAttribLocation(this.id, a); + collect(vertexSource, "uniform", this.uniform); + collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = this.gl.getUniformLocation(this.id, u); + } +}; +function GLImageFilter() { + let drawCount = 0; + let sourceTexture = null; + let lastInChain = false; + let currentFramebufferIndex = -1; + let tempFramebuffers = [null, null]; + let filterChain = []; + let vertexBuffer = null; + let currentProgram = null; + const fxcanvas = canvas(100, 100); + const shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = fxcanvas.getContext("webgl"); + if (!gl) { + log("filter: cannot get webgl context"); + return; + } + this.gl = gl; + function resize(width, height) { + if (width === fxcanvas.width && height === fxcanvas.height) + return; + fxcanvas.width = width; + fxcanvas.height = height; + if (!vertexBuffer) { + const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); + vertexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); + tempFramebuffers = [null, null]; + } + function createFramebufferTexture(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + } + function getTempFramebuffer(index2) { + tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); + return tempFramebuffers[index2]; + } + function draw(flags = 0) { + if (!currentProgram) + return; + let source = null; + let target = null; + let flipY = false; + if (drawCount === 0) + source = sourceTexture; + else + source = getTempFramebuffer(currentFramebufferIndex).texture || null; + drawCount++; + if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = drawCount % 2 === 0; + } else { + currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; + target = getTempFramebuffer(currentFramebufferIndex).fbo || null; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + } + function compileShader(fragmentSource) { + if (shaderProgramCache[fragmentSource]) { + currentProgram = shaderProgramCache[fragmentSource]; + gl.useProgram((currentProgram ? currentProgram.id : null) || null); + return currentProgram; + } + currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); + if (!currentProgram) { + log("filter: could not get webgl program"); + return null; + } + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(currentProgram.attribute["pos"]); + gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(currentProgram.attribute["uv"]); + gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); + shaderProgramCache[fragmentSource] = currentProgram; + return currentProgram; + } + const filter = { + colorMatrix: (matrix) => { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; + const program = compileShader(shader); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + draw(); + }, + brightness: (brightness) => { + const b = (brightness || 0) + 1; + filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + saturation: (amount) => { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturate: () => { + filter.saturation(-1); + }, + contrast: (amount) => { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }, + negative: () => { + filter.contrast(-2); + }, + hue: (rotation) => { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturateLuminance: () => { + filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }, + sepia: () => { + filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + brownie: () => { + filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }, + vintagePinhole: () => { + filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }, + kodachrome: () => { + filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }, + technicolor: () => { + filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }, + polaroid: () => { + filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + shiftToBGR: () => { + filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + convolution: (matrix) => { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / fxcanvas.width; + const pixelSizeY = 1 / fxcanvas.height; + const program = compileShader(convolution); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); + draw(); + }, + detectEdges: () => { + filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }, + sobelX: () => { + filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }, + sobelY: () => { + filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }, + sharpen: (amount) => { + const a = amount || 1; + filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }, + emboss: (size2) => { + const s = size2 || 1; + filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }, + blur: (size2) => { + const blurSizeX = size2 / 7 / fxcanvas.width; + const blurSizeY = size2 / 7 / fxcanvas.height; + const program = compileShader(blur); + if (!program) + return; + gl.uniform2f(program.uniform["px"], 0, blurSizeY); + draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform["px"], blurSizeX, 0); + draw(); + }, + pixelate: (size2) => { + const blurSizeX = size2 / fxcanvas.width; + const blurSizeY = size2 / fxcanvas.height; + const program = compileShader(pixelate); + if (!program) + return; + gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); + draw(); + } + }; + this.add = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const func = filter[name]; + filterChain.push({ func, args }); + }; + this.reset = function() { + filterChain = []; + }; + this.get = function() { + return filterChain; + }; + this.apply = function(image28) { + resize(image28.width, image28.height); + drawCount = 0; + if (!sourceTexture) + sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); + for (let i = 0; i < filterChain.length; i++) { + lastInChain = i === filterChain.length - 1; + const f = filterChain[i]; + f.func.apply(this, f.args || []); + } + return fxcanvas; + }; + this.draw = function(image28) { + this.add("brightness", 0); + return this.apply(image28); + }; +} + +// src/image/enhance.ts +var tf = __toESM(require_tfjs_esm()); +async function histogramEqualization(inputImage) { + const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; + const rgb2 = tf.split(squeeze14, 3, 2); + const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; + const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; + const absMax = await Promise.all(max5.map((channel) => channel.data())); + const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); + const maxRange = maxValue > 1 ? 255 : 1; + const factor = maxRange / maxValue; + let final; + if (factor > 1) { + const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; + const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; + const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; + const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); + final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); + tf.dispose([...sub11, ...range, ...enh]); + } else { + final = tf.expandDims(squeeze14, 0); + } + tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); + return final; +} + +// src/image/image.ts +var maxSize = 3840; +var inCanvas = null; +var outCanvas = null; +var tmpCanvas = null; +var fx; +var last = { + inputSum: 0, + cacheDiff: 1, + sumMethod: 0, + inputTensor: void 0 +}; +function reset() { + last.inputSum = 0; + last.cacheDiff = 1; + last.sumMethod = 0; + last.inputTensor = void 0; +} +function canvas(width, height) { + let c; + if (env.browser) { + if (env.worker) { + if (typeof OffscreenCanvas === "undefined") + throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); + c = new OffscreenCanvas(width, height); + } else { + if (typeof document === "undefined") + throw new Error("canvas error: attempted to run in browser but DOM is not defined"); + c = document.createElement("canvas"); + c.width = width; + c.height = height; + } + } else { + if (typeof env.Canvas !== "undefined") + c = new env.Canvas(width, height); + else if (typeof globalThis.Canvas !== "undefined") + c = new globalThis.Canvas(width, height); + } + return c; +} +function copy(input, output) { + const outputCanvas = output || canvas(input.width, input.height); + const ctx = outputCanvas.getContext("2d"); + ctx.drawImage(input, 0, 0); + return outputCanvas; +} +async function process2(input, config3, getTensor = true) { + var _a, _b, _c; + if (!input) { + if (config3.debug) + log("input error: input is missing"); + return { tensor: null, canvas: null }; + } + if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("input error: type not recognized"); + } + if (input instanceof tf2.Tensor) { + let tensor7 = null; + if (input["isDisposedInternal"]) + throw new Error("input error: attempted to use tensor but it is disposed"); + if (!input.shape) + throw new Error("input error: attempted to use tensor without a shape"); + if (input.shape.length === 3) { + if (input.shape[2] === 3) { + tensor7 = tf2.expandDims(input, 0); + } else if (input.shape[2] === 4) { + const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); + tensor7 = tf2.expandDims(rgb2, 0); + tf2.dispose(rgb2); + } + } else if (input.shape.length === 4) { + if (input.shape[3] === 3) { + tensor7 = tf2.clone(input); + } else if (input.shape[3] === 4) { + tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + } + } + if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) + throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); + if (tensor7.dtype === "int32") { + const cast8 = tf2.cast(tensor7, "float32"); + tf2.dispose(tensor7); + tensor7 = cast8; + } + return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; + } + if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { + if (config3.debug) + log("input stream is not ready"); + return { tensor: null, canvas: inCanvas }; + } + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) { + if (config3.debug) + log("cannot determine input dimensions"); + return { tensor: null, canvas: inCanvas }; + } + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); + } + if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) + targetWidth = config3.filter.width; + else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) + targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); + if ((config3.filter.height || 0) > 0) + targetHeight = config3.filter.height; + else if ((config3.filter.width || 0) > 0) + targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("input error: cannot determine dimension"); + if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) + inCanvas = canvas(targetWidth, targetHeight); + const inCtx = inCanvas.getContext("2d"); + if (typeof ImageData !== "undefined" && input instanceof ImageData) { + inCtx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof inCtx.translate !== "undefined") { + inCtx.translate(originalWidth, 0); + inCtx.scale(-1, 1); + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + inCtx.setTransform(1, 0, 0, 1, 0, 0); + } else { + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); + } + } + if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) + outCanvas = canvas(inCanvas.width, inCanvas.height); + if (config3.filter.enabled && env.webgl.supported) { + if (!fx) + fx = env.browser ? new GLImageFilter() : null; + env.filter = !!fx; + if (!(fx == null ? void 0 : fx.add)) { + if (config3.debug) + log("input process error: cannot initialize filters"); + env.webgl.supported = false; + config3.filter.enabled = false; + copy(inCanvas, outCanvas); + } else { + fx.reset(); + if (config3.filter.brightness !== 0) + fx.add("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.add("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.add("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.add("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.add("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.add("hue", config3.filter.hue); + if (config3.filter.negative) + fx.add("negative"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.vintage) + fx.add("brownie"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.kodachrome) + fx.add("kodachrome"); + if (config3.filter.technicolor) + fx.add("technicolor"); + if (config3.filter.polaroid) + fx.add("polaroid"); + if (config3.filter.pixelate !== 0) + fx.add("pixelate", config3.filter.pixelate); + if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) + outCanvas = fx.apply(inCanvas); + else + outCanvas = fx.draw(inCanvas); + } + } else { + copy(inCanvas, outCanvas); + if (fx) + fx = null; + env.filter = !!fx; + } + if (!getTensor) + return { tensor: null, canvas: outCanvas }; + if (!outCanvas) + throw new Error("canvas error: cannot create output"); + let pixels; + let depth = 3; + if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { + if (env.browser && tf2.browser) { + pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; + } else { + depth = input.data.length / input.height / input.width; + const arr = new Uint8Array(input.data.buffer); + pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); + } + } else { + if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) + tmpCanvas = canvas(outCanvas.width, outCanvas.height); + if (tf2.browser && env.browser) { + if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { + pixels = tf2.browser.fromPixels(outCanvas); + } else { + tmpCanvas = copy(outCanvas); + pixels = tf2.browser.fromPixels(tmpCanvas); + } + } else { + const tempCanvas = copy(outCanvas); + const tempCtx = tempCanvas.getContext("2d"); + const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); + depth = tempData.data.length / targetWidth / targetHeight; + const arr = new Uint8Array(tempData.data.buffer); + pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); + } + } + if (depth === 4) { + const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); + tf2.dispose(pixels); + pixels = rgb2; + } + if (!pixels) + throw new Error("input error: cannot create tensor"); + const casted = tf2.cast(pixels, "float32"); + const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); + tf2.dispose([pixels, casted]); + if (config3.filter.autoBrightness) { + const max5 = tf2.max(tensor6); + const maxVal = await max5.data(); + config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; + tf2.dispose(max5); + } + return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; +} +async function skip(config3, input) { + let skipFrame = false; + if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) + return skipFrame; + if (!last.inputTensor) { + last.inputTensor = tf2.clone(input); + } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { + tf2.dispose(last.inputTensor); + last.inputTensor = tf2.clone(input); + } else { + const t2 = {}; + t2.diff = tf2.sub(input, last.inputTensor); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; + tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); + last.inputTensor = tf2.clone(input); + skipFrame = diffRelative <= (config3.cacheSensitivity || 0); + } + return skipFrame; +} +async function compare(config3, input1, input2) { + const t2 = {}; + if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { + if (!config3.debug) + log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); + return 0; + } + if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { + if (!config3.debug) + log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); + return 0; + } + t2.input1 = tf2.clone(input1); + t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); + t2.diff = tf2.sub(t2.input1, t2.input2); + t2.squared = tf2.mul(t2.diff, t2.diff); + t2.sum = tf2.sum(t2.squared); + const diffSum = await t2.sum.data(); + const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; + tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); + return diffRelative; +} + +// src/util/env.ts +var _canvas, _image, _imageData; +var Env = class { + constructor() { + __publicField(this, "browser"); + __publicField(this, "node"); + __publicField(this, "worker"); + __publicField(this, "platform", ""); + __publicField(this, "agent", ""); + __publicField(this, "backends", []); + __publicField(this, "initial"); + __publicField(this, "filter"); + __publicField(this, "tfjs"); + __publicField(this, "offscreen"); + __publicField(this, "perfadd", false); + __publicField(this, "tensorflow", { + version: void 0, + gpu: void 0 + }); + __publicField(this, "wasm", { + supported: void 0, + backend: void 0, + simd: void 0, + multithread: void 0 + }); + __publicField(this, "webgl", { + supported: void 0, + backend: void 0, + version: void 0, + renderer: void 0, + shader: void 0, + vendor: void 0 + }); + __publicField(this, "webgpu", { + supported: void 0, + backend: void 0, + adapter: void 0 + }); + __publicField(this, "cpu", { + model: void 0, + flags: [] + }); + __publicField(this, "kernels", []); + __privateAdd(this, _canvas, void 0); + __privateAdd(this, _image, void 0); + __privateAdd(this, _imageData, void 0); + this.browser = typeof navigator !== "undefined"; + this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; + this.tfjs = { version: tf3.version["tfjs-core"] }; + this.offscreen = typeof OffscreenCanvas !== "undefined"; + this.initial = true; + this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw == null ? void 0 : raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; + this.agent = navigator.userAgent.replace(raw[0], ""); + if (this.platform[1]) + this.agent = this.agent.replace(raw[1], ""); + this.agent = this.agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + this.platform = `${process.platform} ${process.arch}`; + this.agent = `NodeJS ${process.version}`; + } + } + get Canvas() { + return __privateGet(this, _canvas); + } + set Canvas(val) { + __privateSet(this, _canvas, val); + globalThis.Canvas = val; + } + get Image() { + return __privateGet(this, _image); + } + set Image(val) { + __privateSet(this, _image, val); + globalThis.Image = val; + } + get ImageData() { + return __privateGet(this, _imageData); + } + set ImageData(val) { + __privateSet(this, _imageData, val); + globalThis.ImageData = val; + } + async updateBackend() { + this.backends = Object.keys(tf3.engine().registryFactory); + try { + this.tensorflow = { + version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, + gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 + }; + } catch (e) { + } + this.wasm.supported = typeof WebAssembly !== "undefined"; + this.wasm.backend = this.backends.includes("wasm"); + if (this.wasm.supported && this.wasm.backend) { + this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + } + const c = canvas(100, 100); + const gl = c ? c.getContext("webgl2") : void 0; + this.webgl.supported = typeof gl !== "undefined"; + this.webgl.backend = this.backends.includes("webgl"); + if (this.webgl.supported && this.webgl.backend && gl) { + this.webgl.version = gl.getParameter(gl.VERSION); + this.webgl.vendor = gl.getParameter(gl.VENDOR); + this.webgl.renderer = gl.getParameter(gl.RENDERER); + this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); + } + this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; + this.webgpu.backend = this.backends.includes("webgpu"); + try { + if (this.webgpu.supported) { + const adapter = await navigator.gpu.requestAdapter(); + this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); + } + } catch (e) { + this.webgpu.supported = false; + } + try { + this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); + } catch (e) { + } + } + updateCPU() { + const cpu = { model: "", flags: [] }; + if (this.node && this.platform.startsWith("linux")) { + } + if (!this.cpu) + Object.defineProperty(this, "cpu", { value: cpu }); + else + this.cpu = cpu; + } +}; +_canvas = new WeakMap(); +_image = new WeakMap(); +_imageData = new WeakMap(); +var env = new Env(); + +// src/util/webcam.ts +var WebCam = class { + constructor() { + __publicField(this, "config"); + __publicField(this, "element"); + __publicField(this, "stream"); + __publicField(this, "devices", []); + __publicField(this, "enumerate", async () => { + try { + const devices = await navigator.mediaDevices.enumerateDevices(); + this.devices = devices.filter((device) => device.kind === "videoinput"); + } catch (e) { + this.devices = []; + } + return this.devices; + }); + __publicField(this, "start", async (webcamConfig) => { + var _a, _b; + if (webcamConfig == null ? void 0 : webcamConfig.debug) + this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; + if (webcamConfig == null ? void 0 : webcamConfig.crop) + this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; + if (webcamConfig == null ? void 0 : webcamConfig.mode) + this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; + if (webcamConfig == null ? void 0 : webcamConfig.width) + this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; + if (webcamConfig == null ? void 0 : webcamConfig.height) + this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; + if (webcamConfig == null ? void 0 : webcamConfig.id) + this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; + if (webcamConfig == null ? void 0 : webcamConfig.element) { + if (typeof webcamConfig.element === "string") { + const el = document.getElementById(webcamConfig.element); + if (el && el instanceof HTMLVideoElement) { + this.element = el; + } else { + if (this.config.debug) + log("webcam", "cannot get dom element", webcamConfig.element); + return; + } + } else if (webcamConfig.element instanceof HTMLVideoElement) { + this.element = webcamConfig.element; + } else { + if (this.config.debug) + log("webcam", "unknown dom element", webcamConfig.element); + return; + } + } else { + this.element = document.createElement("video"); + } + const requestedConstraints = { + audio: false, + video: { + facingMode: this.config.mode === "front" ? "user" : "environment", + resizeMode: this.config.crop ? "crop-and-scale" : "none" + } + }; + if (((_a = this.config) == null ? void 0 : _a.width) > 0) + requestedConstraints.video.width = { ideal: this.config.width }; + if (((_b = this.config) == null ? void 0 : _b.height) > 0) + requestedConstraints.video.height = { ideal: this.config.height }; + if (this.config.id) + requestedConstraints.video.deviceId = this.config.id; + this.element.addEventListener("play", () => { + if (this.config.debug) + log("webcam", "play"); + }); + this.element.addEventListener("pause", () => { + if (this.config.debug) + log("webcam", "pause"); + }); + this.element.addEventListener("click", async () => { + if (!this.element || !this.stream) + return; + if (this.element.paused) + await this.element.play(); + else + this.element.pause(); + }); + if (!(navigator == null ? void 0 : navigator.mediaDevices)) { + if (this.config.debug) + log("webcam", "no devices"); + return; + } + try { + this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); + } catch (err) { + log("webcam", err); + return; + } + if (!this.stream) { + if (this.config.debug) + log("webcam", "no stream"); + return; + } + this.element.srcObject = this.stream; + const ready3 = new Promise((resolve) => { + if (!this.element) + resolve(false); + else + this.element.onloadeddata = () => resolve(true); + }); + await ready3; + await this.element.play(); + if (this.config.debug) { + log("webcam", { + width: this.width, + height: this.height, + label: this.label, + stream: this.stream, + track: this.track, + settings: this.settings, + constraints: this.constraints, + capabilities: this.capabilities + }); + } + }); + __publicField(this, "pause", () => { + if (this.element) + this.element.pause(); + }); + __publicField(this, "play", async () => { + if (this.element) + await this.element.play(); + }); + __publicField(this, "stop", () => { + if (this.config.debug) + log("webcam", "stop"); + if (this.track) + this.track.stop(); + }); + this.config = { + element: void 0, + debug: true, + mode: "front", + crop: false, + width: 0, + height: 0 + }; + } + get track() { + if (!this.stream) + return void 0; + return this.stream.getVideoTracks()[0]; + } + get capabilities() { + if (!this.track) + return void 0; + return this.track.getCapabilities ? this.track.getCapabilities() : void 0; + } + get constraints() { + if (!this.track) + return void 0; + return this.track.getConstraints ? this.track.getConstraints() : void 0; + } + get settings() { + if (!this.stream) + return void 0; + const track = this.stream.getVideoTracks()[0]; + return track.getSettings ? track.getSettings() : void 0; + } + get label() { + if (!this.track) + return ""; + return this.track.label; + } + get paused() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.paused) || false; + } + get width() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; + } + get height() { + var _a; + return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; + } +}; + +// src/tfjs/load.ts +var tf4 = __toESM(require_tfjs_esm()); + +// models/models.json +var models_exports = {}; +__export(models_exports, { + age: () => age, + "anti-spoofing": () => anti_spoofing, + antispoof: () => antispoof, + blazeface: () => blazeface, + "blazeface-back": () => blazeface_back, + "blazeface-front": () => blazeface_front, + "blazepose-detector": () => blazepose_detector, + "blazepose-full": () => blazepose_full, + "blazepose-heavy": () => blazepose_heavy, + "blazepose-lite": () => blazepose_lite, + centernet: () => centernet, + default: () => models_default, + efficientpose: () => efficientpose, + "efficientpose-i-lite": () => efficientpose_i_lite, + "efficientpose-ii-lite": () => efficientpose_ii_lite, + "efficientpose-iv": () => efficientpose_iv, + emotion: () => emotion, + faceboxes: () => faceboxes, + facemesh: () => facemesh, + "facemesh-attention": () => facemesh_attention, + "facemesh-attention-pinto": () => facemesh_attention_pinto, + "facemesh-detection-full": () => facemesh_detection_full, + "facemesh-detection-short": () => facemesh_detection_short, + faceres: () => faceres, + "faceres-deep": () => faceres_deep, + gear: () => gear, + gender: () => gender, + "gender-ssrnet-imdb": () => gender_ssrnet_imdb, + handdetect: () => handdetect, + "handlandmark-full": () => handlandmark_full, + "handlandmark-lite": () => handlandmark_lite, + "handlandmark-sparse": () => handlandmark_sparse, + handskeleton: () => handskeleton, + handtrack: () => handtrack, + "insightface-efficientnet-b0": () => insightface_efficientnet_b0, + "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, + "insightface-mobilenet-emore": () => insightface_mobilenet_emore, + "insightface-mobilenet-swish": () => insightface_mobilenet_swish, + iris: () => iris, + liveness: () => liveness, + meet: () => meet, + mobileface: () => mobileface, + mobilefacenet: () => mobilefacenet, + models: () => models, + "movenet-lightning": () => movenet_lightning, + "movenet-multipose": () => movenet_multipose, + "movenet-thunder": () => movenet_thunder, + nanodet: () => nanodet, + "nanodet-e": () => nanodet_e, + "nanodet-g": () => nanodet_g, + "nanodet-m": () => nanodet_m, + "nanodet-t": () => nanodet_t, + posenet: () => posenet, + rvm: () => rvm, + selfie: () => selfie +}); +var antispoof = 853098; +var blazeface = 538928; +var centernet = 4030290; +var emotion = 820516; +var facemesh = 1477958; +var faceres = 6978814; +var handlandmark_lite = 2023432; +var handtrack = 2964837; +var iris = 2599092; +var liveness = 592976; +var models = 0; +var movenet_lightning = 4650216; +var age = 161240; +var blazeface_back = 538928; +var blazeface_front = 402048; +var blazepose_detector = 5928856; +var blazepose_full = 6339202; +var blazepose_heavy = 27502466; +var blazepose_lite = 2726402; +var efficientpose = 5651240; +var faceboxes = 2013002; +var facemesh_attention_pinto = 2387598; +var facemesh_attention = 2382414; +var facemesh_detection_full = 1026192; +var facemesh_detection_short = 201268; +var faceres_deep = 13957620; +var gear = 1498916; +var gender_ssrnet_imdb = 161236; +var gender = 201808; +var handdetect = 3515612; +var handlandmark_full = 5431368; +var handlandmark_sparse = 5286322; +var handskeleton = 5502280; +var meet = 372228; +var mobileface = 2183192; +var mobilefacenet = 5171976; +var movenet_multipose = 9448838; +var movenet_thunder = 12477112; +var nanodet = 7574558; +var posenet = 5032780; +var rvm = 3739355; +var selfie = 212886; +var anti_spoofing = 853098; +var efficientpose_i_lite = 2269064; +var efficientpose_ii_lite = 5651240; +var efficientpose_iv = 25643252; +var insightface_efficientnet_b0 = 13013224; +var insightface_ghostnet_strides1 = 8093408; +var insightface_ghostnet_strides2 = 8049584; +var insightface_mobilenet_emore = 6938536; +var insightface_mobilenet_swish = 12168584; +var nanodet_e = 12319156; +var nanodet_g = 7574558; +var nanodet_m = 1887474; +var nanodet_t = 5294216; +var models_default = { + antispoof, + blazeface, + centernet, + emotion, + facemesh, + faceres, + "handlandmark-lite": handlandmark_lite, + handtrack, + iris, + liveness, + models, + "movenet-lightning": movenet_lightning, + age, + "blazeface-back": blazeface_back, + "blazeface-front": blazeface_front, + "blazepose-detector": blazepose_detector, + "blazepose-full": blazepose_full, + "blazepose-heavy": blazepose_heavy, + "blazepose-lite": blazepose_lite, + efficientpose, + faceboxes, + "facemesh-attention-pinto": facemesh_attention_pinto, + "facemesh-attention": facemesh_attention, + "facemesh-detection-full": facemesh_detection_full, + "facemesh-detection-short": facemesh_detection_short, + "faceres-deep": faceres_deep, + gear, + "gender-ssrnet-imdb": gender_ssrnet_imdb, + gender, + handdetect, + "handlandmark-full": handlandmark_full, + "handlandmark-sparse": handlandmark_sparse, + handskeleton, + meet, + mobileface, + mobilefacenet, + "movenet-multipose": movenet_multipose, + "movenet-thunder": movenet_thunder, + nanodet, + posenet, + rvm, + selfie, + "anti-spoofing": anti_spoofing, + "efficientpose-i-lite": efficientpose_i_lite, + "efficientpose-ii-lite": efficientpose_ii_lite, + "efficientpose-iv": efficientpose_iv, + "insightface-efficientnet-b0": insightface_efficientnet_b0, + "insightface-ghostnet-strides1": insightface_ghostnet_strides1, + "insightface-ghostnet-strides2": insightface_ghostnet_strides2, + "insightface-mobilenet-emore": insightface_mobilenet_emore, + "insightface-mobilenet-swish": insightface_mobilenet_swish, + "nanodet-e": nanodet_e, + "nanodet-g": nanodet_g, + "nanodet-m": nanodet_m, + "nanodet-t": nanodet_t +}; + +// src/tfjs/load.ts +var options = { + cacheModels: true, + cacheSupported: true, + verbose: true, + debug: false, + modelBasePath: "" +}; +var modelStats = {}; +async function httpHandler(url, init4) { + if (options.debug) + log("load model fetch:", url, init4); + return fetch(url, init4); +} +function setModelLoadOptions(config3) { + options.cacheModels = config3.cacheModels; + options.verbose = config3.debug; + options.modelBasePath = config3.modelBasePath; +} +async function loadModel(modelPath) { + var _a, _b, _c, _d; + let modelUrl = join(options.modelBasePath, modelPath || ""); + if (!modelUrl.toLowerCase().endsWith(".json")) + modelUrl += ".json"; + const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); + const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); + const cachedModelName = "indexeddb://" + shortModelName; + modelStats[shortModelName] = { + name: shortModelName, + sizeFromManifest: 0, + sizeLoadedWeights: 0, + sizeDesired: models_exports[shortModelName], + inCache: false, + url: "" + }; + options.cacheSupported = typeof indexedDB !== "undefined"; + let cachedModels = {}; + try { + cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; + } catch (e) { + options.cacheSupported = false; + } + modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); + modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; + const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; + let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); + let loaded = false; + try { + model23.findIOHandler(); + if (options.debug) + log("model load handler:", model23["handler"]); + } catch (err) { + log("error finding model i/o handler:", modelUrl, err); + } + try { + const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; + modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; + if (artifacts) + model23.loadSync(artifacts); + else + model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); + modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; + if (options.verbose) + log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); + loaded = true; + } catch (err) { + log("error loading model:", modelUrl, err); + } + if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { + try { + const saveResult = await model23.save(cachedModelName); + if (options.debug) + log("model saved:", cachedModelName, saveResult); + } catch (err) { + log("error saving model:", modelUrl, err); + } + } + return model23; +} + +// package.json +var version2 = "3.0.2"; + +// src/tfjs/backend.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/tfjs/humangl.ts +var tf5 = __toESM(require_tfjs_esm()); +var config2 = { + name: "humangl", + priority: 999, + canvas: null, + gl: null, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +function register(instance) { + var _a; + if (instance.config.backend !== "humangl") + return; + if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { + log("humangl error: backend invalid context"); + instance.models.reset(); + } + if (!tf5.findBackend(config2.name)) { + try { + config2.canvas = canvas(100, 100); + } catch (err) { + log("humangl error: cannot create canvas:", err); + return; + } + try { + config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); + if (!config2.gl) { + log("humangl error: cannot get webgl context"); + return; + } + const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); + if (!glv2) { + log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); + instance.config.backend = "webgl"; + return; + } + if (config2.canvas) { + config2.canvas.addEventListener("webglcontextlost", (e) => { + log("humangl error:", e.type); + log("possible browser memory leak using webgl or conflict with multiple backend registrations"); + instance.emit("error"); + throw new Error("backend error: webgl context lost"); + }); + config2.canvas.addEventListener("webglcontextrestored", (e) => { + log("humangl error: context restored:", e); + }); + config2.canvas.addEventListener("webglcontextcreationerror", (e) => { + log("humangl error: context create:", e); + }); + } + } catch (err) { + log("humangl error: cannot get webgl context:", err); + return; + } + try { + tf5.setWebGLContext(2, config2.gl); + } catch (err) { + log("humangl error: cannot set webgl context:", err); + return; + } + try { + const ctx = new tf5.GPGPUContext(config2.gl); + tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("humangl error: cannot register webgl backend:", err); + return; + } + try { + const kernels = tf5.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tf5.registerKernel(newKernelConfig); + }); + } catch (err) { + log("humangl error: cannot update webgl backend registration:", err); + return; + } + try { + if (tf5.env().flagRegistry.WEBGL_VERSION) + tf5.env().set("WEBGL_VERSION", 2); + } catch (err) { + log("humangl error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + const backend4 = tf5.backend(); + const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; + if (current) { + if (instance.config.debug) + log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); + } else { + log("humangl error: no current gl context:", current, config2.gl); + } + } +} + +// src/tfjs/constants.ts +var tf6 = __toESM(require_tfjs_esm()); +var constants = { + tf255: 255, + tf1: 1, + tf2: 2, + tf05: 0.5, + tf127: 127.5, + rgb: [0.2989, 0.587, 0.114] +}; +function init() { + constants.tf255 = tf6.scalar(255, "float32"); + constants.tf1 = tf6.scalar(1, "float32"); + constants.tf2 = tf6.scalar(2, "float32"); + constants.tf05 = tf6.scalar(0.5, "float32"); + constants.tf127 = tf6.scalar(127.5, "float32"); + constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); +} + +// src/tfjs/backend.ts +async function getBestBackend() { + var _a; + await env.updateBackend(); + if ((_a = env.tensorflow) == null ? void 0 : _a.version) + return "tensorflow"; + if (env.webgpu.supported && env.webgpu.backend) + return "webgpu"; + if (env.webgl.supported && env.webgl.backend) + return "webgl"; + if (env.wasm.supported && env.wasm.backend) + return "wasm"; + return "cpu"; +} +function registerCustomOps(config3) { + const newKernels = []; + if (!env.kernels.includes("mod")) { + const kernelMod = { + kernelName: "Mod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) + }; + tf7.registerKernel(kernelMod); + env.kernels.push("mod"); + newKernels.push("mod"); + } + if (!env.kernels.includes("floormod")) { + const kernelFloorMod = { + kernelName: "FloorMod", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) + }; + tf7.registerKernel(kernelFloorMod); + env.kernels.push("floormod"); + newKernels.push("floormod"); + } + if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { + const kernelRotateWithOffset = { + kernelName: "RotateWithOffset", + backendName: tf7.getBackend(), + kernelFunc: (op) => tf7.tidy(() => { + const backend4 = tf7.getBackend(); + tf7.setBackend("cpu"); + const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); + tf7.setBackend(backend4); + return t2; + }) + }; + tf7.registerKernel(kernelRotateWithOffset); + env.kernels.push("rotatewithoffset"); + newKernels.push("rotatewithoffset"); + } + if (newKernels.length > 0 && config3.debug) + log("registered kernels:", newKernels); +} +var defaultFlags = {}; +async function check(instance, force = false) { + var _a; + instance.state = "backend"; + if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) + instance.config.backend = await getBestBackend(); + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { + const timeStamp = now(); + if (instance.config.backend && instance.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { + if (instance.config.debug) + log("running inside web worker"); + } + if (env.browser && instance.config.backend === "tensorflow") { + if (instance.config.debug) + log("override: backend set to tensorflow while running in browser"); + instance.config.backend = "webgl"; + } + if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { + if (instance.config.debug) + log(`override: backend set to ${instance.config.backend} while running in nodejs`); + instance.config.backend = "tensorflow"; + } + if (env.browser && instance.config.backend === "webgpu") { + if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { + log("override: backend set to webgpu but browser does not support webgpu"); + instance.config.backend = "webgl"; + } else { + const adapter = await navigator.gpu.requestAdapter(); + if (instance.config.debug) + log("enumerated webgpu adapter:", adapter); + if (!adapter) { + log("override: backend set to webgpu but browser reports no available gpu"); + instance.config.backend = "webgl"; + } else { + const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; + log("webgpu adapter info:", adapterInfo); + } + } + } + let available = Object.keys(tf7.engine().registryFactory); + if (instance.config.backend === "humangl" && !available.includes("humangl")) { + register(instance); + available = Object.keys(tf7.engine().registryFactory); + } + if (instance.config.debug) + log("available backends:", available); + if (!available.includes(instance.config.backend)) { + log(`error: backend ${instance.config.backend} not found in registry`); + instance.config.backend = env.node ? "tensorflow" : "webgl"; + if (instance.config.debug) + log(`override: setting backend ${instance.config.backend}`); + } + if (instance.config.debug) + log("setting backend:", [instance.config.backend]); + if (instance.config.backend === "wasm") { + if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) + tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); + if (instance.config.debug) + log("wasm path:", instance.config.wasmPath); + if (typeof tf7.setWasmPaths !== "undefined") + tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); + else + throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); + let mt = false; + let simd = false; + try { + mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + if (instance.config.debug) + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (instance.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } catch (e) { + log("wasm detection failed"); + } + } + try { + await tf7.setBackend(instance.config.backend); + await tf7.ready(); + } catch (err) { + log("error: cannot set backend:", instance.config.backend, err); + return false; + } + if (instance.config.debug) + defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); + } + if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { + if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) + tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); + if (tf7.env().flagRegistry.WEBGL_EXP_CONV) + tf7.env().set("WEBGL_EXP_CONV", true); + if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + } + if (tf7.getBackend() === "webgpu") { + } + if (instance.config.debug) { + const newFlags = tf7.env().flags; + const updatedFlags = {}; + for (const key of Object.keys(newFlags)) { + if (defaultFlags[key] === newFlags[key]) + continue; + updatedFlags[key] = newFlags[key]; + } + if (instance.config.debug && Object.keys(updatedFlags).length > 0) + log("backend:", tf7.getBackend(), "flags:", updatedFlags); + } + if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { + if (instance.config.debug) + log("flags:", instance.config["flags"]); + for (const [key, val] of Object.entries(instance.config.flags)) { + tf7.env().set(key, val); + } + } + tf7.enableProdMode(); + init(); + instance.performance.initBackend = Math.trunc(now() - timeStamp); + instance.config.backend = tf7.getBackend(); + await env.updateBackend(); + registerCustomOps(instance.config); + env.initial = false; + } + return true; +} +function fakeOps(kernelNames, config3) { + for (const kernelName of kernelNames) { + const kernelConfig = { + kernelName, + backendName: config3.backend, + kernelFunc: (param) => { + var _a; + if (config3.debug) + log("kernelFunc", kernelName, config3.backend, param); + return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; + } + }; + tf7.registerKernel(kernelConfig); + } + env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); +} + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body, + canvas: () => canvas2, + face: () => face, + gesture: () => gesture, + hand: () => hand, + init: () => init2, + object: () => object, + options: () => options2, + person: () => person +}); + +// src/draw/primitives.ts +var getCanvasContext = (input) => { + if (!input) + log("draw error: invalid canvas"); + else if (!input.getContext) + log("draw error: canvas context not defined"); + else { + const ctx = input.getContext("2d"); + if (!ctx) + log("draw error: cannot get canvas context"); + else + return ctx; + } + return null; +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); +var colorDepth = (z, opt) => { + if (!opt.useDepth || typeof z === "undefined") + return opt.color; + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); + return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; +}; +function labels(ctx, str, startX, startY, localOptions2) { + const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); + const x = Math.max(0, startX); + for (let i = line.length - 1; i >= 0; i--) { + const y = i * localOptions2.lineHeight + startY; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(line[i], x + 5, y + 16); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(line[i], x + 4, y + 15); + } +} +function point(ctx, x, y, z, localOptions2) { + ctx.fillStyle = colorDepth(z, localOptions2); + ctx.beginPath(); + ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions2) { + ctx.beginPath(); + ctx.lineWidth = localOptions2.lineWidth; + if (localOptions2.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.moveTo(x + localOptions2.roundRect, y); + ctx.lineTo(x + width - localOptions2.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); + ctx.lineTo(x + width, y + height - localOptions2.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); + ctx.lineTo(x + localOptions2.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); + ctx.lineTo(x, y + localOptions2.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); + ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); + } + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points, localOptions2) { + if (points.length < 2) + return; + ctx.lineWidth = localOptions2.lineWidth; + if (!localOptions2.useCurves || points.length <= 2) { + lines(ctx, points, localOptions2); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions2.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function arrow(ctx, from, to, radius = 5) { + let angle; + let x; + let y; + ctx.beginPath(); + ctx.moveTo(from[0], from[1]); + ctx.lineTo(to[0], to[1]); + angle = Math.atan2(to[1] - from[1], to[0] - from[0]); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + ctx.closePath(); + ctx.stroke(); + ctx.fill(); +} + +// src/draw/options.ts +var options2 = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + alpha: 0.5, + font: 'small-caps 16px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawAttention: true, + drawGestures: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false, + faceLabels: "", + bodyLabels: "", + bodyPartLabels: "", + objectLabels: "", + handLabels: "", + fingerLabels: "", + gestureLabels: "" +}; + +// src/face/facemeshcoords.ts +var meshAnnotations = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], + lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], + lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], + lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], + lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisIndices = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", 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+ 343, + 357, + 412, + 437, + 343, + 399, + 344, + 360, + 440, + 420, + 437, + 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); +function connectionsToIndices(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var pairsLips = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var pairsFaceContour = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +var contourKeypoints = { + lips: connectionsToIndices(pairsLips), + leftEye: connectionsToIndices(pairsLeftEye), + leftEyebrow: connectionsToIndices(pairsLeftEyebrow), + leftIris: connectionsToIndices(pairsLeftIris), + rightEye: connectionsToIndices(pairsRightEye), + rightEyebrow: connectionsToIndices(pairsRightEyebrow), + rightIris: connectionsToIndices(pairsRightIris), + faceOval: connectionsToIndices(pairsFaceContour) +}; + +// src/face/constants.ts +var LIPS_CONNECTIONS = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var FACE_OVAL_CONNECTIONS = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +function connectionsToIndices2(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { + lips: connectionsToIndices2(LIPS_CONNECTIONS), + leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), + leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), + leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), + rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), + rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), + rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), + faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) +}; +var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); +var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); +var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ + 61, + 146, + 91, + 181, + 84, + 17, + 314, + 405, + 321, + 375, + 291, + 185, + 40, + 39, + 37, + 0, + 267, + 269, + 270, + 409, + 78, + 95, + 88, + 178, + 87, + 14, + 317, + 402, + 318, + 324, + 308, + 191, + 80, + 81, + 82, + 13, + 312, + 311, + 310, + 415, + 76, + 77, + 90, + 180, + 85, + 16, + 315, + 404, + 320, + 307, + 306, + 184, + 74, + 73, + 72, + 11, + 302, + 303, + 304, + 408, + 62, + 96, + 89, + 179, + 86, + 15, + 316, + 403, + 319, + 325, + 292, + 183, + 42, + 41, + 38, + 12, + 268, + 271, + 272, + 407 +]; +var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ + 33, + 7, + 163, + 144, + 145, + 153, + 154, + 155, + 133, + 246, + 161, + 160, + 159, + 158, + 157, + 173, + 130, + 25, + 110, + 24, + 23, + 22, + 26, + 112, + 243, + 247, + 30, + 29, + 27, + 28, + 56, + 190, + 226, + 31, + 228, + 229, + 230, + 231, + 232, + 233, + 244, + 113, + 225, + 224, + 223, + 222, + 221, + 189, + 35, + 124, + 46, + 53, + 52, + 65, + 143, + 111, + 117, + 118, + 119, + 120, + 121, + 128, + 245, + 156, + 70, + 63, + 105, + 66, + 107, + 55, + 193 +]; +var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ + 263, + 249, + 390, + 373, + 374, + 380, + 381, + 382, + 362, + 466, + 388, + 387, + 386, + 385, + 384, + 398, + 359, + 255, + 339, + 254, + 253, + 252, + 256, + 341, + 463, + 467, + 260, + 259, + 257, + 258, + 286, + 414, + 446, + 261, + 448, + 449, + 450, + 451, + 452, + 453, + 464, + 342, + 445, + 444, + 443, + 442, + 441, + 413, + 265, + 353, + 276, + 283, + 282, + 295, + 372, + 340, + 346, + 347, + 348, + 349, + 350, + 357, + 465, + 383, + 300, + 293, + 334, + 296, + 336, + 285, + 417 +]; + +// src/draw/face.ts +var localOptions; +function drawLabels(f, ctx) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i; + if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) + return; + let l = localOptions.faceLabels.slice(); + if (f.score) + l = replace(l, "[score]", 100 * f.score); + if (f.gender) + l = replace(l, "[gender]", f.gender); + if (f.genderScore) + l = replace(l, "[genderScore]", 100 * f.genderScore); + if (f.age) + l = replace(l, "[age]", f.age); + if (f.distance) + l = replace(l, "[distance]", 100 * f.distance); + if (f.real) + l = replace(l, "[real]", 100 * f.real); + if (f.live) + l = replace(l, "[live]", 100 * f.live); + if (f.emotion && f.emotion.length > 0) { + const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion2.length > 3) + emotion2.length = 3; + l = replace(l, "[emotions]", emotion2.join(" ")); + } + if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) + l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); + if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) + l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); + if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) + l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); + if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) + l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); + labels(ctx, l, f.box[0], f.box[1], localOptions); +} +function drawIrisElipse(f, ctx) { + var _a, _b, _c, _d; + if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } + if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) { + ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2; + const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2; + ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; + ctx.fill(); + } + } +} +function drawGazeSpheres(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") { + ctx.strokeStyle = "pink"; + const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90; + const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90; + const pathV = new Path2D(` + M ${f.box[0] + f.box[2] / 2} ${f.box[1]} C - ${o} ${e.box[1]}, - ${o} ${e.box[1]+e.box[3]}, - ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} - `),A=new Path2D(` - M ${e.box[0]} ${e.box[1]+e.box[3]/2} + ${valX} ${f.box[1]}, + ${valX} ${f.box[1] + f.box[3]}, + ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} + `); + const pathH = new Path2D(` + M ${f.box[0]} ${f.box[1] + f.box[3] / 2} C - ${e.box[0]} ${r}, - ${e.box[0]+e.box[2]} ${r}, - ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);$t(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function J2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)Z1(r,c,o)}}}function Q2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let x=o.fingerLabels.slice();x=_(x,"[label]",l),ne(r,x,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let x=0;x0?x-1:0][0],c[x>0?x-1:0][1]),r.lineTo(c[x][0],c[x][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function _2(e,t,n){var s;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function $2(e,t,n){var r;let o=a0(x0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let x=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",x),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face + ${f.box[0]} ${valY}, + ${f.box[0] + f.box[2]} ${valY}, + ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} + `); + ctx.stroke(pathH); + ctx.stroke(pathV); + } +} +function drawGazeArrows(f, ctx) { + var _a; + if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { + ctx.strokeStyle = "pink"; + ctx.fillStyle = "pink"; + const leftGaze = [ + f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); + const rightGaze = [ + f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); + } +} +function drawFacePolygons(f, ctx) { + if (localOptions.drawPolygons && f.mesh.length >= 468) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); + lines(ctx, points, localOptions); + } + drawIrisElipse(f, ctx); + } +} +function drawFacePoints(f, ctx) { + if (localOptions.drawPoints && f.mesh.length >= 468) { + for (let i = 0; i < f.mesh.length; i++) { + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); + if (localOptions.drawAttention) { + if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); + if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); + } + } + } +} +function drawFaceBoxes(f, ctx) { + if (localOptions.drawBoxes) { + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); + } +} +function face(inCanvas2, result, drawOptions) { + localOptions = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + for (const f of result) { + drawFaceBoxes(f, ctx); + drawLabels(f, ctx); + if (f.mesh && f.mesh.length > 0) { + drawFacePoints(f, ctx); + drawFacePolygons(f, ctx); + drawGazeSpheres(f, ctx); + drawGazeArrows(f, ctx); + } + } +} + +// src/draw/body.ts +function body(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + ctx.lineWidth = localOptions2.lineWidth; + ctx.font = localOptions2.font; + if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.bodyLabels.slice(); + l = replace(l, "[score]", 100 * result[i].score); + labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); + } + } + if (localOptions2.drawPoints && result[i].keypoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) + continue; + ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); + } + } + if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { + ctx.font = localOptions2.font; + for (const pt of result[i].keypoints) { + if (!pt.score || pt.score === 0) + continue; + let l = localOptions2.bodyPartLabels.slice(); + l = replace(l, "[label]", pt.part); + l = replace(l, "[score]", 100 * pt.score); + labels(ctx, l, pt.position[0], pt.position[1], localOptions2); + } + } + if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { + for (const part of Object.values(result[i].annotations)) { + for (const connected4 of part) + curves(ctx, connected4, localOptions2); + } + } + } +} + +// src/draw/hand.ts +function hand(inCanvas2, result, drawOptions) { + var _a, _b; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.handLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + if (localOptions2.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = colorDepth(pt[2], localOptions2); + point(ctx, pt[0], pt[1], 0, localOptions2); + } + } + } + if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { + for (const [part, pt] of Object.entries(h.annotations)) { + let l = localOptions2.fingerLabels.slice(); + l = replace(l, "[label]", part); + labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); + } + } + if (localOptions2.drawPolygons && h.annotations) { + const addHandLine = (part) => { + if (!part || part.length === 0 || !part[0]) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions2); + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions2.lineWidth; + addHandLine(h.annotations.index); + addHandLine(h.annotations.middle); + addHandLine(h.annotations.ring); + addHandLine(h.annotations.pinky); + addHandLine(h.annotations.thumb); + } + } +} + +// src/draw/object.ts +function object(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (const h of result) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); + if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { + let l = localOptions2.objectLabels.slice(); + l = replace(l, "[label]", h.label); + l = replace(l, "[score]", 100 * h.score); + labels(ctx, l, h.box[0], h.box[1], localOptions2); + } + ctx.stroke(); + } + } +} + +// src/draw/gesture.ts +function gesture(inCanvas2, result, drawOptions) { + var _a; + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions2.font; + ctx.fillStyle = localOptions2.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + const [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + let l = localOptions2.gestureLabels.slice(); + l = replace(l, "[where]", where[0]); + l = replace(l, "[who]", who); + l = replace(l, "[what]", what[1]); + labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); + i += 1; + } + } + } +} + +// src/draw/labels.ts +var defaultLabels = { + face: `face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -118,7 +6467,7523 @@ live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var r5=0;function Fs(e,t,n){let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;sa5,kpt:()=>A5});var A5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],a5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,U1,Gs=5,nt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function Y1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var q1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),q1[0]),A=D.mul(D.div(A,t2),q1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function Xs(e,t,n,o){var c,x;let r=[],s={};s.boxes=Zs(e,U1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let y=a[i],d=l[i],p=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],f={score:y,boxRaw:d,box:p};r.push(f)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function K1(e,t,n){let o={};o.res=_0==null?void 0:_0.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await Xs(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function ve(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function J1(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function ot(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var F0,l5=256,i5=Number.MAX_SAFE_INTEGER,qs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},st=[],Oe=[[0,0],[0,0],[0,0],[0,0]],Q1=0,_1=e=>1-1/(1+Math.exp(e)),e3=e=>Y1(e);async function t3(e){if(R.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await O(e.body.modelPath);let t=F0!=null&&F0.executor?Object.values(F0.modelSignature.inputs):void 0;l5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return F0}function $1(e,t,n){var s,A;let o={};if(!((s=e==null?void 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},f=Wn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Gn=1.65,Vn=[0,5,9,13,17,1,2],SA=0,jA=2,Zn=0,Et=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>J5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Pt(kt(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Pt(kt(n),Gn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=K5(o,[0,0]),c=a.map(d=>[...J5(d,l),d[2]]),x=Fn(r),i=[...L2(n),1],y=[Ze(i,x[0]),Ze(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Zn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,x,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let x=Pt(kt(c),Gn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Xn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,qn;async function Q5(e,t){let n=await qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Rt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Un(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached 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a=Q.unstack(o.scores,1);Q.dispose(a[Kn]),a.splice(Kn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=Q.slice(o.boxes,y,1),p=await d.data();Q.dispose(d);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=ot(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=x[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function $5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Rt(o.keypoints);for(let i of Object.keys(Qn))o.annotations[i]=Qn[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function e1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],zt++;let n=(t.hand.skipTime||0)>g()-_5,o=zt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-_5,l=zt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):(y0.boxes=await WA(e,t),_5=g(),y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))),zt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[x].fingerScore&&y0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=ot(i.box,Jn),d=ot(i.boxRaw,Jn);y0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var C2={};ze(C2,{connected:()=>jt,horizontal:()=>t1,kpt:()=>St,relative:()=>o1,vertical:()=>n1});var St=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],t1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],n1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],o1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),r1=0;function to(e,t){var 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v=0;v((r-1)*w.face[v].box[g0]+U)/r),n0=e.face[v].boxRaw.map((U,g0)=>((r-1)*w.face[v].boxRaw[g0]+U)/r);if(e.face[v].rotation){let U={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};U.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,U.angle={roll:((r-1)*(((p=(d=w.face[v].rotation)==null?void 0:d.angle)==null?void 0:p.roll)||0)+(((b=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:b.roll)||0))/r,yaw:((r-1)*(((T=(M=w.face[v].rotation)==null?void 0:M.angle)==null?void 0:T.yaw)||0)+(((h=(m=e.face[v].rotation)==null?void 0:m.angle)==null?void 0:h.yaw)||0))/r,pitch:((r-1)*(((P=(S=w.face[v].rotation)==null?void 0:S.angle)==null?void 0:P.pitch)||0)+(((q=(I=e.face[v].rotation)==null?void 0:I.angle)==null?void 0:q.pitch)||0))/r},U.gaze={bearing:((r-1)*(((t0=w.face[v].rotation)==null?void 0:t0.gaze.bearing)||0)+(((G=e.face[v].rotation)==null?void 0:G.gaze.bearing)||0))/r,strength:((r-1)*((($=w.face[v].rotation)==null?void 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n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function Ao(e){for(let 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0:p.enabled)&&!this.models.insightface?un(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)==null?void 0:f.includes("blazepose"))?t3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?e3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)==null?void 0:b.includes("efficientpose"))?a3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)==null?void 0:M.includes("movenet"))?co(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)==null?void 0:T.includes("posenet"))?vo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((h=(m=this.instance.config.hand.detector)==null?void 0:m.modelPath)==null?void 0:h.includes("handtrack"))?_n(this.instance.config):null,n.handskeleton=this.instance.config.hand.enabled&&this.instance.config.hand.landmarks&&!this.models.handskeleton&&((P=(S=this.instance.config.hand.detector)==null?void 0:S.modelPath)==null?void 0:P.includes("handtrack"))?$n(this.instance.config):null,(q=(I=this.instance.config.hand.detector)==null?void 0:I.modelPath)!=null&&q.includes("handdetect")&&([n.handpose,n.handskeleton]=this.models.handpose?[null,null]:await Un(this.instance.config)),n.centernet=this.instance.config.object.enabled&&!this.models.centernet&&((t0=this.instance.config.object.modelPath)==null?void 0:t0.includes("centernet"))?r3(this.instance.config):null,n.nanodet=this.instance.config.object.enabled&&!this.models.nanodet&&((G=this.instance.config.object.modelPath)==null?void 0:G.includes("nanodet"))?fo(this.instance.config):null,n.selfie=this.instance.config.segmentation.enabled&&!this.models.selfie&&(($=this.instance.config.segmentation.modelPath)==null?void 0:$.includes("selfie"))?g1(this.instance.config):null,n.meet=this.instance.config.segmentation.enabled&&!this.models.meet&&((A0=this.instance.config.segmentation.modelPath)==null?void 0:A0.includes("meet"))?s1(this.instance.config):null,n.rvm=this.instance.config.segmentation.enabled&&!this.models.rvm&&((v=this.instance.config.segmentation.modelPath)==null?void 0:v.includes("rvm"))?b1(this.instance.config):null;for(let[V,n0]of Object.entries(n))n0!=null&&n0.then&&n0.then(U=>this.models[V]=U);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(E0).find(r=>r.startsWith(n.name));!o||(n.size=E0[o].sizeLoadedWeights,n.url=E0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Ft(this.instance,o,n);r&&t.push(r)}return t}};function So(e,t,n,o,r){var a,l,c,x,i,y;let s=0,A=[];for(let d of e){let p={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let h of t)d.box[0]>h.box[0]&&d.box[0]h.box[1]&&d.box[1]+d.box[3]p.body.box[0]&&h.box[0]+h.box[2]p.body.box[1]&&h.box[1]+h.box[3]p.body.box[0]&&h.box[1]+h.box[3]>p.body.box[1]&&h.box[1]+h.box[3]{h&&h.length===4&&(f.push(h[0],h[0]+h[2]),b.push(h[1],h[1]+h[3]))};M(p.face.box),M((x=p.body)==null?void 0:x.box),M((i=p.hands.left)==null?void 0:i.box),M((y=p.hands.right)==null?void 0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var d0=Z(H());var Bt=` + gaze: [gaze]\xB0`, + body: "body [score]%", + bodyPart: "[label] [score]%", + object: "[label] [score]%", + hand: "[label] [score]%", + finger: "[label]", + gesture: "[where] [who]: [what]" +}; + +// src/draw/draw.ts +var drawTime = 0; +function person(inCanvas2, result, drawOptions) { + const localOptions2 = mergeDeep(options2, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions2.font; + for (let i = 0; i < result.length; i++) { + if (localOptions2.drawBoxes) { + ctx.strokeStyle = localOptions2.color; + ctx.fillStyle = localOptions2.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); + if (localOptions2.drawLabels) { + const label = `person #${i}`; + if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { + ctx.fillStyle = localOptions2.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions2.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +function canvas2(input, output) { + if (!input || !output) + return; + const ctx = getCanvasContext(output); + if (!ctx) + return; + ctx.drawImage(input, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + if (!(result == null ? void 0 : result.performance) || !inCanvas2) + return null; + const timeStamp = now(); + const localOptions2 = mergeDeep(options2, drawOptions); + const promise = Promise.all([ + face(inCanvas2, result.face, localOptions2), + body(inCanvas2, result.body, localOptions2), + hand(inCanvas2, result.hand, localOptions2), + object(inCanvas2, result.object, localOptions2), + gesture(inCanvas2, result.gesture, localOptions2) + ]); + drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); + result.performance.draw = drawTime; + return promise; +} +function init2() { + options2.faceLabels = defaultLabels.face; + options2.bodyLabels = defaultLabels.body; + options2.bodyPartLabels = defaultLabels.bodyPart; + options2.handLabels = defaultLabels.hand; + options2.fingerLabels = defaultLabels.finger; + options2.objectLabels = defaultLabels.object; + options2.gestureLabels = defaultLabels.gesture; +} + +// src/body/blazepose.ts +var tf9 = __toESM(require_tfjs_esm()); + +// src/body/blazeposecoords.ts +var blazeposecoords_exports = {}; +__export(blazeposecoords_exports, { + connected: () => connected, + kpt: () => kpt +}); +var kpt = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftPinky", + "rightPinky", + "leftIndex", + "rightIndex", + "leftThumb", + "rightThumb", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle", + "leftHeel", + "rightHeel", + "leftFoot", + "rightFoot", + "bodyCenter", + "bodyTop", + "leftPalm", + "leftHand", + "rightPalm", + "rightHand" +]; +var connected = { + shoulders: ["leftShoulder", "rightShoulder"], + hips: ["rightHip", "leftHip"], + mouth: ["leftMouth", "rightMouth"], + leftLegUpper: ["leftHip", "leftKnee"], + leftLegLower: ["leftKnee", "leftAnkle"], + leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], + leftTorso: ["leftShoulder", "leftHip"], + leftArmUpper: ["leftShoulder", "leftElbow"], + leftArmLower: ["leftElbow", "leftWrist"], + leftHand: ["leftWrist", "leftPalm"], + leftHandPinky: ["leftPalm", "leftPinky"], + leftHandIndex: ["leftPalm", "leftIndex"], + leftHandThumb: ["leftPalm", "leftThumb"], + leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], + rightLegUpper: ["rightHip", "rightKnee"], + rightLegLower: ["rightKnee", "rightAnkle"], + rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], + rightTorso: ["rightShoulder", "rightHip"], + rightArmUpper: ["rightShoulder", "rightElbow"], + rightArmLower: ["rightElbow", "rightWrist"], + rightHand: ["rightWrist", "rightPalm"], + rightHandPinky: ["rightPalm", "rightPinky"], + rightHandIndex: ["rightPalm", "rightIndex"], + rightHandThumb: ["rightPalm", "rightThumb"], + rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] +}; + +// src/body/blazeposedetector.ts +var tf8 = __toESM(require_tfjs_esm()); +var model; +var inputSize = 224; +var anchorTensor; +var numLayers = 5; +var strides = [8, 16, 32, 32, 32]; +function createAnchors() { + const anchors3 = []; + let layerId = 0; + while (layerId < numLayers) { + let anchorCount = 0; + let lastSameStrideLayer = layerId; + while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { + anchorCount += 2; + lastSameStrideLayer++; + } + const stride = strides[layerId]; + const featureMapHeight = Math.ceil(inputSize / stride); + const featureMapWidth = Math.ceil(inputSize / stride); + for (let y = 0; y < featureMapHeight; ++y) { + for (let x = 0; x < featureMapWidth; ++x) { + for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); + } + } + } + layerId = lastSameStrideLayer; + } + anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; +} +async function loadDetector(config3) { + if (env.initial) + model = null; + if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { + model = await loadModel(config3.body["detector"].modelPath); + const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; + inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug && model) + log("cached model:", model["modelUrl"]); + createAnchors(); + return model; +} +var cropFactor = [5, 5]; +function decodeBoxes(boxesTensor, anchor) { + return tf8.tidy(() => { + const split6 = tf8.split(boxesTensor, 12, 1); + let xCenter = tf8.squeeze(split6[0]); + let yCenter = tf8.squeeze(split6[1]); + let width = tf8.squeeze(split6[2]); + let height = tf8.squeeze(split6[3]); + xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); + yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); + width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); + height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); + const xMin = tf8.sub(xCenter, tf8.div(width, 2)); + const yMin = tf8.sub(yCenter, tf8.div(height, 2)); + const xMax = tf8.add(xMin, width); + const yMax = tf8.add(yMin, height); + const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); + return boxes; + }); +} +async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { + var _a, _b; + const detectedBoxes = []; + const t2 = {}; + t2.boxes = decodeBoxes(boxesTensor, anchorTensor); + t2.scores = tf8.sigmoid(logitsTensor); + t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); + const nms = await t2.nms.data(); + const scores = await t2.scores.data(); + const boxes = await t2.boxes.array(); + for (const i of Array.from(nms)) { + const score = scores[i]; + const boxRaw = boxes[i]; + const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; + const detectedBox = { score, boxRaw, box }; + detectedBoxes.push(detectedBox); + } + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return detectedBoxes; +} +async function detectBoxes(input, config3, outputSize2) { + const t2 = {}; + t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); + t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); + t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); + t2.logits = tf8.squeeze(t2.logitsRaw); + t2.boxes = tf8.squeeze(t2.boxesRaw); + const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); + Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); + return boxes; +} + +// src/util/box.ts +function calc(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function square(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; + const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); + const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function scale(box, scaleFact) { + const dist = [box[2] * scaleFact, box[3] * scaleFact]; + const newBox = [ + box[0] - (dist[0] - box[2]) / 2, + box[1] - (dist[1] - box[3]) / 2, + dist[0], + dist[1] + ]; + return newBox; +} + +// src/body/blazepose.ts +var model2; +var inputSize2 = 256; +var skipped = Number.MAX_SAFE_INTEGER; +var outputNodes = { + landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], + detector: [] +}; +var cache = []; +var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; +var lastTime = 0; +var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); +var loadDetect = (config3) => loadDetector(config3); +async function loadPose(config3) { + if (env.initial) + model2 = null; + if (!model2) { + model2 = await loadModel(config3.body.modelPath); + const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; + inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + } else if (config3.debug) + log("cached model:", model2["modelUrl"]); + return model2; +} +function prepareImage(input, size2, cropBox) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + let final; + if (cropBox) { + t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + } + if (input.shape[1] !== input.shape[2]) { + const height = [ + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 + ]; + const width = [ + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 + ]; + padding = [ + [0, 0], + height, + width, + [0, 0] + ]; + t2.pad = tf9.pad(t2.cropped || input, padding); + t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else if (input.shape[1] !== size2) { + t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); + final = tf9.div(t2.resize, constants.tf255); + } else { + final = tf9.div(t2.cropped || input, constants.tf255); + } + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + return final; +} +function rescaleKeypoints(keypoints, outputSize2, cropBox) { + for (const kpt4 of keypoints) { + kpt4.position = [ + Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), + Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), + kpt4.position[2] + ]; + kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; + } + if (cropBox) { + const width = cropBox[2] - cropBox[0]; + const height = cropBox[3] - cropBox[1]; + for (const kpt4 of keypoints) { + kpt4.positionRaw = [ + kpt4.positionRaw[0] / height + cropBox[1], + kpt4.positionRaw[1] / width + cropBox[0], + kpt4.positionRaw[2] + ]; + kpt4.position = [ + Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), + Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), + kpt4.positionRaw[2] + ]; + } + } + return keypoints; +} +function fixKeypoints(keypoints) { + const leftPalm = keypoints.find((k) => k.part === "leftPalm"); + const leftWrist = keypoints.find((k) => k.part === "leftWrist"); + const leftIndex = keypoints.find((k) => k.part === "leftIndex"); + leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; + const rightPalm = keypoints.find((k) => k.part === "rightPalm"); + const rightWrist = keypoints.find((k) => k.part === "rightWrist"); + const rightIndex = keypoints.find((k) => k.part === "rightIndex"); + rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; +} +async function detectLandmarks(input, config3, outputSize2) { + if (!(model2 == null ? void 0 : model2["executor"])) + return null; + const t2 = {}; + [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); + const poseScore = (await t2.poseflag.data())[0]; + const points = await t2.ld.data(); + const distances = await t2.world.data(); + Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); + const keypointsRelative = []; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + const score = sigmoid2(points[depth * i + 3]); + const presence = sigmoid2(points[depth * i + 4]); + const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; + const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; + const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; + const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; + keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); + } + if (poseScore < (config3.body.minConfidence || 0)) + return null; + fixKeypoints(keypointsRelative); + const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); + const kpts = keypoints.map((k) => k.position); + const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; + return body4; +} +async function predict(input, config3) { + var _a, _b, _c; + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime; + const skipFrame = skipped < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { + skipped++; + } else { + let boxes = []; + if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { + const preparedImage = prepareImage(input, 224); + boxes = await detectBoxes(preparedImage, config3, outputSize2); + tf9.dispose(preparedImage); + } else { + boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; + } + for (let i = 0; i < boxes.length; i++) { + const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); + cache.length = 0; + const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); + tf9.dispose(preparedBox); + if (!bodyResult) + continue; + bodyResult.id = i; + cache.push(bodyResult); + } + lastTime = now(); + skipped = 0; + } + return cache; +} + +// src/object/centernet.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/object/labels.ts +var labels2 = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/centernet.ts +var model3; +var inputSize3 = 0; +var last2 = []; +var lastTime2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +async function load(config3) { + if (env.initial) + model3 = null; + if (!model3) { + model3 = await loadModel(config3.object.modelPath); + const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; + inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} +async function process3(res, outputShape, config3) { + if (!res) + return []; + const t2 = {}; + const results = []; + const detections = await res.array(); + t2.squeeze = tf10.squeeze(res); + const arr = tf10.split(t2.squeeze, 6, 1); + t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); + t2.boxes = tf10.squeeze(t2.stack); + t2.scores = tf10.squeeze(arr[4]); + t2.classes = tf10.squeeze(arr[5]); + tf10.dispose([res, ...arr]); + t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t2.nms.data(); + let i = 0; + for (const id of Array.from(nms)) { + const score = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + if (Number.isNaN(classVal)) + continue; + const label = labels2[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize3, + detections[0][id][1] / inputSize3 + ]; + const boxRaw = [ + x, + y, + detections[0][id][2] / inputSize3 - x, + detections[0][id][3] / inputSize3 - y + ]; + const box = [ + Math.trunc(boxRaw[0] * outputShape[0]), + Math.trunc(boxRaw[1] * outputShape[1]), + Math.trunc(boxRaw[2] * outputShape[0]), + Math.trunc(boxRaw[3] * outputShape[1]) + ]; + results.push({ id: i++, score, class: classVal, label, box, boxRaw }); + } + Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); + return results; +} +async function predict2(input, config3) { + if (!(model3 == null ? void 0 : model3["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; + const skipFrame = skipped2 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { + skipped2++; + return last2; + } + skipped2 = 0; + return new Promise(async (resolve) => { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); + const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; + lastTime2 = now(); + tf10.dispose(resize); + const obj = await process3(objectT, outputSize2, config3); + last2 = obj; + resolve(obj); + }); +} + +// src/body/efficientpose.ts +var tf11 = __toESM(require_tfjs_esm()); + +// src/body/efficientposecoords.ts +var efficientposecoords_exports = {}; +__export(efficientposecoords_exports, { + connected: () => connected2, + kpt: () => kpt2 +}); +var kpt2 = [ + "head", + "neck", + "rightShoulder", + "rightElbow", + "rightWrist", + "chest", + "leftShoulder", + "leftElbow", + "leftWrist", + "bodyCenter", + "rightHip", + "rightKnee", + "rightAnkle", + "leftHip", + "leftKnee", + "leftAnkle" +]; +var connected2 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/efficientpose.ts +var model4; +var lastTime3 = 0; +var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; +var skipped3 = Number.MAX_SAFE_INTEGER; +async function load2(config3) { + if (env.initial) + model4 = null; + if (!model4) + model4 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + const reshaped = tf11.reshape(inputs, [height * width]); + const max5 = tf11.max(reshaped, 0); + const newScore = (await max5.data())[0]; + if (newScore > minScore) { + const coordinates = tf11.argMax(reshaped, 0); + const mod3 = tf11.mod(coordinates, width); + const x = (await mod3.data())[0]; + const div15 = tf11.div(coordinates, width); + const y = (await div15.data())[0]; + tf11.dispose([reshaped, max5, coordinates, mod3, div15]); + return [x, y, newScore]; + } + tf11.dispose([reshaped, max5]); + return [0, 0, newScore]; +} +async function predict3(image28, config3) { + if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) + return []; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; + const skipFrame = skipped3 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { + skipped3++; + return [cache2]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + const tensor6 = tf11.tidy(() => { + var _a, _b; + const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); + const enhance2 = tf11.mul(resize, constants.tf2); + const norm = tf11.sub(enhance2, constants.tf1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = model4 == null ? void 0 : model4.execute(tensor6); + lastTime3 = now(); + tf11.dispose(tensor6); + if (resT) { + cache2.keypoints.length = 0; + const squeeze14 = tf11.squeeze(resT); + tf11.dispose(resT); + const stack5 = tf11.unstack(squeeze14, 2); + tf11.dispose(squeeze14); + for (let id = 0; id < stack5.length; id++) { + const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); + if (partScore > (config3.body.minConfidence || 0)) { + cache2.keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: kpt2[id], + positionRaw: [ + x2 / model4.inputs[0].shape[2], + y2 / model4.inputs[0].shape[1] + ], + position: [ + Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), + Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) + ] + }); + } + } + stack5.forEach((s) => tf11.dispose(s)); + } + cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = cache2.keypoints.map((a) => a.position[0]); + const y = cache2.keypoints.map((a) => a.position[1]); + cache2.box = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); + const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); + cache2.boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + for (const [name, indexes] of Object.entries(connected2)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + cache2.annotations[name] = pt; + } + resolve([cache2]); + }); +} + +// src/face/face.ts +var tf25 = __toESM(require_tfjs_esm()); + +// src/face/facemesh.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/face/blazeface.ts +var tf13 = __toESM(require_tfjs_esm()); + +// src/face/facemeshutil.ts +var tf12 = __toESM(require_tfjs_esm()); +var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; +var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; +var clampBox = (box, input) => box ? [ + Math.trunc(Math.max(0, box.startPoint[0])), + Math.trunc(Math.max(0, box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) +] : [0, 0, 0, 0]; +var getRawBox = (box, input) => box ? [ + box.startPoint[0] / (input.shape[2] || 0), + box.startPoint[1] / (input.shape[1] || 0), + (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), + (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) +] : [0, 0, 0, 0]; +var scaleBoxCoordinates = (box, factor) => { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; +}; +var cutAndResize = (box, image28, cropSize) => { + const h = image28.shape[1]; + const w = image28.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; + const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); + const norm = tf12.div(crop, constants.tf255); + tf12.dispose(crop); + return norm; +}; +var enlargeBox = (box, factor) => { + const center = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; +}; +var squarifyBox = (box) => { + const centers = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = Math.max(...size2) / 2; + return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; +}; +var calculateLandmarksBoundingBox = (landmarks) => { + const x = landmarks.map((d) => d[0]); + const y = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; +}; +var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); +var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +var dot = (v1, v2) => { + let product = 0; + for (let i = 0; i < v1.length; i++) + product += v1[i] * v2[i]; + return product; +}; +var getColumnFrom2DArr = (arr, columnIndex) => { + const column = []; + for (let i = 0; i < arr.length; i++) + column.push(arr[i][columnIndex]); + return column; +}; +var multiplyTransformMatrices = (mat1, mat2) => { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + return product; +}; +var buildRotationMatrix = (rotation, center) => { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +}; +var invertTransformMatrix = (matrix) => { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; + return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; +}; +var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; +function generateAnchors(inputSize10) { + const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize10 + stride - 1) / stride); + const gridCols = Math.floor((inputSize10 + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) + anchors3.push([anchorX, anchorY]); + } + } + } + return anchors3; +} +function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { + const boxSize = getBoxSize(box); + const coordsScaled = coordsRaw.map((coord) => [ + boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), + boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), + coord[2] || 0 + ]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; + const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; + const boxCenter = getBoxCenter(box); + const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + offsets[0]), + Math.trunc(coord[1] + offsets[1]), + Math.trunc(coord[2] || 0) + ]); +} +function correctFaceRotation(rotate, box, input, inputSize10) { + const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + let angle = 0; + let rotationMatrix = fixedRotationMatrix; + let face4; + if (rotate && env.kernels.includes("rotatewithoffset")) { + angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + if (largeAngle) { + const center = getBoxCenter(box); + const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; + const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); + rotationMatrix = buildRotationMatrix(-angle, center); + face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); + tf12.dispose(rotated); + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + return [angle, rotationMatrix, face4]; +} +var findFaceCenter = (mesh) => { + const x = mesh.map((m) => m[0]); + const y = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; +}; +var calculateFaceBox = (mesh, previousBox) => { + const center = findFaceCenter(mesh); + const boxSize = getBoxSize(previousBox); + const calculatedBox = { + startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], + endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] + }; + return calculatedBox; +}; + +// src/face/blazeface.ts +var keypointsCount = 6; +var faceBoxScaleFactor = 1.4; +var model5; +var anchors = null; +var inputSize4 = 0; +var inputSizeT = null; +var size = () => inputSize4; +async function load3(config3) { + var _a; + if (env.initial) + model5 = null; + if (!model5) + model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model5["modelUrl"]); + inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; + inputSizeT = tf13.scalar(inputSize4, "int32"); + anchors = tf13.tensor2d(generateAnchors(inputSize4)); + return model5; +} +function decodeBoxes2(boxOutputs) { + if (!anchors || !inputSizeT) + return tf13.zeros([0, 0]); + const t2 = {}; + t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); + t2.centers = tf13.add(t2.boxStarts, anchors); + t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); + t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); + t2.centersNormalized = tf13.div(t2.centers, inputSizeT); + t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); + t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); + t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); + t2.startNormalized = tf13.mul(t2.starts, inputSizeT); + t2.endNormalized = tf13.mul(t2.ends, inputSizeT); + const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} +async function getBoxes(inputImage, config3) { + var _a, _b, _c, _d; + if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return []; + const t2 = {}; + t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); + t2.div = tf13.div(t2.resized, constants.tf127); + t2.normalized = tf13.sub(t2.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t2.normalized); + if (Array.isArray(res) && res.length > 2) { + const sorted = res.sort((a, b) => a.size - b.size); + t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); + t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); + t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); + t2.batch = tf13.squeeze(t2.concat, [0]); + } else if (Array.isArray(res)) { + t2.batch = tf13.squeeze(res[0]); + } else { + t2.batch = tf13.squeeze(res); + } + tf13.dispose(res); + t2.boxes = decodeBoxes2(t2.batch); + t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); + t2.sigmoid = tf13.sigmoid(t2.logits); + t2.scores = tf13.squeeze(t2.sigmoid); + t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); + const nms = await t2.nms.array(); + const boxes = []; + const scores = await t2.scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scores[nms[i]]; + if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { + const b = {}; + b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); + b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = tf13.squeeze(b.slice); + b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); + const points = await b.bbox.data(); + const rawBox = { + startPoint: [points[0], points[1]], + endPoint: [points[2], points[3]], + landmarks: await b.landmarks.array(), + confidence + }; + const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); + const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); + const squaredBox = squarifyBox(enlargedBox); + boxes.push(squaredBox); + Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); + } + } + Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); + return boxes; +} + +// src/face/iris.ts +var tf14 = __toESM(require_tfjs_esm()); +var model6; +var inputSize5 = 0; +var irisEnlarge = 2.3; +var leftOutline = meshAnnotations.leftEyeLower0; +var rightOutline = meshAnnotations.rightEyeLower0; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +async function load4(config3) { + var _a, _b; + if (env.initial) + model6 = null; + if (!model6) + model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model6["modelUrl"]); + inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; + if (inputSize5 === -1) + inputSize5 = 64; + return model6; +} +function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < irisIndices.length; i++) { + const { key, indices } = irisIndices[i]; + const originalIndices = meshAnnotations[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index2 = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index2][0], + newCoords[index2][1], + (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var getLeftToRightEyeDepthDifference = (rawCoords) => { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; +}; +var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { + const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); + const boxSize = getBoxSize(box); + let crop = tf14.image.cropAndResize(face4, [[ + box.startPoint[1] / meshSize, + box.startPoint[0] / meshSize, + box.endPoint[1] / meshSize, + box.endPoint[0] / meshSize + ]], [0], [inputSize5, inputSize5]); + if (flip && env.kernels.includes("flipleftright")) { + const flipped = tf14.image.flipLeftRight(crop); + tf14.dispose(crop); + crop = flipped; + } + return { box, boxSize, crop }; +}; +var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; +}; +var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { + const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); +}; +async function augmentIris(rawCoords, face4, meshSize) { + if (!(model6 == null ? void 0 : model6["executor"])) + return rawCoords; + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); + const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); + tf14.dispose(leftEyeCrop); + tf14.dispose(rightEyeCrop); + const eyePredictions = model6.execute(combined); + tf14.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tf14.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); + const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; +} + +// src/face/attention.ts +async function augment(rawCoords, results) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + const t2 = { + lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), + irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), + eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), + irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), + eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) + }; + for (const val of Object.values(t2)) { + if (!val) + return rawCoords; + } + const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisL.length / 2; i++) + rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); + const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; + for (let i = 0; i < t2.irisR.length / 2; i++) + rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); + for (let i = 0; i < t2.eyeL.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.eyeR.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; + for (let i = 0; i < t2.lips.length / 2; i++) + rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; + return rawCoords; +} + +// src/face/facemesh.ts +var cache3 = { + boxes: [], + skipped: Number.MAX_SAFE_INTEGER, + timestamp: 0 +}; +var model7 = null; +var inputSize6 = 0; +async function predict4(input, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + if (!(model7 == null ? void 0 : model7["executor"])) + return []; + const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; + const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); + if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { + cache3.boxes = await getBoxes(input, config3); + cache3.timestamp = now(); + cache3.skipped = 0; + } else { + cache3.skipped++; + } + const faces = []; + const newCache = []; + let id = 0; + const size2 = inputSize6; + for (let i = 0; i < cache3.boxes.length; i++) { + const box = cache3.boxes[i]; + let angle = 0; + let rotationMatrix; + const face4 = { + id: id++, + mesh: [], + meshRaw: [], + box: [0, 0, 0, 0], + boxRaw: [0, 0, 0, 0], + score: 0, + boxScore: 0, + faceScore: 0, + annotations: {} + }; + [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); + if (config3.filter.equalization) { + const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; + tf15.dispose(face4.tensor); + if (equilized) + face4.tensor = equilized; + } + face4.boxScore = Math.round(100 * box.confidence) / 100; + if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } else if (!model7) { + if (config3.debug) + log("face mesh detection requested, but model is not loaded"); + } else { + if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { + config3.face.attention.enabled = false; + tf15.dispose(face4.tensor); + return faces; + } + const results = model7.execute(face4.tensor); + const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); + const faceConfidence = await confidenceT.data(); + face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; + if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { + box.confidence = face4.faceScore; + if (config3.face.mesh.keepInvalid) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } + } else { + const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); + const coordsReshaped = tf15.reshape(meshT, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + tf15.dispose(coordsReshaped); + if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { + rawCoords = await augment(rawCoords, results); + } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); + } + face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); + for (const key of Object.keys(meshAnnotations)) + face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); + face4.score = face4.faceScore; + const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; + face4.box = clampBox(calculatedBox, input); + face4.boxRaw = getRawBox(calculatedBox, input); + newCache.push(calculatedBox); + } + tf15.dispose(results); + } + if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) + faces.push(face4); + else + tf15.dispose(face4.tensor); + } + cache3.boxes = newCache; + return faces; +} +async function load5(config3) { + var _a, _b, _c, _d, _e, _f; + if (env.initial) + model7 = null; + if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { + if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) + model7 = null; + } + if (!model7) { + if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) + model7 = await loadModel(config3.face.attention.modelPath); + else + model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); + } else if (config3.debug) { + log("cached model:", model7["modelUrl"]); + } + inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; + return model7; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/gear/emotion.ts +var tf16 = __toESM(require_tfjs_esm()); +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model8; +var last3 = []; +var lastCount = 0; +var lastTime4 = 0; +var skipped4 = Number.MAX_SAFE_INTEGER; +async function load6(config3) { + var _a; + if (env.initial) + model8 = null; + if (!model8) + model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model8["modelUrl"]); + return model8; +} +async function predict5(image28, config3, idx, count2) { + var _a, _b; + if (!model8) + return []; + const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; + if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { + skipped4++; + return last3[idx]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + var _a2; + const obj = []; + if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { + const t2 = {}; + const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; + t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); + t2.channels = tf16.mul(t2.resize, constants.rgb); + t2.grayscale = tf16.sum(t2.channels, 3, true); + t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); + t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); + t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); + lastTime4 = now(); + const data = await t2.emotion.data(); + for (let i = 0; i < data.length; i++) { + if (data[i] > (config3.face.emotion.minConfidence || 0)) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); + } + last3[idx] = obj; + lastCount = count2; + resolve(obj); + }); +} + +// src/face/faceres.ts +var tf17 = __toESM(require_tfjs_esm()); +var model9; +var last4 = []; +var lastTime5 = 0; +var lastCount2 = 0; +var skipped5 = Number.MAX_SAFE_INTEGER; +async function load7(config3) { + var _a; + if (env.initial) + model9 = null; + if (!model9) + model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +function enhance(input) { + const tensor6 = input.image || input.tensor || input; + if (!(model9 == null ? void 0 : model9.inputs[0].shape)) + return tensor6; + const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + const norm = tf17.mul(crop, constants.tf255); + tf17.dispose(crop); + return norm; +} +async function predict6(image28, config3, idx, count2) { + var _a, _b, _c, _d; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if (!(model9 == null ? void 0 : model9["executor"])) + return obj; + const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; + if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped5++; + return last4[idx]; + } + skipped5 = 0; + return new Promise(async (resolve) => { + var _a2; + if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { + const enhanced = enhance(image28); + const resT = model9 == null ? void 0 : model9.execute(enhanced); + lastTime5 = now(); + tf17.dispose(enhanced); + const genderT = resT.find((t2) => t2.shape[1] === 1); + const gender2 = await genderT.data(); + const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; + if (confidence > (config3.face.description.minConfidence || 0)) { + obj.gender = gender2[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); + const ageIdx = (await argmax.data())[0]; + tf17.dispose(argmax); + const ageT = resT.find((t2) => t2.shape[1] === 100); + const all2 = await ageT.data(); + obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; + if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) + log("faceres error:", { model: model9, result: resT }); + const desc = resT.find((t2) => t2.shape[1] === 1024); + const descriptor = desc ? await desc.data() : []; + obj.descriptor = Array.from(descriptor); + resT.forEach((t2) => tf17.dispose(t2)); + } + last4[idx] = obj; + lastCount2 = count2; + resolve(obj); + }); +} + +// src/face/mask.ts +var expandFact = 0.1; +var alpha = 0.5; +function insidePoly(x, y, polygon) { + let inside = false; + let j = polygon.length - 1; + for (let i = 0; i < polygon.length; j = i++) { + if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + inside = !inside; + } + return inside; +} +async function mask(face4) { + if (!face4.tensor) + return face4.tensor; + if (!face4.mesh || face4.mesh.length < 100) + return face4.tensor; + const width = face4.tensor.shape[2] || 0; + const height = face4.tensor.shape[1] || 0; + const buffer = await face4.tensor.buffer(); + let silhouette = []; + for (const pt of meshAnnotations.silhouette) + silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); + if (expandFact && expandFact > 0) + silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); + for (let x = 0; x < width; x++) { + for (let y = 0; y < height; y++) { + const inside = insidePoly(x / width, y / width, silhouette); + if (!inside) { + buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); + buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); + buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); + } + } + } + const output = buffer.toTensor(); + return output; +} + +// src/face/antispoof.ts +var tf18 = __toESM(require_tfjs_esm()); +var model10; +var cached = []; +var skipped6 = Number.MAX_SAFE_INTEGER; +var lastCount3 = 0; +var lastTime6 = 0; +async function load8(config3) { + var _a; + if (env.initial) + model10 = null; + if (!model10) + model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model10["modelUrl"]); + return model10; +} +async function predict7(image28, config3, idx, count2) { + var _a, _b; + if (!(model10 == null ? void 0 : model10["executor"])) + return 0; + const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; + const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { + skipped6++; + return cached[idx]; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); + const res = model10 == null ? void 0 : model10.execute(resize); + const num = (await res.data())[0]; + cached[idx] = Math.round(100 * num) / 100; + lastCount3 = count2; + lastTime6 = now(); + tf18.dispose([resize, res]); + resolve(cached[idx]); + }); +} + +// src/face/liveness.ts +var tf19 = __toESM(require_tfjs_esm()); +var model11; +var cached2 = []; +var skipped7 = Number.MAX_SAFE_INTEGER; +var lastCount4 = 0; +var lastTime7 = 0; +async function load9(config3) { + var _a; + if (env.initial) + model11 = null; + if (!model11) + model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model11["modelUrl"]); + return model11; +} +async function predict8(image28, config3, idx, count2) { + var _a, _b; + if (!(model11 == null ? void 0 : model11["executor"])) + return 0; + const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; + const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { + skipped7++; + return cached2[idx]; + } + skipped7 = 0; + return new Promise(async (resolve) => { + const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); + const res = model11 == null ? void 0 : model11.execute(resize); + const num = (await res.data())[0]; + cached2[idx] = Math.round(100 * num) / 100; + lastCount4 = count2; + lastTime7 = now(); + tf19.dispose([resize, res]); + resolve(cached2[idx]); + }); +} + +// src/gear/gear.ts +var tf20 = __toESM(require_tfjs_esm()); +var model12; +var last5 = []; +var raceNames = ["white", "black", "asian", "indian", "other"]; +var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; +var lastCount5 = 0; +var lastTime8 = 0; +var skipped8 = Number.MAX_SAFE_INTEGER; +async function load10(config3) { + var _a; + if (env.initial) + model12 = null; + if (!model12) + model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model12["modelUrl"]); + return model12; +} +async function predict9(image28, config3, idx, count2) { + var _a, _b; + if (!model12) + return { age: 0, gender: "unknown", genderScore: 0, race: [] }; + const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; + if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { + skipped8++; + return last5[idx]; + } + skipped8 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + if (!(model12 == null ? void 0 : model12.inputs[0].shape)) + return; + const t2 = {}; + const box = [[0, 0.1, 0.9, 0.9]]; + t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); + const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; + if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) + [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); + const gender2 = await t2.gender.data(); + obj.gender = gender2[0] > gender2[1] ? "male" : "female"; + obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; + const race = await t2.race.data(); + for (let i = 0; i < race.length; i++) { + if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) + obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); + } + obj.race.sort((a, b) => b.score - a.score); + const ageDistribution = Array.from(await t2.age.data()); + const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); + let age2 = ageSorted[0][0]; + for (let i = 1; i < ageSorted.length; i++) + age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); + obj.age = Math.round(10 * age2) / 10; + Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); + last5[idx] = obj; + lastCount5 = count2; + lastTime8 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-age.ts +var tf21 = __toESM(require_tfjs_esm()); +var model13; +var last6 = []; +var lastCount6 = 0; +var lastTime9 = 0; +var skipped9 = Number.MAX_SAFE_INTEGER; +async function load11(config3) { + if (env.initial) + model13 = null; + if (!model13) + model13 = await loadModel(config3.face["ssrnet"].modelPathAge); + else if (config3.debug) + log("cached model:", model13["modelUrl"]); + return model13; +} +async function predict10(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model13) + return { age: 0 }; + const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; + if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { + skipped9++; + return last6[idx]; + } + skipped9 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) + return; + const t2 = {}; + t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); + t2.enhance = tf21.mul(t2.resize, constants.tf255); + const obj = { age: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.age = model13.execute(t2.enhance); + if (t2.age) { + const data = await t2.age.data(); + obj.age = Math.trunc(10 * data[0]) / 10; + } + Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); + last6[idx] = obj; + lastCount6 = count2; + lastTime9 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-gender.ts +var tf22 = __toESM(require_tfjs_esm()); +var model14; +var last7 = []; +var lastCount7 = 0; +var lastTime10 = 0; +var skipped10 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load12(config3) { + var _a; + if (env.initial) + model14 = null; + if (!model14) + model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); + else if (config3.debug) + log("cached model:", model14["modelUrl"]); + return model14; +} +async function predict11(image28, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model14) + return { gender: "unknown", genderScore: 0 }; + const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; + if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped10++; + return last7[idx]; + } + skipped10 = 0; + return new Promise(async (resolve) => { + var _a2; + if (!(model14 == null ? void 0 : model14.inputs[0].shape)) + return; + const t2 = {}; + t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); + t2.enhance = tf22.tidy(() => { + const [red, green, blue] = tf22.split(t2.resize, 3, 3); + const redNorm = tf22.mul(red, rgb[0]); + const greenNorm = tf22.mul(green, rgb[1]); + const blueNorm = tf22.mul(blue, rgb[2]); + const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); + const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); + return normalize2; + }); + const obj = { gender: "unknown", genderScore: 0 }; + if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) + t2.gender = model14.execute(t2.enhance); + const data = await t2.gender.data(); + obj.gender = data[0] > data[1] ? "female" : "male"; + obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; + Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); + last7[idx] = obj; + lastCount7 = count2; + lastTime10 = now(); + resolve(obj); + }); +} + +// src/face/mobilefacenet.ts +var tf23 = __toESM(require_tfjs_esm()); +var model15; +var last8 = []; +var lastCount8 = 0; +var lastTime11 = 0; +var skipped11 = Number.MAX_SAFE_INTEGER; +async function load13(config3) { + var _a; + if (env.initial) + model15 = null; + if (!model15) + model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model15["modelUrl"]); + return model15; +} +async function predict12(input, config3, idx, count2) { + var _a, _b; + if (!(model15 == null ? void 0 : model15["executor"])) + return []; + const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; + if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { + skipped11++; + return last8[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); + t2.data = model15.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); + } + last8[idx] = data; + lastCount8 = count2; + lastTime11 = now(); + resolve(data); + }); +} + +// src/face/insightface.ts +var tf24 = __toESM(require_tfjs_esm()); +var model16; +var last9 = []; +var lastCount9 = 0; +var lastTime12 = 0; +var skipped12 = Number.MAX_SAFE_INTEGER; +async function load14(config3) { + if (env.initial) + model16 = null; + if (!model16) + model16 = await loadModel(config3.face["insightface"].modelPath); + else if (config3.debug) + log("cached model:", model16["modelUrl"]); + return model16; +} +async function predict13(input, config3, idx, count2) { + var _a, _b; + if (!(model16 == null ? void 0 : model16["executor"])) + return []; + const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; + if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { + skipped12++; + return last9[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { + const t2 = {}; + t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); + t2.data = model16.execute(t2.crop); + const output = await t2.data.data(); + data = Array.from(output); + Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); + } + last9[idx] = data; + lastCount9 = count2; + lastTime12 = now(); + resolve(data); + }); +} + +// src/face/angles.ts +var calculateGaze = (face4) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); + const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; + const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; + const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); + strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face4, imageSize) => { + const normalize2 = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + if (Number.isNaN(thetaX)) + thetaX = 0; + if (Number.isNaN(thetaY)) + thetaY = 0; + if (Number.isNaN(thetaZ)) + thetaZ = 0; + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const mesh = face4.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); + const yAxis = normalize2(subVectors(pts[1], pts[0])); + let xAxis = normalize2(subVectors(pts[3], pts[2])); + const zAxis = normalize2(crossVectors(xAxis, yAxis)); + xAxis = crossVectors(yAxis, zAxis); + const matrix = [ + xAxis[0], + xAxis[1], + xAxis[2], + yAxis[0], + yAxis[1], + yAxis[2], + zAxis[0], + zAxis[1], + zAxis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; + +// src/face/anthropometry.ts +function calculateCameraDistance(face4, width) { + const f = face4 == null ? void 0 : face4.annotations; + if (!f) + return 0; + const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; + const cameraDistance = Math.round(1.17 / irisSize) / 100; + return cameraDistance; +} + +// src/face/face.ts +var detectFace = async (instance, input) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + let timeStamp = now(); + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let mobilefacenetRes; + let insightfaceRes; + let antispoofRes; + let livenessRes; + let descRes; + const faceRes = []; + instance.state = "run:face"; + const faces = await predict4(input, instance.config); + instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + instance.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { + const masked = await mask(faces[i]); + tf25.dispose(faces[i].tensor); + if (masked) + faces[i].tensor = masked; + } + const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; + instance.analyze("Start Emotion:"); + if (instance.config.async) { + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + } else { + instance.state = "run:emotion"; + timeStamp = now(); + emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; + instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Emotion:"); + instance.analyze("Start AntiSpoof:"); + if (instance.config.async) { + antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:antispoof"; + timeStamp = now(); + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End AntiSpoof:"); + instance.analyze("Start Liveness:"); + if (instance.config.async) { + livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:liveness"; + timeStamp = now(); + livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Liveness:"); + instance.analyze("Start GEAR:"); + if (instance.config.async) { + gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:gear"; + timeStamp = now(); + gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.gear = Math.trunc(now() - timeStamp); + } + instance.analyze("End GEAR:"); + instance.analyze("Start SSRNet:"); + if (instance.config.async) { + ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:ssrnet"; + timeStamp = now(); + ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.ssrnet = Math.trunc(now() - timeStamp); + } + instance.analyze("End SSRNet:"); + instance.analyze("Start MobileFaceNet:"); + if (instance.config.async) { + mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End MobileFaceNet:"); + instance.analyze("Start InsightFace:"); + if (instance.config.async) { + insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End InsightFace:"); + instance.analyze("Start Description:"); + if (instance.config.async) { + descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + } else { + instance.state = "run:description"; + timeStamp = now(); + descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); + instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Description:"); + if (instance.config.async) { + [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); + } + instance.analyze("Finish Face:"); + if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { + descRes = { + ...descRes, + age: ageRes.age, + gender: genderRes.gender, + genderScore: genderRes.genderScore + }; + } + if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { + descRes = { + ...descRes, + age: gearRes.age, + gender: gearRes.gender, + genderScore: gearRes.genderScore, + race: gearRes.race + }; + } + if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { + descRes.descriptor = mobilefacenetRes; + } + if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { + descRes.descriptor = insightfaceRes; + } + const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; + const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; + tf25.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + const res = { + ...faces[i], + id: i + }; + if (descRes.age) + res.age = descRes.age; + if (descRes.gender) + res.gender = descRes.gender; + if (descRes.genderScore) + res.genderScore = descRes.genderScore; + if (descRes.descriptor) + res.embedding = descRes.descriptor; + if (descRes.race) + res.race = descRes.race; + if (emotionRes) + res.emotion = emotionRes; + if (antispoofRes) + res.real = antispoofRes; + if (livenessRes) + res.live = livenessRes; + if (irisSize > 0) + res.distance = irisSize; + if (rotation) + res.rotation = rotation; + if (tensor6) + res.tensor = tensor6; + faceRes.push(res); + instance.analyze("End Face"); + } + instance.analyze("End FaceMesh:"); + if (instance.config.async) { + if (instance.performance.face) + delete instance.performance.face; + if (instance.performance.age) + delete instance.performance.age; + if (instance.performance.gender) + delete instance.performance.gender; + if (instance.performance.emotion) + delete instance.performance.emotion; + } + return faceRes; +}; + +// src/hand/fingerdef.ts +var Finger = { + thumb: 0, + index: 1, + middle: 2, + ring: 3, + pinky: 4, + all: [0, 1, 2, 3, 4], + nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, + pointsMapping: { + 0: [[0, 1], [1, 2], [2, 3], [3, 4]], + 1: [[0, 5], [5, 6], [6, 7], [7, 8]], + 2: [[0, 9], [9, 10], [10, 11], [11, 12]], + 3: [[0, 13], [13, 14], [14, 15], [15, 16]], + 4: [[0, 17], [17, 18], [18, 19], [19, 20]] + }, + getName: (value) => Finger.nameMapping[value], + getPoints: (value) => Finger.pointsMapping[value] +}; +var FingerCurl = { + none: 0, + half: 1, + full: 2, + nameMapping: { 0: "none", 1: "half", 2: "full" }, + getName: (value) => FingerCurl.nameMapping[value] +}; +var FingerDirection = { + verticalUp: 0, + verticalDown: 1, + horizontalLeft: 2, + horizontalRight: 3, + diagonalUpRight: 4, + diagonalUpLeft: 5, + diagonalDownRight: 6, + diagonalDownLeft: 7, + nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, + getName: (value) => FingerDirection.nameMapping[value] +}; +var FingerGesture = class { + constructor(name) { + __publicField(this, "name"); + __publicField(this, "curls"); + __publicField(this, "directions"); + __publicField(this, "weights"); + __publicField(this, "weightsRelative"); + this.name = name; + this.curls = {}; + this.directions = {}; + this.weights = [1, 1, 1, 1, 1]; + this.weightsRelative = [1, 1, 1, 1, 1]; + } + curl(finger, curl, confidence) { + if (typeof this.curls[finger] === "undefined") + this.curls[finger] = []; + this.curls[finger].push([curl, confidence]); + } + direction(finger, position, confidence) { + if (!this.directions[finger]) + this.directions[finger] = []; + this.directions[finger].push([position, confidence]); + } + weight(finger, weight) { + this.weights[finger] = weight; + const total = this.weights.reduce((a, b) => a + b, 0); + this.weightsRelative = this.weights.map((el) => el * 5 / total); + } + matchAgainst(detectedCurls, detectedDirections) { + let confidence = 0; + for (const fingerIdx in detectedCurls) { + const detectedCurl = detectedCurls[fingerIdx]; + const expectedCurls = this.curls[fingerIdx]; + if (typeof expectedCurls === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedCurl, score] of expectedCurls) { + if (detectedCurl === expectedCurl) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + for (const fingerIdx in detectedDirections) { + const detectedDirection = detectedDirections[fingerIdx]; + const expectedDirections = this.directions[fingerIdx]; + if (typeof expectedDirections === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedDirection, score] of expectedDirections) { + if (detectedDirection === expectedDirection) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + return confidence / 10; + } +}; + +// src/hand/fingergesture.ts +var { thumb, index, middle, ring, pinky } = Finger; +var { none, half, full } = FingerCurl; +var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; +var ThumbsUp = new FingerGesture("thumbs up"); +ThumbsUp.curl(thumb, none, 1); +ThumbsUp.direction(thumb, verticalUp, 1); +ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); +ThumbsUp.direction(thumb, diagonalUpRight, 0.25); +for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { + ThumbsUp.curl(finger, full, 1); + ThumbsUp.direction(finger, horizontalLeft, 1); + ThumbsUp.direction(finger, horizontalRight, 1); +} +var Victory = new FingerGesture("victory"); +Victory.curl(thumb, half, 0.5); +Victory.curl(thumb, none, 0.5); +Victory.direction(thumb, verticalUp, 1); +Victory.direction(thumb, diagonalUpLeft, 1); +Victory.curl(index, none, 1); +Victory.direction(index, verticalUp, 0.75); +Victory.direction(index, diagonalUpLeft, 1); +Victory.curl(middle, none, 1); +Victory.direction(middle, verticalUp, 1); +Victory.direction(middle, diagonalUpLeft, 0.75); +Victory.curl(ring, full, 1); +Victory.direction(ring, verticalUp, 0.2); +Victory.direction(ring, diagonalUpLeft, 1); +Victory.direction(ring, horizontalLeft, 0.2); +Victory.curl(pinky, full, 1); +Victory.direction(pinky, verticalUp, 0.2); +Victory.direction(pinky, diagonalUpLeft, 1); +Victory.direction(pinky, horizontalLeft, 0.2); +Victory.weight(index, 2); +Victory.weight(middle, 2); +var Point = new FingerGesture("point"); +Point.curl(thumb, full, 1); +Point.curl(index, none, 0.5); +Point.curl(middle, full, 0.5); +Point.curl(ring, full, 0.5); +Point.curl(pinky, full, 0.5); +Point.weight(index, 2); +Point.weight(middle, 2); +var MiddleFinger = new FingerGesture("middle finger"); +MiddleFinger.curl(thumb, none, 1); +MiddleFinger.curl(index, full, 0.5); +MiddleFinger.curl(middle, full, 0.5); +MiddleFinger.curl(ring, full, 0.5); +MiddleFinger.curl(pinky, full, 0.5); +MiddleFinger.weight(index, 2); +MiddleFinger.weight(middle, 2); +var OpenPalm = new FingerGesture("open palm"); +OpenPalm.curl(thumb, none, 0.75); +OpenPalm.curl(index, none, 0.75); +OpenPalm.curl(middle, none, 0.75); +OpenPalm.curl(ring, none, 0.75); +OpenPalm.curl(pinky, none, 0.75); +var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; + +// src/hand/fingerpose.ts +var minConfidence = 0.7; +var options3 = { + HALF_CURL_START_LIMIT: 60, + NO_CURL_START_LIMIT: 130, + DISTANCE_VOTE_POWER: 1.1, + SINGLE_ANGLE_VOTE_POWER: 0.9, + TOTAL_ANGLE_VOTE_POWER: 1.6 +}; +function calculateSlope(point1x, point1y, point2x, point2y) { + const value = (point1y - point2y) / (point1x - point2x); + let slope = Math.atan(value) * 180 / Math.PI; + if (slope <= 0) + slope = -slope; + else if (slope > 0) + slope = 180 - slope; + return slope; +} +function getSlopes(point1, point2) { + if (!point1 || !point2) + return [0, 0]; + const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); + if (point1.length === 2) + return slopeXY; + const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); + return [slopeXY, slopeYZ]; +} +function angleOrientationAt(angle, weightageAt = 1) { + let isVertical = 0; + let isDiagonal = 0; + let isHorizontal = 0; + if (angle >= 75 && angle <= 105) + isVertical = 1 * weightageAt; + else if (angle >= 25 && angle <= 155) + isDiagonal = 1 * weightageAt; + else + isHorizontal = 1 * weightageAt; + return [isVertical, isDiagonal, isHorizontal]; +} +function estimateFingerCurl(startPoint, midPoint, endPoint) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const start_mid_z_dist = startPoint[2] - midPoint[2]; + const start_end_z_dist = startPoint[2] - endPoint[2]; + const mid_end_z_dist = midPoint[2] - endPoint[2]; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); + let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); + if (cos_in > 1) + cos_in = 1; + else if (cos_in < -1) + cos_in = -1; + let angleOfCurve = Math.acos(cos_in); + angleOfCurve = 57.2958 * angleOfCurve % 180; + let fingerCurl; + if (angleOfCurve > options3.NO_CURL_START_LIMIT) + fingerCurl = FingerCurl.none; + else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) + fingerCurl = FingerCurl.half; + else + fingerCurl = FingerCurl.full; + return fingerCurl; +} +function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + if (max_dist_x === Math.abs(start_end_x_dist)) { + if (start_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else if (max_dist_x === Math.abs(start_mid_x_dist)) { + if (start_mid_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else { + if (mid_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } + return estimatedDirection; +} +function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { + let estimatedDirection; + if (max_dist_y === Math.abs(start_end_y_dist)) { + if (start_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else if (max_dist_y === Math.abs(start_mid_y_dist)) { + if (start_mid_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else { + if (mid_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } + return estimatedDirection; +} +function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + if (reqd_vertical_direction === FingerDirection.verticalUp) { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalUpLeft; + else + estimatedDirection = FingerDirection.diagonalUpRight; + } else { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalDownLeft; + else + estimatedDirection = FingerDirection.diagonalDownRight; + } + return estimatedDirection; +} +function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); + const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); + let voteVertical = 0; + let voteDiagonal = 0; + let voteHorizontal = 0; + const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); + if (start_end_x_y_dist_ratio > 1.5) + voteVertical += options3.DISTANCE_VOTE_POWER; + else if (start_end_x_y_dist_ratio > 0.66) + voteDiagonal += options3.DISTANCE_VOTE_POWER; + else + voteHorizontal += options3.DISTANCE_VOTE_POWER; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); + const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); + let calc_start_point_x = startPoint[0]; + let calc_start_point_y = startPoint[1]; + let calc_end_point_x = endPoint[0]; + let calc_end_point_y = endPoint[1]; + if (max_dist === start_mid_dist) { + calc_end_point_x = endPoint[0]; + calc_end_point_y = endPoint[1]; + } else if (max_dist === mid_end_dist) { + calc_start_point_x = midPoint[0]; + calc_start_point_y = midPoint[1]; + } + const calcStartPoint = [calc_start_point_x, calc_start_point_y]; + const calcEndPoint = [calc_end_point_x, calc_end_point_y]; + const totalAngle = getSlopes(calcStartPoint, calcEndPoint); + const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); + voteVertical += votes[0]; + voteDiagonal += votes[1]; + voteHorizontal += votes[2]; + for (const fingerSlope of fingerSlopes) { + const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); + voteVertical += fingerVotes[0]; + voteDiagonal += fingerVotes[1]; + voteHorizontal += fingerVotes[2]; + } + let estimatedDirection; + if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } else { + estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } + return estimatedDirection; +} +function estimate(landmarks) { + const slopesXY = []; + const slopesYZ = []; + const fingerCurls = []; + const fingerDirections = []; + if (!landmarks) + return { curls: fingerCurls, directions: fingerDirections }; + for (const finger of Finger.all) { + const points = Finger.getPoints(finger); + const slopeAtXY = []; + const slopeAtYZ = []; + for (const point2 of points) { + const point1 = landmarks[point2[0]]; + const point22 = landmarks[point2[1]]; + const slopes = getSlopes(point1, point22); + const slopeXY = slopes[0]; + const slopeYZ = slopes[1]; + slopeAtXY.push(slopeXY); + slopeAtYZ.push(slopeYZ); + } + slopesXY.push(slopeAtXY); + slopesYZ.push(slopeAtYZ); + } + for (const finger of Finger.all) { + const pointIndexAt = finger === Finger.thumb ? 1 : 0; + const fingerPointsAt = Finger.getPoints(finger); + const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; + const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; + const endPoint = landmarks[fingerPointsAt[3][1]]; + const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); + const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); + fingerCurls[finger] = fingerCurled; + fingerDirections[finger] = fingerPosition; + } + return { curls: fingerCurls, directions: fingerDirections }; +} +function analyze(keypoints) { + if (!keypoints || keypoints.length === 0) + return null; + const estimatorRes = estimate(keypoints); + const landmarks = {}; + for (const fingerIdx of Finger.all) { + landmarks[Finger.getName(fingerIdx)] = { + curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), + direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) + }; + } + return landmarks; +} +function match(keypoints) { + const poses = []; + if (!keypoints || keypoints.length === 0) + return poses; + const estimatorRes = estimate(keypoints); + for (const gesture2 of fingergesture_default) { + const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); + if (confidence >= minConfidence) + poses.push({ name: gesture2.name, confidence }); + } + return poses; +} + +// src/gesture/gesture.ts +var body2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); + } + } + return gestures; +}; +var face2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 450) { + const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); + const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; + if (Math.abs(zDiff / xDiff) <= 0.15) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); + const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2] || 0; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris2 = (res) => { + var _a, _b, _c, _d; + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > rightIrisCenterX) { + if (leftIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking right" }); + } else { + if (rightIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking left" }); + } + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + if (res[i].annotations) { + for (const [finger, pos] of Object.entries(res[i].annotations)) { + if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + if (res[i].keypoints) { + const poses = match(res[i].keypoints); + for (const pose of poses) + gestures.push({ hand: i, gesture: pose.name }); + } + } + return gestures; +}; + +// src/hand/handposedetector.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/hand/handposeutil.ts +var tf26 = __toESM(require_tfjs_esm()); +function getBoxSize2(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; +} +function getBoxCenter2(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box, image28, cropSize) { + const h = image28.shape[1]; + const w = image28.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tf26.image.cropAndResize(image28, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; +} +function enlargeBox2(box, factor = 1.5) { + const center = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function squarifyBox2(box) { + const centers = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const maxEdge = Math.max(...size2); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/hand/handposeanchors.ts +var anchors2 = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 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+ { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/hand/handposedetector.ts +var HandDetector = class { + constructor(model23) { + __publicField(this, "model"); + __publicField(this, "anchors"); + __publicField(this, "anchorsTensor"); + __publicField(this, "inputSize"); + __publicField(this, "inputSizeTensor"); + __publicField(this, "doubleInputSizeTensor"); + var _a, _b, _c, _d; + this.model = model23; + this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tf27.tensor2d(this.anchors); + this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; + this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + const t2 = {}; + t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); + t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); + t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); + t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); + t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); + t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); + t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); + t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); + t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); + const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + normalizeLandmarks(rawPalmLandmarks, index2) { + const t2 = {}; + t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); + t2.div = tf27.div(t2.reshape, this.inputSizeTensor); + t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); + const res = tf27.mul(t2.landmarks, this.inputSizeTensor); + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return res; + } + async predict(input, config3) { + var _a; + const t2 = {}; + t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); + t2.div = tf27.div(t2.resize, constants.tf127); + t2.image = tf27.sub(t2.div, constants.tf1); + t2.batched = this.model.execute(t2.image); + t2.predictions = tf27.squeeze(t2.batched); + t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); + t2.sigmoid = tf27.sigmoid(t2.slice); + t2.scores = tf27.squeeze(t2.sigmoid); + const scores = await t2.scores.data(); + t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); + t2.norm = this.normalizeBoxes(t2.boxes); + t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t2.nms.array(); + const hands = []; + for (const index2 of nms) { + const p = {}; + p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); + p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); + p.norm = this.normalizeLandmarks(p.slice, index2); + p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); + const box = await p.box.data(); + const startPoint = box.slice(0, 2); + const endPoint = box.slice(2, 4); + const palmLandmarks = await p.palmLandmarks.array(); + const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; + const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); + hands.push(scaled); + Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); + } + Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); + return hands; + } +}; + +// src/hand/handposepipeline.ts +var tf28 = __toESM(require_tfjs_esm()); +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var lastTime13 = 0; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + __publicField(this, "handDetector"); + __publicField(this, "handPoseModel"); + __publicField(this, "inputSize"); + __publicField(this, "storedBoxes"); + __publicField(this, "skipped"); + __publicField(this, "detectedHands"); + var _a, _b, _c; + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; + this.storedBoxes = []; + this.skipped = Number.MAX_SAFE_INTEGER; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box2, angle, rotationMatrix) { + const boxSize = getBoxSize2(box2); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box2), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image28, config3) { + let useFreshBox = false; + let boxes; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; + const skipFrame = this.skipped < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + boxes = await this.handDetector.predict(image28, config3); + this.skipped = 0; + } + if (config3.skipAllowed) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tf28.div(croppedInput, constants.tf255); + tf28.dispose(croppedInput); + tf28.dispose(rotatedImage); + const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); + lastTime13 = now(); + tf28.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tf28.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence / 4) { + const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tf28.dispose(keypoints); + tf28.dispose(keypointsReshaped); + const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords, + confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tf28.dispose(keypoints); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: 0, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, + landmarks: [] + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + if (hands.length > config3.hand.maxDetected) + hands.length = config3.hand.maxDetected; + return hands; + } +}; + +// src/hand/handpose.ts +var meshAnnotations2 = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palm: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict14(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations2 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations2)) { + annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); + } + } + const keypoints = predictions[i].landmarks; + let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw = [0, 0, 0, 0]; + if (keypoints && keypoints.length > 0) { + for (const pt of keypoints) { + if (pt[0] < box[0]) + box[0] = pt[0]; + if (pt[1] < box[1]) + box[1] = pt[1]; + if (pt[0] > box[2]) + box[2] = pt[0]; + if (pt[1] > box[3]) + box[3] = pt[1]; + } + box[2] -= box[0]; + box[3] -= box[1]; + boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; + } else { + box = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + const landmarks = analyze(keypoints); + hands.push({ + id: i, + score: Math.round(100 * predictions[i].confidence) / 100, + boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, + fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, + label: "hand", + box, + boxRaw, + keypoints, + annotations: annotations2, + landmarks + }); + } + return hands; +} +async function load15(config3) { + var _a, _b; + if (env.initial) { + handDetectorModel = null; + handPoseModel = null; + } + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, + config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null + ]); + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; + if (handDetector && handPoseModel) + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/hand/handtrack.ts +var tf29 = __toESM(require_tfjs_esm()); +var models2 = [null, null]; +var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; +var inputSize7 = [[0, 0], [0, 0]]; +var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; +var faceIndex = 4; +var boxExpandFact = 1.6; +var maxDetectorResolution = 512; +var detectorExpandFact = 1.4; +var skipped13 = Number.MAX_SAFE_INTEGER; +var lastTime14 = 0; +var outputSize = [0, 0]; +var cache4 = { + boxes: [], + hands: [] +}; +var fingerMap = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + base: [0], + palm: [0, 17, 13, 9, 5, 1, 0] +}; +async function loadDetect2(config3) { + var _a; + if (env.initial) + models2[0] = null; + if (!models2[0]) { + fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); + models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); + const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; + inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[0]["modelUrl"]); + return models2[0]; +} +async function loadSkeleton(config3) { + var _a; + if (env.initial) + models2[1] = null; + if (!models2[1]) { + models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); + const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; + inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[1]["modelUrl"]); + return models2[1]; +} +async function detectHands(input, config3) { + const hands = []; + if (!input || !models2[0]) + return hands; + const t2 = {}; + const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); + const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); + const width = Math.round(height * ratio2 / 8) * 8; + t2.resize = tf29.image.resizeBilinear(input, [height, width]); + t2.cast = tf29.cast(t2.resize, "int32"); + [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); + t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); + t2.scores = tf29.squeeze(t2.rawScores, [0]); + const classScores = tf29.unstack(t2.scores, 1); + tf29.dispose(classScores[faceIndex]); + classScores.splice(faceIndex, 1); + t2.filtered = tf29.stack(classScores, 1); + tf29.dispose(classScores); + t2.max = tf29.max(t2.filtered, 1); + t2.argmax = tf29.argMax(t2.filtered, 1); + let id = 0; + t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t2.nms.data(); + const scores = await t2.max.data(); + const classNum = await t2.argmax.data(); + for (const nmsIndex of Array.from(nms)) { + const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); + const boxYX = await boxSlice.data(); + tf29.dispose(boxSlice); + const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; + const boxRaw = scale(boxData, detectorExpandFact); + const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; + const score = scores[nmsIndex]; + const label = classes[classNum[nmsIndex]]; + const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; + hands.push(hand3); + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + hands.sort((a, b) => b.score - a.score); + if (hands.length > (config3.hand.maxDetected || 1)) + hands.length = config3.hand.maxDetected || 1; + return hands; +} +async function detectFingers(input, h, config3) { + const hand3 = { + id: h.id, + score: Math.round(100 * h.score) / 100, + boxScore: Math.round(100 * h.score) / 100, + fingerScore: 0, + box: h.box, + boxRaw: h.boxRaw, + label: h.label, + keypoints: [], + landmarks: {}, + annotations: {} + }; + if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { + const t2 = {}; + const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; + t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t2.div = tf29.div(t2.crop, constants.tf255); + [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); + const rawScore = (await t2.score.data())[0]; + const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; + if (score >= (config3.hand.minConfidence || 0)) { + hand3.fingerScore = score; + t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); + const coordsData = await t2.reshaped.array(); + const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); + const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); + hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); + hand3.landmarks = analyze(hand3.keypoints); + for (const key of Object.keys(fingerMap)) { + hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); + } + } + Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); + } + return hand3; +} +async function predict15(input, config3) { + var _a, _b; + if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) + return []; + outputSize = [input.shape[2] || 0, input.shape[1] || 0]; + skipped13++; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrame = skipped13 < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache4.hands; + } + return new Promise(async (resolve) => { + const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; + const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); + if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else { + cache4.boxes = await detectHands(input, config3); + lastTime14 = now(); + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + skipped13 = 0; + } + const oldCache = [...cache4.boxes]; + cache4.boxes.length = 0; + if (config3.cacheSensitivity > 0) { + for (let i = 0; i < cache4.hands.length; i++) { + const boxKpt = square(cache4.hands[i].keypoints, outputSize); + if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { + const boxScale = scale(boxKpt.box, boxExpandFact); + const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); + cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); + } + } + } + for (let i = 0; i < cache4.hands.length; i++) { + const bbox = calc(cache4.hands[i].keypoints, outputSize); + cache4.hands[i].box = bbox.box; + cache4.hands[i].boxRaw = bbox.boxRaw; + } + resolve(cache4.hands); + }); +} + +// src/result.ts +var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); + +// src/body/movenetcoords.ts +var movenetcoords_exports = {}; +__export(movenetcoords_exports, { + connected: () => connected3, + horizontal: () => horizontal, + kpt: () => kpt3, + relative: () => relative, + vertical: () => vertical +}); +var kpt3 = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var horizontal = [ + ["leftEye", "rightEye"], + ["leftEar", "rightEar"], + ["leftShoulder", "rightShoulder"], + ["leftElbow", "rightElbow"], + ["leftWrist", "rightWrist"], + ["leftHip", "rightHip"], + ["leftKnee", "rightKnee"], + ["leftAnkle", "rightAnkle"] +]; +var vertical = [ + ["leftKnee", "leftShoulder"], + ["rightKnee", "rightShoulder"], + ["leftAnkle", "leftKnee"], + ["rightAnkle", "rightKnee"] +]; +var relative = [ + [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], + [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] +]; +var connected3 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/util/interpolate.ts +var bufferedResult = empty(); +var interpolateTime = 0; +function calc2(newResult, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; + const t0 = now(); + if (!newResult) + return empty(); + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; + if (newResult.canvas) + bufferedResult.canvas = newResult.canvas; + if (newResult.error) + bufferedResult.error = newResult.error; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { + var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + return { + score: newKpt.score, + part: newKpt.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + ], + distance: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + ] + }; + }); + const annotations2 = {}; + let coords = { connected: {} }; + if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) + coords = efficientposecoords_exports; + else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + coords = blazeposecoords_exports; + else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) + coords = movenetcoords_exports; + for (const [name, indexes] of Object.entries(coords.connected)) { + const pt = []; + for (let j = 0; j < indexes.length - 1; j++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[j]); + const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) + bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + let annotations2 = {}; + if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { + bufferedResult.hand[i].annotations = newResult.hand[i].annotations; + annotations2 = bufferedResult.hand[i].annotations; + } else if (newResult.hand[i].annotations) { + for (const key of Object.keys(newResult.hand[i].annotations)) { + annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + } + } + bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + if (newResult.face[i].rotation) { + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; + } else { + bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; + } + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + bufferedResult.width = newResult.width; + bufferedResult.height = newResult.height; + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); + if (newResult.performance) + bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; + return bufferedResult; +} + +// src/segmentation/meet.ts +var tf30 = __toESM(require_tfjs_esm()); +var model17; +async function load16(config3) { + if (!model17 || env.initial) + model17 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model17["modelUrl"]); + return model17; +} +async function predict16(input, config3) { + var _a; + if (!model17) + model17 = await load16(config3); + if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + t2.norm = tf30.div(t2.resize, constants.tf255); + t2.res = model17.execute(t2.norm); + t2.squeeze = tf30.squeeze(t2.res, [0]); + [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); + t2.fg = tf30.softmax(t2.fgRaw); + t2.mul = tf30.mul(t2.fg, constants.tf255); + t2.expand = tf30.expandDims(t2.mul, 2); + t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf30.squeeze(input); + t2.concat = tf30.concat([t2.input, t2.output], -1); + rgba = tf30.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf30.cast(t2.output, "int32"); + break; + default: + rgba = tf30.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); + return rgba; +} + +// src/face/match.ts +var match_exports = {}; +__export(match_exports, { + distance: () => distance, + find: () => find, + similarity: () => similarity +}); +function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { + if (!descriptor1 || !descriptor1) + return Number.MAX_SAFE_INTEGER; + let sum3 = 0; + for (let i = 0; i < descriptor1.length; i++) { + const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); + sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; + } + return (options4.multiplier || 20) * sum3; +} +var normalizeDistance = (dist, order, min2, max5) => { + if (dist === 0) + return 1; + const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); + const norm = (1 - root / 100 - min2) / (max5 - min2); + const clamp2 = Math.max(Math.min(norm, 1), 0); + return clamp2; +}; +function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { + const dist = distance(descriptor1, descriptor2, options4); + return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); +} +function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { + if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { + return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; + } + let lowestDistance = Number.MAX_SAFE_INTEGER; + let index2 = -1; + for (let i = 0; i < descriptors.length; i++) { + const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; + if (res < lowestDistance) { + lowestDistance = res; + index2 = i; + } + if (lowestDistance < (options4.threshold || 0)) + break; + } + const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); + return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; +} + +// src/models.ts +var models_exports2 = {}; +__export(models_exports2, { + Models: () => Models, + validateModel: () => validateModel +}); + +// src/body/movenet.ts +var tf32 = __toESM(require_tfjs_esm()); + +// src/body/movenetfix.ts +var tf31 = __toESM(require_tfjs_esm()); +var maxJitter = 5e-3; +var cache5 = { + keypoints: [], + padding: [[0, 0], [0, 0], [0, 0], [0, 0]] +}; +function bodyParts(body4) { + for (const pair of horizontal) { + const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); + if (body4.keypoints[left] && body4.keypoints[right]) { + if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } + } + for (const pair of vertical) { + const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + if (body4.keypoints[lower] && body4.keypoints[higher]) { + if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { + body4.keypoints.splice(lower, 1); + } + } + } + for (const [pair, compare2] of relative) { + const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); + const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); + if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) + continue; + const distanceLeft = body4.keypoints[left] ? [ + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) + ] : [0, 0]; + const distanceRight = body4.keypoints[right] ? [ + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) + ] : [0, 0]; + if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } +} +function jitter(keypoints) { + for (let i = 0; i < keypoints.length; i++) { + if (keypoints[i] && cache5.keypoints[i]) { + const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; + if (diff[0] < maxJitter && diff[1] < maxJitter) { + keypoints[i] = cache5.keypoints[i]; + } else { + cache5.keypoints[i] = keypoints[i]; + } + } else { + cache5.keypoints[i] = keypoints[i]; + } + } + return keypoints; +} +function padInput(input, inputSize10) { + var _a, _b; + const t2 = {}; + if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) + return input; + cache5.padding = [ + [0, 0], + [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], + [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], + [0, 0] + ]; + t2.pad = tf31.pad(input, cache5.padding); + t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); + const final = tf31.cast(t2.resize, "int32"); + Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); + return final; +} +function rescaleBody(body4, outputSize2) { + body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); + for (const kpt4 of body4.keypoints) { + kpt4.position = [ + kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], + kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] + ]; + kpt4.positionRaw = [ + kpt4.position[0] / outputSize2[0], + kpt4.position[1] / outputSize2[1] + ]; + } + const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); + body4.box = rescaledBoxes.box; + body4.boxRaw = rescaledBoxes.boxRaw; + return body4; +} + +// src/body/movenet.ts +var model18; +var inputSize8 = 0; +var skipped14 = Number.MAX_SAFE_INTEGER; +var cache6 = { + boxes: [], + bodies: [], + last: 0 +}; +async function load17(config3) { + var _a; + if (env.initial) + model18 = null; + if (!model18) { + fakeOps(["size"], config3); + model18 = await loadModel(config3.body.modelPath); + } else if (config3.debug) + log("cached model:", model18["modelUrl"]); + inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; + if (inputSize8 < 64) + inputSize8 = 256; + return model18; +} +function parseSinglePose(res, config3, image28) { + const kpt4 = res[0][0]; + const keypoints = []; + let score = 0; + for (let id = 0; id < kpt4.length; id++) { + score = kpt4[id][2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[id][1], kpt4[id][0]]; + keypoints.push({ + score: Math.round(100 * score) / 100, + part: kpt3[id], + positionRaw, + position: [ + Math.round((image28.shape[2] || 0) * positionRaw[0]), + Math.round((image28.shape[1] || 0) * positionRaw[1]) + ] + }); + } + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const bodies = []; + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + return bodies; +} +function parseMultiPose(res, config3, image28) { + const bodies = []; + for (let id = 0; id < res[0].length; id++) { + const kpt4 = res[0][id]; + const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; + if (totalScore > config3.body.minConfidence) { + const keypoints = []; + for (let i = 0; i < 17; i++) { + const score = kpt4[3 * i + 2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; + keypoints.push({ + part: kpt3[i], + score: Math.round(100 * score) / 100, + positionRaw, + position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] + }); + } + } + const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + } + } + bodies.sort((a, b) => b.score - a.score); + if (bodies.length > config3.body.maxDetected) + bodies.length = config3.body.maxDetected; + return bodies; +} +async function predict17(input, config3) { + var _a; + if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) + return []; + if (!config3.skipAllowed) + cache6.boxes.length = 0; + skipped14++; + const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; + const skipFrame = skipped14 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache6.bodies; + } + return new Promise(async (resolve) => { + const t2 = {}; + skipped14 = 0; + t2.input = padInput(input, inputSize8); + t2.res = model18 == null ? void 0 : model18.execute(t2.input); + cache6.last = now(); + const res = await t2.res.array(); + cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); + for (const body4 of cache6.bodies) { + rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); + jitter(body4.keypoints); + } + Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); + resolve(cache6.bodies); + }); +} + +// src/object/nanodet.ts +var tf33 = __toESM(require_tfjs_esm()); +var model19; +var last10 = []; +var lastTime15 = 0; +var skipped15 = Number.MAX_SAFE_INTEGER; +var inputSize9 = 0; +var scaleBox = 2.5; +async function load18(config3) { + if (!model19 || env.initial) { + model19 = await loadModel(config3.object.modelPath); + const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; + inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; + } else if (config3.debug) + log("cached model:", model19["modelUrl"]); + return model19; +} +async function process4(res, outputShape, config3) { + var _a, _b; + let id = 0; + let results = []; + const size2 = inputSize9; + for (const strideSize of [1, 2, 4]) { + const baseSize = strideSize * 13; + const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scores = await scoresT.array(); + const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); + const boxIdxT = tf33.argMax(boxesMaxT, 2); + const boxIdx = await boxIdxT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { + const score = scores[i][j]; + if (score > (config3.object.minConfidence || 0) && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw = [x, y, w, h]; + boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); + const box = [ + boxRaw[0] * outputShape[0], + boxRaw[1] * outputShape[1], + boxRaw[2] * outputShape[0], + boxRaw[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score) / 100, + class: j + 1, + label: labels2[j].label, + box: box.map((a) => Math.trunc(a)), + boxRaw + }; + results.push(result); + } + } + } + tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); + } + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = Array.from(await nms.data()); + tf33.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict18(image28, config3) { + if (!(model19 == null ? void 0 : model19["executor"])) + return []; + const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; + const skipFrame = skipped15 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { + skipped15++; + return last10; + } + skipped15 = 0; + if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) + return last10; + return new Promise(async (resolve) => { + const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; + const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); + const normT = tf33.div(resizeT, constants.tf255); + const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); + let objectT; + if (config3.object.enabled) + objectT = model19.execute(transposeT); + lastTime15 = now(); + const obj = await process4(objectT, outputSize2, config3); + last10 = obj; + tf33.dispose([resizeT, normT, transposeT, ...objectT]); + resolve(obj); + }); +} + +// src/body/posenet.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/body/posenetutils.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; +function getBoundingBox(keypoints) { + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score, part, position }) => ({ + score, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })), + annotations: {} + }); + const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + __publicField(this, "priorityQueue"); + __publicField(this, "numberOfElements"); + __publicField(this, "getElementValue"); + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max5 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max5; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t2 = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t2; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min2, max5) { + if (a < min2) + return min2; + if (a > max5) + return max5; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/body/posenet.ts +var model20; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors( + { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, + { x: offsetPoint.x, y: offsetPoint.y } + ); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints; +} +function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence2, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < minConfidence2) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) + queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses, { x, y }, keypointId) { + return poses.some(({ keypoints }) => { + var _a; + const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints) { + const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { + const poses = []; + const queue = buildPartWithScoreQueue(minConfidence2, scores); + while (poses.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses, rootImageCoords, root.part.id)) + continue; + let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints = keypoints.filter((a) => a.score > minConfidence2); + const score = getInstanceScore(poses, keypoints); + const box = getBoundingBox(keypoints); + if (score > minConfidence2) + poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); + } + return poses; +} +async function predict19(input, config3) { + if (!(model20 == null ? void 0 : model20["executor"])) + return []; + const res = tf34.tidy(() => { + if (!model20.inputs[0].shape) + return []; + const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); + const results = model20.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tf34.squeeze(y, [0])); + results3d[1] = tf34.sigmoid(results3d[1]); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); + for (const t2 of res) + tf34.dispose(t2); + const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model20.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); + return scaled; +} +async function load19(config3) { + if (!model20 || env.initial) + model20 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model20["modelUrl"]); + return model20; +} + +// src/segmentation/rvm.ts +var tf35 = __toESM(require_tfjs_esm()); +var model21; +var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; +var t = {}; +var ratio = 0; +function init3(config3) { + tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + t.r1i = tf35.tensor(0); + t.r2i = tf35.tensor(0); + t.r3i = tf35.tensor(0); + t.r4i = tf35.tensor(0); + ratio = config3.segmentation.ratio || 0.5; + t.downsample_ratio = tf35.tensor(ratio); +} +async function load20(config3) { + if (!model21 || env.initial) + model21 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model21["modelUrl"]); + init3(config3); + return model21; +} +var normalize = (r) => tf35.tidy(() => { + const squeeze14 = tf35.squeeze(r, [0]); + const mul15 = tf35.mul(squeeze14, constants.tf255); + const cast8 = tf35.cast(mul15, "int32"); + return cast8; +}); +function getRGBA(fgr, pha) { + const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); + const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); + const rgba = tf35.concat([rgb2, a], -1); + tf35.dispose([rgb2, a]); + return rgba; +} +function getState(state) { + return tf35.tidy(() => { + const r = {}; + r.unstack = tf35.unstack(state, -1); + r.concat = tf35.concat(r.unstack, 1); + r.split = tf35.split(r.concat, 4, 1); + r.stack = tf35.concat(r.split, 2); + r.squeeze = tf35.squeeze(r.stack, [0]); + r.expand = tf35.expandDims(r.squeeze, -1); + r.add = tf35.add(r.expand, 1); + r.mul = tf35.mul(r.add, 127.5); + r.cast = tf35.cast(r.mul, "int32"); + r.tile = tf35.tile(r.cast, [1, 1, 3]); + r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); + return tf35.concat([r.tile, r.alpha], -1); + }); +} +async function predict20(input, config3) { + if (!model21) + model21 = await load20(config3); + if (!(model21 == null ? void 0 : model21["executor"])) + return null; + t.src = tf35.div(input, 255); + if (ratio !== config3.segmentation.ratio) + init3(config3); + const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + rgba = getRGBA(fgr, pha); + break; + case "alpha": + rgba = getRGBA(null, pha); + break; + case "foreground": + rgba = getRGBA(fgr, null); + break; + case "state": + rgba = getState(r1o); + break; + default: + rgba = tf35.tensor(0); + } + tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; + return rgba; +} + +// src/segmentation/selfie.ts +var tf36 = __toESM(require_tfjs_esm()); +var model22; +async function load21(config3) { + if (!model22 || env.initial) + model22 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model22["modelUrl"]); + return model22; +} +async function predict21(input, config3) { + var _a; + if (!model22) + model22 = await load21(config3); + if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) + return null; + const t2 = {}; + t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); + t2.norm = tf36.div(t2.resize, constants.tf255); + t2.res = model22.execute(t2.norm); + t2.squeeze = tf36.squeeze(t2.res, [0]); + t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); + t2.mul = tf36.mul(t2.alpha, constants.tf255); + let rgba; + switch (config3.segmentation.mode || "default") { + case "default": + t2.input = tf36.squeeze(input); + t2.concat = tf36.concat([t2.input, t2.mul], -1); + rgba = tf36.cast(t2.concat, "int32"); + break; + case "alpha": + rgba = tf36.cast(t2.mul, "int32"); + break; + default: + rgba = tf36.tensor(0); + } + Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); + return rgba; +} + +// src/models.ts +function validateModel(instance, model23, name) { + var _a, _b; + if (!model23) + return null; + if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) + return null; + const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; + const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; + const ops = []; + const missing = []; + const url = model23["modelUrl"]; + const executor = model23["executor"]; + if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { + for (const kernel of Object.values(executor.graph.nodes)) { + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); + } + } else { + if (!executor && instance.config.debug) { + log("model not loaded", name); + } + } + for (const op of ops) { + if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); + } + } + if (instance.config.debug && missing.length > 0) + log("model validation failed:", name, missing); + return missing.length > 0 ? { name, missing, ops, url } : null; +} +var Models = class { + constructor(currentInstance) { + __publicField(this, "instance"); + __publicField(this, "models", {}); + this.models = {}; + this.instance = currentInstance; + } + stats() { + let totalSizeFromManifest = 0; + let totalSizeWeights = 0; + let totalSizeLoading = 0; + for (const m of Object.values(modelStats)) { + totalSizeFromManifest += m.sizeFromManifest; + totalSizeWeights += m.sizeLoadedWeights; + totalSizeLoading += m.sizeDesired; + } + const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; + return { + numLoadedModels: Object.values(modelStats).length, + numDefinedModels: Object.keys(this.models).length, + percentageLoaded, + totalSizeFromManifest, + totalSizeWeights, + totalSizeLoading, + modelStats: Object.values(modelStats) + }; + } + reset() { + for (const model23 of Object.keys(this.models)) + this.models[model23] = null; + } + async load(instance) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; + if (env.initial) + this.reset(); + if (instance) + this.instance = instance; + const m = {}; + m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; + m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; + m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; + m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; + m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; + m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; + m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; + m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; + m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; + m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; + m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; + m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; + m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; + m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; + m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; + m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; + m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; + m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; + if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) + [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; + m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; + m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; + m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; + m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; + m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; + for (const [model23, promise] of Object.entries(m)) { + if (promise == null ? void 0 : promise["then"]) + promise["then"]((val) => this.models[model23] = val); + } + await Promise.all(Object.values(m)); + } + list() { + const models3 = Object.keys(this.models).map((model23) => { + var _a; + return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; + }); + for (const m of models3) { + const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); + if (!stats) + continue; + m.size = modelStats[stats].sizeLoadedWeights; + m.url = modelStats[stats].url; + } + return models3; + } + loaded() { + const list = this.list(); + const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); + return loaded; + } + validate() { + const missing = []; + for (const defined of Object.keys(this.models)) { + const model23 = this.models[defined]; + if (!model23) + continue; + const res = validateModel(this.instance, model23, defined); + if (res) + missing.push(res); + } + return missing; + } +}; + +// src/util/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f; + let id = 0; + const persons = []; + for (const face4 of faces) { + const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture2 of gestures) { + if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) + person2.gestures.push(gesture2); + else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) + person2.gestures.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) + person2.gestures.push(gesture2); + } + const x = []; + const y = []; + const extractXY = (box) => { + if (box && box.length === 4) { + x.push(box[0], box[0] + box[2]); + y.push(box[1], box[1] + box[3]); + } + }; + extractXY(person2.face.box); + extractXY((_d = person2.body) == null ? void 0 : _d.box); + extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); + extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons.push(person2); + } + return persons; +} + +// src/warmup.ts +var tf37 = __toESM(require_tfjs_esm()); + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -269,7 +14134,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Ht=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -837,4 +14703,563 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Bt);break;case"body":case"full":n=await t(Ht);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Aa(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Bt;break;case"full":case"body":n="data:image/jpeg;base64,"+Ht;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=te(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function aa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Bt):n=t(Ht);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ia(e){let t;return typeof createImageBitmap=="function"?t=await sa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await Aa(e):t=await aa(e),t}async function la(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let d=(y==null?void 0:y.modelSignature)&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])==null?void 0:l.shape)?[...y.inputs[0].shape]:[1,64,64,3],p=(y==null?void 0:y.modelSignature)&&((x=(c=y==null?void 0:y.inputs)==null?void 0:c[0])==null?void 0:x.dtype)?y.inputs[0].dtype:"float32";for(let 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0);me(this,B2,void 0);me(this,H2,void 0);k(this,"analyze",(...t)=>{if(!G0(this,B2))return;let n=this.tf.engine().state.numTensors,o=G0(this,M2);ge(this,M2,n);let r=n-o;r!==0&&u(...t,r)});me(this,Gt,t=>{if(!G0(this,H2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new U2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});me(this,Ue,{});let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Ye.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ye.modelBasePath=R.browser?"../models/":"file://models/",this.version=_t,Object.defineProperty(this,"version",{value:_t}),this.config=JSON.parse(JSON.stringify(Ye)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),F1(this.config),this.tf=ae,this.state="idle",ge(this,M2,0),ge(this,B2,!1),ge(this,H2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new F2(this),s5(),this.result=he(),this.process={tensor:null,canvas:null},this.faceTriangulation=j3,this.faceUVMap=N3,Ft(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ye)),this.config.backend=t,Jt(),R.initial=!0}validate(t){let n=Ut(Ye,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return X2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await X2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await ko(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await no(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Eo(o.tensor,this.config)),ae.dispose(o.tensor),r}compare(t,n){return D1(this.config,t,n)}async init(){await j2(this,!0),await this.tf.ready(),Jt()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await j2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return to(t,this.config)}async warmup(t){let n=g(),o=await jo(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return 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u5(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await l1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((t0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?Q5(a.tensor,d):[]:($=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?e1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(v=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&v.includes("handdetect")?x=this.config.hand.enabled?await Q5(a.tensor,d):[]:(n0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&n0.includes("handtrack")&&(x=this.config.hand.enabled?await e1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?i=this.config.object.enabled?d1(a.tensor,this.config):[]:(g0=this.config.object.modelPath)!=null&&g0.includes("centernet")&&(i=this.config.object.enabled?y5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(m0=this.config.object.modelPath)!=null&&m0.includes("nanodet")?i=this.config.object.enabled?await d1(a.tensor,this.config):[]:(B=this.config.object.modelPath)!=null&&B.includes("centernet")&&(i=this.config.object.enabled?await y5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Sn(l),...zn(c),...Nn(x),...jn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let f=((X=this.process.tensor)==null?void 0:X.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return So(l,c,x,p,f)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,Ue)[t.id]||(this.config.debug&&u("video start",t.id),G0(this,Ue)[t.id]=!0),!t.paused&&G0(this,Ue)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,Ue)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),G0(this,Ue)[t.id]=!1)}};M2=new WeakMap,B2=new WeakMap,H2=new WeakMap,Gt=new WeakMap,Ue=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); +2Q==`; + +// src/warmup.ts +async function warmupBitmap(instance) { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (instance.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "body": + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await instance.detect(bitmap, instance.config); + bitmap.close(); + } + return res; +} +async function warmupCanvas(instance) { + return new Promise((resolve) => { + let src; + switch (instance.config.warmup) { + case "face": + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + src = "data:image/jpeg;base64," + body3; + break; + default: + src = ""; + } + let img; + if (typeof Image !== "undefined") + img = new Image(); + else if (env.Image) + img = new env.Image(); + else { + resolve(void 0); + return; + } + img.onload = async () => { + const canvas3 = canvas(img.naturalWidth, img.naturalHeight); + if (!canvas3) { + log("Warmup: Canvas not found"); + resolve(void 0); + } else { + const ctx = canvas3.getContext("2d"); + if (ctx) + ctx.drawImage(img, 0, 0); + const tensor6 = await instance.image(canvas3, true); + const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; + resolve(res); + } + }; + if (src) + img.src = src; + else + resolve(void 0); + }); +} +async function warmupNode(instance) { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (instance.config.warmup === "face") + img = atob(face3); + else + img = atob(body3); + let res; + if ("node" in tf37 && tf37.getBackend() === "tensorflow") { + const data = tf37["node"].decodeJpeg(img); + const expanded = tf37.expandDims(data, 0); + instance.tf.dispose(data); + res = await instance.detect(expanded, instance.config); + instance.tf.dispose(expanded); + } else { + if (instance.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; +} +async function runInference(instance) { + let res; + if (typeof createImageBitmap === "function") + res = await warmupBitmap(instance); + else if (typeof Image !== "undefined" || env.Canvas !== void 0) + res = await warmupCanvas(instance); + else + res = await warmupNode(instance); + return res; +} +async function runCompile(instance) { + var _a, _b, _c, _d; + if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) + return; + const backendType = tf37.getBackend(); + const webGLBackend = tf37.backend(); + if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { + return; + } + tf37.env().set("ENGINE_COMPILE_ONLY", true); + const numTensorsStart = tf37.engine().state.numTensors; + const compiledModels = []; + for (const [modelName, model23] of Object.entries(instance.models.models)) { + if (!model23) + continue; + const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; + const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; + for (let dim = 0; dim < shape.length; dim++) { + if (shape[dim] === -1) + shape[dim] = dim === 0 ? 1 : 64; + } + const tensor6 = tf37.zeros(shape, dtype); + try { + const res = model23.execute(tensor6); + compiledModels.push(modelName); + if (Array.isArray(res)) + res.forEach((t2) => tf37.dispose(t2)); + else + tf37.dispose(res); + } catch (e) { + if (instance.config.debug) + log("compile fail model:", modelName); + } + tf37.dispose(tensor6); + } + const kernels = await webGLBackend["checkCompileCompletionAsync"](); + webGLBackend["getUniformLocations"](); + if (instance.config.debug) + log("compile pass:", { models: compiledModels, kernels: kernels.length }); + tf37.env().set("ENGINE_COMPILE_ONLY", false); + const numTensorsEnd = tf37.engine().state.numTensors; + if (numTensorsEnd - numTensorsStart > 0) + log("tensor leak:", numTensorsEnd - numTensorsStart); +} +async function warmup(instance, userConfig) { + await check(instance, false); + const t0 = now(); + instance.state = "warmup"; + if (userConfig) + instance.config = mergeDeep(instance.config, userConfig); + if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { + return empty(); + } + return new Promise(async (resolve) => { + await instance.models.load(); + await runCompile(instance); + const res = await runInference(instance); + const t1 = now(); + if (instance.config.debug) + log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); + instance.emit("warmup"); + resolve(res); + }); +} + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; +var Human = class { + constructor(userConfig) { + __publicField(this, "version"); + __publicField(this, "config"); + __publicField(this, "result"); + __publicField(this, "state"); + __publicField(this, "process"); + __publicField(this, "tf"); + __publicField(this, "env", env); + __publicField(this, "draw", draw_exports); + __publicField(this, "match", match_exports); + __publicField(this, "models"); + __publicField(this, "events"); + __publicField(this, "faceTriangulation"); + __publicField(this, "faceUVMap"); + __publicField(this, "performance"); + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __publicField(this, "analyze", (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }); + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.env.node && !(input instanceof tf38.Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __publicField(this, "webcam", new WebCam()); + __publicField(this, "emit", (event) => { + var _a; + if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) + this.events.dispatchEvent(new Event(event)); + }); + __privateAdd(this, _loops, {}); + const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); + config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; + config.modelBasePath = env.browser ? "../models/" : "file://models/"; + this.version = version2; + Object.defineProperty(this, "version", { value: version2 }); + this.config = JSON.parse(JSON.stringify(config)); + Object.seal(this.config); + this.config.cacheModels = typeof indexedDB !== "undefined"; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + setModelLoadOptions(this.config); + this.tf = tf38; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + this.performance = {}; + this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; + this.models = new Models(this); + init2(); + this.result = empty(); + this.process = { tensor: null, canvas: null }; + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + validateModel(this, null, ""); + this.emit("create"); + if (this.config.debug || this.env.browser) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version["tfjs-core"]}`); + const envTemp = JSON.parse(JSON.stringify(this.env)); + delete envTemp.kernels; + delete envTemp.initial; + delete envTemp.perfadd; + if (this.config.debug) + log("environment:", envTemp); + } + reset() { + const currentBackend = this.config.backend; + this.config = JSON.parse(JSON.stringify(config)); + this.config.backend = currentBackend; + reset(); + env.initial = true; + } + validate(userConfig) { + const msgs = validate(config, userConfig || this.config); + if (msgs.length === 0) + this.config = mergeDeep(this.config, userConfig); + return msgs; + } + now() { + return now(); + } + image(input, getTensor = false) { + return process2(input, this.config, getTensor); + } + async segmentation(input, userConfig) { + var _a, _b, _c; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (!this.config.segmentation.enabled) + return null; + const processed = await process2(input, this.config); + if (!processed.tensor) + return null; + let tensor6 = null; + if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) + tensor6 = await predict20(processed.tensor, this.config); + if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) + tensor6 = await predict16(processed.tensor, this.config); + if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) + tensor6 = await predict21(processed.tensor, this.config); + tf38.dispose(processed.tensor); + return tensor6; + } + compare(firstImageTensor, secondImageTensor) { + return compare(this.config, firstImageTensor, secondImageTensor); + } + async init() { + await check(this, true); + await this.tf.ready(); + reset(); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + const count2 = Object.values(this.models.models).filter((model23) => model23).length; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (this.env.initial) { + if (!await check(this, false)) + log("error: backend check failed"); + await tf38.ready(); + if (this.env.browser) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("tf flags:", this.tf.ENV.flags); + } + } + await this.models.load(this); + if (this.env.initial && this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + this.env.initial = false; + const loaded = Object.values(this.models.models).filter((model23) => model23).length; + if (loaded !== count2) { + this.models.validate(); + this.emit("load"); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.loadModels || 0)) + this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; + } + next(result = this.result) { + return calc2(result, this.config); + } + async warmup(userConfig) { + const t0 = now(); + const res = await warmup(this, userConfig); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t0); + return res; + } + async profile(input, userConfig) { + const profile = await this.tf.profile(() => this.detect(input, userConfig)); + const kernels = {}; + let total = 0; + for (const kernel of profile.kernels) { + const ms = Number(kernel.kernelTimeMs) || 0; + if (kernels[kernel.name]) + kernels[kernel.name] += ms; + else + kernels[kernel.name] = ms; + total += ms; + } + const kernelArr = []; + Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); + for (const kernel of kernelArr) { + kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; + kernel.time = Math.round(1e3 * kernel.time) / 1e3; + } + kernelArr.sort((a, b) => b.time - a.time); + kernelArr.length = 20; + return kernelArr; + } + async detect(input, userConfig) { + this.state = "detect"; + return new Promise(async (resolve) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; + this.state = "config"; + let timeStamp; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + this.emit("error"); + resolve(empty(error)); + } + const timeStart = now(); + await this.load(); + timeStamp = now(); + this.state = "image"; + const img = await process2(input, this.config); + this.process = img; + this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (!img.tensor) { + if (this.config.debug) + log("could not convert input to tensor"); + this.emit("error"); + resolve(empty("could not convert input to tensor")); + return; + } + this.emit("image"); + timeStamp = now(); + this.config.skipAllowed = await skip(this.config, img.tensor); + this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; + if (!this.performance.totalFrames) + this.performance.totalFrames = 0; + if (!this.performance.cachedFrames) + this.performance.cachedFrames = 0; + this.performance.totalFrames++; + if (this.config.skipAllowed) + this.performance.cachedFrames++; + this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes = []; + let bodyRes = []; + let handRes = []; + let objectRes = []; + this.state = "detect:face"; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; + this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) + faceRes = await faceRes; + this.analyze("Start Body:"); + this.state = "detect:body"; + const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) + bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; + else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; + else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; + else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) + bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; + if (this.performance.body) + delete this.performance.body; + } else { + timeStamp = now(); + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; + else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; + else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; + else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; + this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + this.state = "detect:hand"; + const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) + handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; + else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) + handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + timeStamp = now(); + if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) + handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; + else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) + handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; + this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + this.state = "detect:object"; + if (this.config.async) { + if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) + objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; + else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) + objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + timeStamp = now(); + if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; + else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) + objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; + this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Object:"); + this.state = "detect:await"; + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + this.state = "detect:gesture"; + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; + if (!this.config.async) + this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); + const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: this.process.canvas, + timestamp: Date.now(), + error: null, + width: shape[2], + height: shape[1], + get persons() { + return join2(faceRes, bodyRes, handRes, gestureRes, shape); + } + }; + tf38.dispose(img.tensor); + this.emit("detect"); + this.state = "idle"; + resolve(this.result); + }); + } + async sleep(ms) { + return new Promise((resolve) => { + setTimeout(resolve, ms); + }); + } + async video(element, run = true, delay = 0) { + if (run) { + if (!__privateGet(this, _loops)[element.id]) { + if (this.config.debug) + log("video start", element.id); + __privateGet(this, _loops)[element.id] = true; + } + if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) + await this.detect(element); + if (delay > 0) + await this.sleep(delay); + if (__privateGet(this, _loops)[element.id]) + requestAnimationFrame(() => this.video(element, run, delay)); + } else { + if (this.config.debug) + log("video stop", element.id); + __privateGet(this, _loops)[element.id] = false; + } + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_sanity = new WeakMap(); +_loops = new WeakMap(); +// Annotate the CommonJS export names for ESM import in node: +0 && (module.exports = { + Env, + Human, + defaults, + draw, + empty, + env, + match, + models +}); diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js index 40398235..2f0ab8b0 100644 --- a/dist/tfjs.esm.js +++ b/dist/tfjs.esm.js @@ -4,57 +4,57 @@ author: ' */ -var yV=Object.create;var ub=Object.defineProperty;var bV=Object.getOwnPropertyDescriptor;var CV=Object.getOwnPropertyNames;var 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Ab;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(Ab||(Ab={}));var Rb;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(Rb||(Rb={}));var nz={float32:Ab,int32:Eb,bool:$b,complex64:Rb};function dt(r,e){if(r==="string"||e==="string"){if(r==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${r} with ${e}`)}return nz[r][e]}function ka(r){return dt(r,"int32")}function Re(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Fb(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function sz(r,e){return e.some(t=>t.id===r.id)}function Qc(r){let e=[];return f0(r,e,new Set),e}function f0(r,e,t){if(r==null)return;if(r instanceof it){e.push(r);return}if(!az(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),f0(s,e,t))}}function az(r){return Array.isArray(r)||typeof r=="object"}function Db(r){return r.kernelName!=null}var Pm=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},xi=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Pm}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Am(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Zr)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return xi.nextTensorId++}nextVariableId(){return xi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i="float32",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(qc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Db(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=qc(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Db(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Cb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Sm(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof va||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let o=e.size*Sm(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await 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a=o.size/n.size,i=He(et(s),et(n));return a>1?He(i,Ce(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return He(et(s),Ce(o.size));{let a=ne(n,Hs(o.shape)),i=je(et(hd(a,Ce(0))),"float32");return He(et(s),i)}}throw Error(`Unknown reduction: ${t}`)}var ar=N({computeWeightedLoss_:FH});function OH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),gt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Yt(ke(n,s));return ar(i,a,o)}var W1=N({absoluteDifference_:OH});function PH(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),gt(s.shape,a.shape,"Error in cosineDistance: ");let p=Ce(1),u=ke(p,et(ne(s,a),t,!0));return ar(u,i,n)}var U1=N({cosineDistance_:PH});function MH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),gt(n.shape,s.shape,"Error in hingeLoss: ");let i=Ce(1);n=ke(ne(Ce(2),n),i);let p=Fi(ke(i,ne(n,s)));return ar(p,a,o)}var G1=N({hingeLoss_:MH});function LH(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),gt(s.shape,a.shape,"Error in huberLoss: ");let p=Ce(o),u=Yt(ke(a,s)),c=fd(u,p),l=ke(u,c),m=be(ne(Ce(.5),Qt(c)),ne(p,l));return ar(m,i,n)}var H1=N({huberLoss_:LH});function BH(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),gt(s.shape,a.shape,"Error in logLoss: ");let p=Ce(1),u=Ce(o),c=Cr(ne(s,Da(be(a,u)))),l=ne(ke(p,s),Da(be(ke(p,a),u))),m=ke(c,l);return ar(m,i,n)}var K1=N({logLoss_:BH});function VH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),gt(n.shape,s.shape,"Error in meanSquaredError: ");let i=_d(n,s);return ar(i,a,o)}var q1=N({meanSquaredError_:VH});function zH(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");gt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Fi(o),s=ne(o,t),a=sd(So(Cr(Yt(o))));return be(ke(n,s),a)}function WH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),gt(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=Ce(o),c=Ce(1),l=Ce(.5);s=be(ne(s,ke(c,u)),ne(l,u))}let p=zH(s,a);return ar(p,i,n)}var j1=N({sigmoidCrossEntropy_:WH});function UH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return br((n,s,a)=>{let p=ud(s,[t],!0),u=ke(je(s,"float32"),p);a([n,u]);let c=Cr(ne(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Ea(d.shape,[t]);return[ne(W(d,x),ke(je(h,"float32"),So(g))),ne(W(d,x),ke(So(g),je(h,"float32")))]}}})(r,e)}function GH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),gt(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=Ce(o),c=Ce(1),l=Ce(s.shape[1]);s=be(ne(s,ke(c,u)),He(u,l))}let p=UH(s,a);return ar(p,i,n)}var X1=N({softmaxCrossEntropy_:GH});function HH(r,e,t,o){let n=v(r,"indices","sparseFillEmptyRows","int32"),s=v(e,"values","sparseFillEmptyRows"),a=v(t,"denseShape","sparseFillEmptyRows","int32"),i=v(o,"defaultValue","sparseFillEmptyRows",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape + ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(hi,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var Y1=N({sparseFillEmptyRows_:HH});function KH(r,e,t){let o=v(r,"inputIndices","sparseReshape","int32"),n=v(e,"inputShape","sparseReshape","int32"),s=v(t,"newShape","sparseReshape","int32");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape + ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(va,a);return{outputIndices:i[0],outputShape:i[1]}}var Q1=N({sparseReshape_:KH});function qH(r,e,t){let o=v(r,"data","sparseSegmentMean"),n=v(e,"indices","sparseSegmentMean","int32"),s=v(t,"segmentIds","sparseSegmentMean","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(pi,a)}var gN=N({sparseSegmentMean_:tq});function rq(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape + ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(gi,a)}var Z1=N({sparseSegmentMean_:qH});function jH(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ci,a)}var xN=N({sparseSegmentSum_:rq});function oq(r,e,t,o,n,s,a,i){let p=v(r,"data","stringNGrams","string");if(p.dtype!=="string")throw new Error("Data must be of datatype string");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(As,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var yN=N({stringNGrams_:oq});function nq(r,e,t=!0){let o=v(r,"input","stringSplit","string"),n=v(e,"delimiter","stringSplit","string");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(di,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var bN=N({stringSplit_:nq});function sq(r,e){let t=v(r,"input","stringToHashBucketFast","string"),o={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let n={input:t};return T.runKernel(fi,n,o)}var CN=N({stringToHashBucketFast_:sq});var aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className="Adadelta";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className="Adagrad";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className="Adam";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className="Adamax";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className="Momentum";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Di.className="RMSProp";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static 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this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ce(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var yu=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}static get className(){return"Adadelta"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=be(ne(p,this.rho),ne(Qt(i),1-this.rho)),l=ne(He($r(be(u,this.epsilon)),$r(be(p,this.epsilon))),i),m=be(ne(u,this.rho),ne(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(ne(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var bu=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Us(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=be(i,Qt(a));i.assign(p);let u=be(ne(He(a,$r(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Cu=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return"Adam"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=ke(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=be(ne(l,this.beta2),ne(Qt(u),1-this.beta2)),f=He(m,o),h=He(d,n);c.assign(m),l.assign(d);let g=be(ne(He(f,be($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ne(this.accBeta1,this.beta1)),this.accBeta2.assign(ne(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Aa(this.beta1,this.iterations_+1)),this.accBeta2.assign(Aa(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var Su=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return"Adamax"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=He(-this.learningRate,be(ne(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=ne(l,this.beta2),f=Yt(u),h=dd(d,f);c.assign(m),l.assign(h);let g=be(ne(He(n,o),He(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(ne(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var Pa=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=be(ne(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var wu=class extends Pa{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=Ce(this.momentum)}static get className(){return"Momentum"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=be(ne(this.m,a),i);this.useNesterov?p=be(ne(this.c,be(i,ne(u,this.m))),s):p=be(ne(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var Iu=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}static get className(){return"RMSProp"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=be(ne(p,this.decay),ne(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=be(ne(l,this.decay),ne(i,1-this.decay)),d=He(ne(i,this.learningRate),$r(ke(c,be(Qt(m),this.epsilon)))),f=be(ne(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=ke(s,f);s.assign(h)}else{let l=be(ne(p,this.decay),ne(Qt(i),1-this.decay)),m=be(ne(u,this.momentum),He(ne(i,this.learningRate),$r(be(l,this.epsilon))));p.assign(l),u.assign(m);let d=ke(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var sK=[yu,bu,Cu,Su,wu,Iu,Pa];function nN(){for(let r of sK)IC(r)}var La={};Ge(La,{browserFiles:()=>aN,browserHTTPRequest:()=>pN,concatenateArrayBuffers:()=>Fp,copyModel:()=>V0,decodeWeights:()=>Bm,encodeWeights:()=>v0,fromMemory:()=>cN,fromMemorySync:()=>$C,getLoadHandlers:()=>E0,getModelArtifactsForJSON:()=>Op,getModelArtifactsForJSONSync:()=>Qb,getModelArtifactsInfoForJSON:()=>Ms,getSaveHandlers:()=>$0,getWeightSpecs:()=>zm,http:()=>Vd,isHTTPScheme:()=>Bd,listModels:()=>L0,loadWeights:()=>iN,moveModel:()=>z0,registerLoadRouter:()=>_0,registerSaveRouter:()=>T0,removeModel:()=>B0,weightsLoaderFactory:()=>TC,withSaveHandler:()=>lN,withSaveHandlerSync:()=>mN});var aK="model",iK=".json",uK=".weights.bin";function sN(r){return new Promise(e=>setTimeout(e)).then(r)}var Ma=class{constructor(e){if(!O().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ma.URL_SCHEME)&&(e=e.slice(Ma.URL_SCHEME.length)),(e==null||e.length===0)&&(e=aK),this.modelJsonFileName=e+iK,this.weightDataFileName=e+uK}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let o=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],n=Vm(e,o),s=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=s,await sN(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await sN(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ms(e)}}}};Ma.URL_SCHEME="downloads://";var vC=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let o=new FileReader;o.onload=n=>{let s=JSON.parse(n.target.result),a=s.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let p=Op(s,u=>this.loadWeights(u));e(p)},o.onerror=n=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. 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BK(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function VK(r){return r<=Ud?r:cp(r,Math.floor(Math.sqrt(r)))}function zK(r,e,t){let o=t*(typeof r=="number"?r:r[0]),n=e*(typeof r=="number"?r:r[1]);return[o,n]}function WK(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function GK(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s/g,TN=",",_N="...";function cq(r,e){r=r.replace(/\s/g,"");let t=(r.length-r.replace(pq,"").length)/RC.length;if(t<1)throw new Error("Equations without an arrow are not supported.");if(t>1)throw new Error(`Equation must contain exactly one arrow ("${RC}").`);let[o,n]=r.split(RC);$(o.indexOf(_N)===-1,()=>`The ellipsis notation ("${_N}") is not supported yet.`);let s=o.split(TN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function mq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function dq(r,e){let t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function hq(r,e){let t=[];for(let o=0;o"Number of splits must evenly divide the axis."),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);$(n<=1,()=>"There should be only one negative value in split array.");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}$(r.shape[t]===e.reduce((a,i)=>a+i),()=>"The sum of sizes must match the size of the axis dimension."),o=e}return o}function xq(r){return`Received SparseTensor with denseShape[0] = 0 but + indices.shape[0] = ${r}`}function yq(r,e){return`indices(${r}, 0) is invalid: ${e} < 0`}function bq(r,e,t){return`indices(${r}, 0) is invalid: ${e} >= ${t}`}function Cq(r,e){return`only one output dimension may be -1, not both ${r} and ${e}`}function Sq(r,e){return`size ${r} must be non-negative, not ${e}`}function wq(){return"reshape cannot infer the missing input size for an empty tensor unless all specified 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o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=I("indices",r,e,t),n=I("tensor",r,e,t),s=I("elementShape",r,e,t),a=I("numElements",r,e,t),i=FN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=I("elementShape",r,e,t),n=I("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=I(s,r,e,t),i=r.op==="TensorListReserve"?-1:a,p=RN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case"TensorListGather":{let o=I("tensorListId",r,e,t),n=I("indices",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=I("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=AN(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id),s=I("dtype",r,e,t),a=I("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=I("tensorListId",r,e,t),n=I("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("lengths",r,e,t),a=DN(o,s,n);return t.addTensorList(a),[a.idTensor]}case"TensorListLength":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id);return[be(n.size(),"int32")]}case"TensorListResize":{let o=I("tensorListId",r,e,t),n=I("size",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PN(r,e,t){let[o,n]=I("fusedOps",r,e,t),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,e,t);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,e,t),l=ul(r,e,t),m=I("dataFormat",r,e,t).toUpperCase(),d=I("dilations",r,e,t),[f,h]=I("args",r,e,t);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var MN=(r,e,t,o=Ye)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilation",r,e,t);return[o.conv1d(I("x",r,e,t),I("filter",r,e,t),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,e,t),s=ul(r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv2d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.conv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.depthwiseConv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=I("outputShape",r,e,t),s=I("strides",r,e,t),a=ul(r,e,t);return[o.conv2dTranspose(I("x",r,e,t),I("filter",r,e,t),n,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=I("strides",r,e,t),s=ul(r,e,t),a=I("dilations",r,e,t),i=I("dataFormat",r,e,t).toUpperCase();return[o.depthwiseConv2d(I("input",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case"Conv3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv3d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case"AvgPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPoolWithArgmax":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t),i=I("includeBatchInIndex",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dilations",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,e,t),I("filter",r,e,t),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LN=(r,e,t,o=Ye)=>{switch(r.op){case"Fill":{let n=I("shape",r,e,t),s=I("dtype",r,e,t),a=I("value",r,e,t);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("num",r,e,t);return[o.linspace(n,s,a)]}case"Multinomial":{let n=I("logits",r,e,t),s=I("numSamples",r,e,t),a=I("seed",r,e,t);return[o.multinomial(n,s,a)]}case"OneHot":{let n=I("indices",r,e,t),s=I("depth",r,e,t),a=I("onValue",r,e,t),i=I("offValue",r,e,t),p=I("dtype",r,e,t);return[o.oneHot(n,s,a,i,p)]}case"Ones":return[o.ones(I("shape",r,e,t),I("dtype",r,e,t))];case"OnesLike":return[o.onesLike(I("x",r,e,t))];case"RandomStandardNormal":return[o.randomStandardNormal(I("shape",r,e,t),I("dtype",r,e,t),I("seed",r,e,t))];case"RandomUniform":return[o.randomUniform(I("shape",r,e,t),I("minval",r,e,t),I("maxval",r,e,t),I("dtype",r,e,t))];case"Range":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("step",r,e,t);return[o.range(n,s,a,I("dtype",r,e,t))]}case"TruncatedNormal":{let n=I("shape",r,e,t),s=I("mean",r,e,t),a=I("stdDev",r,e,t),i=I("seed",r,e,t);return[o.truncatedNormal(n,s,a,I("dtype",r,e,t),i)]}case"Zeros":return[o.zeros(I("shape",r,e,t),I("dtype",r,e,t))];case"ZerosLike":return[o.zerosLike(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function HC(r,e,t){let o=I("boxes",r,e,t),n=I("scores",r,e,t),s=I("maxOutputSize",r,e,t),a=I("iouThreshold",r,e,t),i=I("scoreThreshold",r,e,t),p=I("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var BN=async(r,e,t,o,n=Ye)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=HC(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t),c=I("padToMaxOutputSize",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case"Where":{let s=n.cast(I("condition",r,e,t),"bool"),a=[await n.whereAsync(s)];return s.dispose(),a}case"ListDiff":return n.setdiff1dAsync(I("x",r,e,t),I("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VN=(r,e,t,o=Ye)=>{switch(r.op){case"LowerBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.lowerBound(n,s)]}case"TopKV2":{let n=I("x",r,e,t),s=I("k",r,e,t),a=I("sorted",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case"UpperBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.upperBound(n,s)]}case"Unique":{let n=I("x",r,e,t),s=o.unique(n);return[s.values,s.indices]}case"UniqueV2":{let n=I("x",r,e,t),s=I("axis",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zN=(r,e,t,o=Ye)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let n=I("default",r,e,t);return[Gt(r.name,e,t)||n];case"Placeholder":return[Gt(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,e,t);return[as(c)]}case"IdentityN":return I("x",r,e,t).map(c=>as(c));case"Snapshot":let s=I("x",r,e,t);return[as(s)];case"Shape":return[o.tensor1d(I("x",r,e,t).shape,"int32")];case"ShapeN":return I("x",r,e,t).map(c=>o.tensor1d(c.shape));case"Size":return[o.scalar(I("x",r,e,t).size,"int32")];case"Rank":return[o.scalar(I("x",r,e,t).rank,"int32")];case"NoOp":return[o.scalar(1)];case"Print":let a=I("x",r,e,t),i=I("data",r,e,t),p=I("message",r,e,t),u=I("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,e,t),a=I("valueDType",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case"LookupTableSize":case"LookupTableSizeV2":{let n=I("tableHandle",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UN=(r,e,t,o=Ye)=>{switch(r.op){case"ResizeBilinear":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case"ResizeNearestNeighbor":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case"CropAndResize":{let n=I("image",r,e,t),s=I("boxes",r,e,t),a=I("boxInd",r,e,t),i=I("cropSize",r,e,t),p=I("method",r,e,t),u=I("extrapolationValue",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case"ImageProjectiveTransformV3":{let n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HN=(r,e,t,o=Ye)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];case"SparseToDense":return[o.sparseToDense(I("sparseIndices",r,e,t),I("outputShape",r,e,t),I("sparseValues",r,e,t),I("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,e,t),I("limits",r,e,t),I("splits",r,e,t));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,e,t),I("values",r,e,t),I("defaultValue",r,e,t),I("rowPartitionTensors",r,e,t),I("rowPartitionTypes",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jN=(r,e,t,o=Ye)=>{switch(r.op){case"Max":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.max(I("x",r,e,t),i,p)]}case"Mean":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.mean(I("x",r,e,t),i,p)]}case"Min":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.min(I("x",r,e,t),i,p)]}case"Sum":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.sum(I("x",r,e,t),i,p)]}case"All":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.all(I("x",r,e,t),i,p)]}case"Any":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.any(I("x",r,e,t),i,p)]}case"ArgMax":{let i=I("axis",r,e,t);return[o.argMax(I("x",r,e,t),i)]}case"ArgMin":{let i=I("axis",r,e,t);return[o.argMin(I("x",r,e,t),i)]}case"Prod":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.prod(I("x",r,e,t),i,p)]}case"Cumprod":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumprod(I("x",r,e,t),i,p,u)]}case"Cumsum":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumsum(I("x",r,e,t),i,p,u)]}case"Bincount":let n=I("x",r,e,t),s=I("weights",r,e,t),a=I("size",r,e,t);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,e,t),p=I("weights",r,e,t),u=I("size",r,e,t),c=I("binaryOutput",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,e,t),s=I("axis",r,e,t),a=I("tensors",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,e,t),s=I("batchDims",r,e,t),a=I("x",r,e,t),i=I("indices",r,e,t);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,e,t),s=[];for(let i=0;i{let n=I("axis",r,e,t),s=I("tensors",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,e,t),s=I("tensor",r,e,t);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,e,t);return[o.tile(I("x",r,e,t),n)]}case"Split":case"SplitV":{let n=I("axis",r,e,t),s=I("numOrSizeSplits",r,e,t),a=I("x",r,e,t);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("shape",r,e,t);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,e,t),s=I("outputShape",r,e,t),a=I("sparseValues",r,e,t),i=I("defaultValue",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qC(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>_N(a,i,p));case"basic_math":return 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File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var cl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function KC(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((jC(m)||i6(m)||u6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function eT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var n6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],s6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],a6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function jC(r){return n6.indexOf(r.op)>=0}function i6(r){return s6.indexOf(r.op)>=0}function u6(r){return a6.indexOf(r.op)>=0}var Cu=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Cu(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=KC(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return eT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new cl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category==="control"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!=="control"){let u=vN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new cl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=KC(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=p.filter(C=>!jC(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C="";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=ss(l.node.name,o)),n[l.node.name]==null){let d=qC(l.node,n,o,this._resourceManager);m||([m]=ss(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=ss(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var p6="?tfjs-format=file",c6="model.json",ll=class{constructor(e,t={},o=Ea){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cu(pl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=pl.Instance.transformGraph(e.modelInitializer);this.initializer=new Cu(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Dt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function l6(r,e={},t=Ea){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");e==null&&(e={}),e.fromTFHub&&typeof r=="string"&&(r=d6(r));let o=new ll(r,e,t);return await o.load(),o}function m6(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error("modelJSON must be the first element of the array");if(!n||!(n instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in o))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in o))throw new Error("Model JSON is missing 'weightsManifest'");let s=Ea.getWeightSpecs(o.weightsManifest),a=Ea.getModelArtifactsForJSONSync(o,s,n);e=Ea.fromMemorySync(a)}else if("load"in r)e=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)e=Ea.fromMemorySync(r);else throw new Error("Unknown model format");let t=new ll(e);return t.load(),t}function d6(r){return r.endsWith("/")||(r=r+"/"),`${r}${c6}${p6}`}var f6="4.1.0";function K(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var h6=Lt.whereImpl,Oi=class extends Zr{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Do(this,cr())}nextDataId(){return Oi.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,O().get("IS_NODE")&&S.warn(` + ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=of(s,t),i=o===0?0:r.size/o,p=Ee(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l{switch(r.op){case"If":case"StatelessIf":{let o=I("thenBranch",r,e,t),n=I("elseBranch",r,e,t),s=I("cond",r,e,t),a=I("args",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let o=I("body",r,e,t),n=I("cond",r,e,t),s=I("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case"LoopCond":{let o=I("pred",r,e,t);return[is(o)]}case"Switch":{let o=I("pred",r,e,t),n=I("data",r,e,t);return n.kept||(n=is(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>Gt(n,e,t)!==void 0);if(o){let n=Gt(o,e,t);return[is(n)]}return}case"Enter":{let o=I("frameName",r,e,t),n=I("tensor",r,e,t);return t.enterFrame(o),[is(n)]}case"Exit":{let o=I("tensor",r,e,t);return t.exitFrame(),[is(o)]}case"NextIteration":{let o=I("tensor",r,e,t);return t.nextIteration(),[is(o)]}case"TensorArrayV3":{let o=I("size",r,e,t),n=I("dtype",r,e,t),s=I("elementShape",r,e,t),a=I("dynamicSize",r,e,t),i=I("clearAfterRead",r,e,t),p=I("identicalElementShapes",r,e,t),u=I("name",r,e,t),c=new nf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,Ce(1)]}case"TensorArrayWriteV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t);return[t.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("dtype",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id),s=I("dtype",r,e,t);return[n.concat(s)]}case"TensorArraySplitV3":{let o=I("tensorArrayId",r,e,t),n=I("tensor",r,e,t),s=I("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[Ce(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=I("indices",r,e,t),n=I("tensor",r,e,t),s=I("elementShape",r,e,t),a=I("numElements",r,e,t),i=BN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=I("elementShape",r,e,t),n=I("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=I(s,r,e,t),i=r.op==="TensorListReserve"?-1:a,p=LN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case"TensorListGather":{let o=I("tensorListId",r,e,t),n=I("indices",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=I("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=MN(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id),s=I("dtype",r,e,t),a=I("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=I("tensorListId",r,e,t),n=I("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("lengths",r,e,t),a=VN(o,s,n);return t.addTensorList(a),[a.idTensor]}case"TensorListLength":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id);return[Ce(n.size(),"int32")]}case"TensorListResize":{let o=I("tensorListId",r,e,t),n=I("size",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function WN(r,e,t){let[o,n]=I("fusedOps",r,e,t),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,e,t);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,e,t),l=ll(r,e,t),m=I("dataFormat",r,e,t).toUpperCase(),d=I("dilations",r,e,t),[f,h]=I("args",r,e,t);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var UN=(r,e,t,o=Ye)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilation",r,e,t);return[o.conv1d(I("x",r,e,t),I("filter",r,e,t),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,e,t),s=ll(r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv2d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.conv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.depthwiseConv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=I("outputShape",r,e,t),s=I("strides",r,e,t),a=ll(r,e,t);return[o.conv2dTranspose(I("x",r,e,t),I("filter",r,e,t),n,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=I("strides",r,e,t),s=ll(r,e,t),a=I("dilations",r,e,t),i=I("dataFormat",r,e,t).toUpperCase();return[o.depthwiseConv2d(I("input",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case"Conv3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv3d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case"AvgPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPoolWithArgmax":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t),i=I("includeBatchInIndex",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dilations",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,e,t),I("filter",r,e,t),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case"Fill":{let n=I("shape",r,e,t),s=I("dtype",r,e,t),a=I("value",r,e,t);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("num",r,e,t);return[o.linspace(n,s,a)]}case"Multinomial":{let n=I("logits",r,e,t),s=I("numSamples",r,e,t),a=I("seed",r,e,t);return[o.multinomial(n,s,a)]}case"OneHot":{let n=I("indices",r,e,t),s=I("depth",r,e,t),a=I("onValue",r,e,t),i=I("offValue",r,e,t),p=I("dtype",r,e,t);return[o.oneHot(n,s,a,i,p)]}case"Ones":return[o.ones(I("shape",r,e,t),I("dtype",r,e,t))];case"OnesLike":return[o.onesLike(I("x",r,e,t))];case"RandomStandardNormal":return[o.randomStandardNormal(I("shape",r,e,t),I("dtype",r,e,t),I("seed",r,e,t))];case"RandomUniform":return[o.randomUniform(I("shape",r,e,t),I("minval",r,e,t),I("maxval",r,e,t),I("dtype",r,e,t))];case"Range":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("step",r,e,t);return[o.range(n,s,a,I("dtype",r,e,t))]}case"TruncatedNormal":{let n=I("shape",r,e,t),s=I("mean",r,e,t),a=I("stdDev",r,e,t),i=I("seed",r,e,t);return[o.truncatedNormal(n,s,a,I("dtype",r,e,t),i)]}case"Zeros":return[o.zeros(I("shape",r,e,t),I("dtype",r,e,t))];case"ZerosLike":return[o.zerosLike(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function rS(r,e,t){let o=I("boxes",r,e,t),n=I("scores",r,e,t),s=I("maxOutputSize",r,e,t),a=I("iouThreshold",r,e,t),i=I("scoreThreshold",r,e,t),p=I("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var HN=async(r,e,t,o,n=Ye)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=rS(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t),c=I("padToMaxOutputSize",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case"Where":{let s=n.cast(I("condition",r,e,t),"bool"),a=[await n.whereAsync(s)];return s.dispose(),a}case"ListDiff":return n.setdiff1dAsync(I("x",r,e,t),I("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case"LowerBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.lowerBound(n,s)]}case"TopKV2":{let n=I("x",r,e,t),s=I("k",r,e,t),a=I("sorted",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case"UpperBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.upperBound(n,s)]}case"Unique":{let n=I("x",r,e,t),s=o.unique(n);return[s.values,s.indices]}case"UniqueV2":{let n=I("x",r,e,t),s=I("axis",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let n=I("default",r,e,t);return[Gt(r.name,e,t)||n];case"Placeholder":return[Gt(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,e,t);return[is(c)]}case"IdentityN":return I("x",r,e,t).map(c=>is(c));case"Snapshot":let s=I("x",r,e,t);return[is(s)];case"Shape":return[o.tensor1d(I("x",r,e,t).shape,"int32")];case"ShapeN":return I("x",r,e,t).map(c=>o.tensor1d(c.shape));case"Size":return[o.scalar(I("x",r,e,t).size,"int32")];case"Rank":return[o.scalar(I("x",r,e,t).rank,"int32")];case"NoOp":return[o.scalar(1)];case"Print":let a=I("x",r,e,t),i=I("data",r,e,t),p=I("message",r,e,t),u=I("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ce(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,e,t),a=I("valueDType",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case"LookupTableSize":case"LookupTableSizeV2":{let n=I("tableHandle",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case"ResizeBilinear":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case"ResizeNearestNeighbor":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case"CropAndResize":{let n=I("image",r,e,t),s=I("boxes",r,e,t),a=I("boxInd",r,e,t),i=I("cropSize",r,e,t),p=I("method",r,e,t),u=I("extrapolationValue",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case"ImageProjectiveTransformV3":{let n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];case"SparseToDense":return[o.sparseToDense(I("sparseIndices",r,e,t),I("outputShape",r,e,t),I("sparseValues",r,e,t),I("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,e,t),I("limits",r,e,t),I("splits",r,e,t));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,e,t),I("values",r,e,t),I("defaultValue",r,e,t),I("rowPartitionTensors",r,e,t),I("rowPartitionTypes",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eT=(r,e,t,o=Ye)=>{switch(r.op){case"Max":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.max(I("x",r,e,t),i,p)]}case"Mean":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.mean(I("x",r,e,t),i,p)]}case"Min":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.min(I("x",r,e,t),i,p)]}case"Sum":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.sum(I("x",r,e,t),i,p)]}case"All":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.all(I("x",r,e,t),i,p)]}case"Any":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.any(I("x",r,e,t),i,p)]}case"ArgMax":{let i=I("axis",r,e,t);return[o.argMax(I("x",r,e,t),i)]}case"ArgMin":{let i=I("axis",r,e,t);return[o.argMin(I("x",r,e,t),i)]}case"Prod":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.prod(I("x",r,e,t),i,p)]}case"Cumprod":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumprod(I("x",r,e,t),i,p,u)]}case"Cumsum":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumsum(I("x",r,e,t),i,p,u)]}case"Bincount":let n=I("x",r,e,t),s=I("weights",r,e,t),a=I("size",r,e,t);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,e,t),p=I("weights",r,e,t),u=I("size",r,e,t),c=I("binaryOutput",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tT=(r,e,t,o=Ye)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,e,t),s=I("axis",r,e,t),a=I("tensors",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,e,t),s=I("batchDims",r,e,t),a=I("x",r,e,t),i=I("indices",r,e,t);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,e,t),s=[];for(let i=0;i{let n=I("axis",r,e,t),s=I("tensors",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,e,t),s=I("tensor",r,e,t);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,e,t);return[o.tile(I("x",r,e,t),n)]}case"Split":case"SplitV":{let n=I("axis",r,e,t),s=I("numOrSizeSplits",r,e,t),a=I("x",r,e,t);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("shape",r,e,t);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,e,t),s=I("outputShape",r,e,t),a=I("sparseValues",r,e,t),i=I("defaultValue",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rT=(r,e,t,o=Ye)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oT=(r,e,t,o=Ye)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nT=(r,e,t,o=Ye)=>{switch(r.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sT=(r,e,t,o=Ye)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function oS(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>FN(a,i,p));case"basic_math":return n(()=>ON(a,i,p));case"control":return zN(a,i,p);case"convolution":return n(()=>UN(a,i,p));case"creation":return n(()=>GN(a,i,p));case"dynamic":return HN(a,i,p);case"evaluation":return n(()=>KN(a,i,p));case"image":return n(()=>XN(a,i,p));case"graph":return n(()=>qN(a,i,p));case"logical":return n(()=>YN(a,i,p));case"matrices":return n(()=>QN(a,i,p));case"normalization":return n(()=>ZN(a,i,p));case"ragged":return n(()=>JN(a,i,p));case"reduction":return n(()=>eT(a,i,p));case"slice_join":return n(()=>tT(a,i,p));case"sparse":return n(()=>rT(a,i,p));case"spectral":return n(()=>oT(a,i,p));case"string":return n(()=>nT(a,i,p));case"transformation":return n(()=>sT(a,i,p));case"hash_table":return jN(a,i,p,o);case"custom":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function nS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((sS(m)||T6(m)||_6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function aT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var v6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],k6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],N6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function sS(r){return v6.indexOf(r.op)>=0}function T6(r){return k6.indexOf(r.op)>=0}function _6(r){return N6.indexOf(r.op)>=0}var ku=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ku(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=nS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return aT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new dl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category==="control"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!=="control"){let u=EN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new dl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=nS(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=p.filter(C=>!sS(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C="";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=as(l.node.name,o)),n[l.node.name]==null){let d=oS(l.node,n,o,this._resourceManager);m||([m]=as(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=as(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var $6="?tfjs-format=file",E6="model.json",fl=class{constructor(e,t={},o=La){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ku(ml.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=ml.Instance.transformGraph(e.modelInitializer);this.initializer=new ku(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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uj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;K([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,w=x-1-c.padInfo.top,k=le(a.shape,"float32"),_=1/(d*f),$=t.data.get(n.dataId).values,A=le(n.shape,"float32",$);for(let R=0;R=c.outHeight||Math.floor(q)!==q))for(let H=0;H=c.outWidth||Math.floor(j)!==j)continue;let X=A.get(R,q,j,D);V+=X}}k.set(V*_,R,P,M,D)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var i2={kernelName:wm,backendName:"cpu",kernelFunc:uj};function pj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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i=s.reduce((x,b)=>x*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Me({inputs:{x:n},backend:t,attrs:{shape:p}}),f=Ct({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Me({inputs:{x:f},backend:t,attrs:{shape:c}}),g=No({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var p2={kernelName:xs,backendName:"cpu",kernelFunc:cj};function lj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Kp(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var c2={kernelName:Ja,backendName:"cpu",kernelFunc:lj};function mj(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var l2={kernelName:up,backendName:"cpu",kernelFunc:mj};var dj=Ie(lo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return ar({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(w=>wo({inputs:{input:w},backend:t})),g=p.map(w=>Xs({inputs:{input:w},backend:t})),x=Pi({inputs:h,backend:t,attrs:{axis:s}}),b=Pi({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(w=>t.disposeIntermediateTensorInfo(w)),g.forEach(w=>t.disposeIntermediateTensorInfo(w)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Me({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=Su(c,i,e[0].dtype,l),d=S.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var h2={kernelName:ys,backendName:"cpu",kernelFunc:Pi};function NS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;K([n,s],"conv2d");let l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",w=new st(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],A=C?k[1]:k[2],R=C?k[2]:1,D=C?1:k[1],P=w.strides[0],M=C?w.strides[1]:w.strides[2],L=C?w.strides[2]:1,W=C?1:w.strides[1],V=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,q=w.values;for(let H=0;H=m.inHeight)continue;let pe=J*_[0],he=j+ie*A;for(let we=0;we=m.inWidth)continue;let pt=pe+Le*_[1],Oe=he+nt*R,mt=pt;for(let at=0;at=u.inDepth)continue;let H=U*R[0],j=P+q*A[1];for(let X=0;X=u.inHeight)continue;let ie=H+Y*R[1],pe=j+J*A[2];for(let he=0;he=u.inWidth)continue;let nt=ie+$e*R[2],pt=pe+Le*u.inChannels,Oe=nt;for(let mt=0;mtMath.cos(r)),w2={kernelName:qo,backendName:"cpu",kernelFunc:Cj};var Sj=Ie(Ko,r=>Math.cosh(r)),I2={kernelName:Ko,backendName:"cpu",kernelFunc:Sj};function wj(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=le([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let W=h>1?(P-R)*(l-1)/(h-1):0,V=g>1?(M-D)*(m-1)/(g-1):0;for(let U=0;U1?R*(l-1)+U*W:.5*(R+P)*(l-1);if(q<0||q>l-1){for(let H=0;H1?D*(m-1)+Z*V:.5*(D+M)*(m-1);if(ee<0||ee>m-1){for(let pe=0;pe1?D*(m-1)+H*V:.5*(D+M)*(m-1);if(j<0||j>m-1){for(let ee=0;eex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,w=b.top,k=d.outChannels/d.inChannels,_=new st(d.outShape,n.dtype),$=t.data.get(n.dataId).values,A=t.data.get(s.dataId).values,R=_.values;for(let D=0;D=d.inHeight)continue;let H=U*l[0],j=P+q*c[1];for(let X=0;X=d.inWidth)continue;let ie=H+Y*l[1],pe=j+J*d.inChannels,he=Z,we=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:w,strideWidth:k,filterHeight:_,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:D}=S.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),P=y.sizeFromShape(D),M=D.length,L=y.getArrayFromDType(o.dtype,P);for(let V=0;V=0&&J=0&&peZ&&(Z=ve)}}}let ee=y.locToIndex([V,U,H,X],M,y.computeStrides(D));L[ee]=Z}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),D,o.dtype),shape:D,dtype:o.dtype}}};var D2={kernelName:bb,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${bb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=ee,Z=J)}}}P[X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var O2={kernelName:yb,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${yb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=Y,Z=ie)}}}P[L][X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function La(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;K(n,"sum");let i;n.dtype==="bool"?i=Io({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=ar({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=S.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=Ct({inputs:{x:i},backend:t,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,p)),S.assertAxesAreInnerMostDims("sum",l,m.shape.length);let[d,f]=S.computeOutAndReduceShapes(m.shape,l),h=S.upcastType(m.dtype,"int32"),g=Hp(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let w=0;w=0&&(m=La({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var M2={kernelName:ri,backendName:"cpu",kernelFunc:$j};function Aj(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;K([o,n],"eluGrad");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=1?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var L2={kernelName:km,backendName:"cpu",kernelFunc:Aj};var Rj=S.ERF_P,Fj=S.ERF_A1,Dj=S.ERF_A2,Oj=S.ERF_A3,Pj=S.ERF_A4,Mj=S.ERF_A5,Lj=Ie(ma,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+Rj*t);return e*(1-((((Mj*o+Pj)*o+Oj)*o+Dj)*o+Fj)*o*Math.exp(-t*t))}),B2={kernelName:ma,backendName:"cpu",kernelFunc:Lj};function Jp(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Me({inputs:{x:n},backend:t,attrs:{shape:i}})}var V2={kernelName:bs,backendName:"cpu",kernelFunc:Jp};var Bj=Be((r,e)=>r/e),hl=Qe(Jo,Bj),gl={kernelName:Jo,backendName:"cpu",kernelFunc:hl};function vf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&CMath.floor(r/e)),qj=Qe(sn,Hj,null,"int32"),G2={kernelName:sn,backendName:"cpu",kernelFunc:qj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=NS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;if(c==="NCHW"&&a.shape.length===1&&a.shape[0]!==1){let x=Me({inputs:{x:a},backend:t,attrs:{shape:[a.shape[0],1,1]}});h=js({inputs:{a:h,b:x},backend:t}),t.disposeIntermediateTensorInfo(x)}else h=js({inputs:{a:h,b:a},backend:t});t.disposeIntermediateTensorInfo(g)}if(d){let g=h;if(c==="NCHW"&&d==="prelu"&&i.shape.length===1&&i.shape[0]!==1){let x=Me({inputs:{x:i},backend:t,attrs:{shape:[i.shape[0],1,1]}});h=_u(t,h,d,x,f),t.disposeIntermediateTensorInfo(x)}else h=_u(t,h,d,i,f);t.disposeIntermediateTensorInfo(g)}return h}var H2={kernelName:ho,backendName:"cpu",kernelFunc:Kj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=TS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;h=js({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(d){let g=h;h=_u(t,h,d,i,f),t.disposeIntermediateTensorInfo(g)}return h}var q2={kernelName:go,backendName:"cpu",kernelFunc:jj};function Xj(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=y.sizeFromShape(o.shape),a=n.shape,i=a[a.length-1],[p,u,c,l]=S.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(p,o.dtype,[]);let m=t.data.get(n.dataId).values,d=t.bufferSync(o),f=pf(m,d,o.dtype,u,i,c,l,o.shape,s);return t.makeTensorInfo(p,o.dtype,f.values)}var K2={kernelName:un,backendName:"cpu",kernelFunc:Xj};function Yj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o;K([n,s],"gatherV2");let p=y.parseAxisParam(a,n.shape)[0],u=t.data.get(s.dataId).values,c=n.shape[p];for(let w=0;w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=S.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Me({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Me({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=cf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var j2={kernelName:Ss,backendName:"cpu",kernelFunc:Yj};function Qj(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Me({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=vf(i,!0,t),u=Me({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var X2={kernelName:ni,backendName:"cpu",kernelFunc:Qj};var Zj=Ie(fa,r=>Number.isFinite(r)?1:0,"bool"),Y2={kernelName:fa,backendName:"cpu",kernelFunc:Zj};var 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i=s.reduce((x,b)=>x*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Le({inputs:{x:n},backend:t,attrs:{shape:p}}),f=St({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Le({inputs:{x:f},backend:t,attrs:{shape:c}}),g=To({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var h2={kernelName:bs,backendName:"cpu",kernelFunc:Ej};function Aj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=jp(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var g2={kernelName:ti,backendName:"cpu",kernelFunc:Aj};function Rj(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var x2={kernelName:fp,backendName:"cpu",kernelFunc:Rj};var Dj=we(lo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return ir({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(w=>Io({inputs:{input:w},backend:t})),g=p.map(w=>Ys({inputs:{input:w},backend:t})),x=Mi({inputs:h,backend:t,attrs:{axis:s}}),b=Mi({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(w=>t.disposeIntermediateTensorInfo(w)),g.forEach(w=>t.disposeIntermediateTensorInfo(w)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Le({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=Nu(c,i,e[0].dtype,l),d=S.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var S2={kernelName:Cs,backendName:"cpu",kernelFunc:Mi};function MS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;j([n,s],"conv2d");let l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",w=new st(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],A=C?k[1]:k[2],R=C?k[2]:1,D=C?1:k[1],P=w.strides[0],M=C?w.strides[1]:w.strides[2],L=C?w.strides[2]:1,V=C?1:w.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,K=w.values;for(let H=0;H=m.inHeight)continue;let ce=te*_[0],ye=q+ie*A;for(let Ne=0;Ne=m.inWidth)continue;let pt=ce+Re*_[1],ze=ye+ot*R,ct=pt;for(let at=0;at=u.inDepth)continue;let H=U*R[0],q=P+K*A[1];for(let Z=0;Z=u.inHeight)continue;let ie=H+J*R[1],ce=q+te*A[2];for(let ye=0;ye=u.inWidth)continue;let ot=ie+Ae*R[2],pt=ce+Re*u.inChannels,ze=ot;for(let ct=0;ctMath.cos(r)),_2={kernelName:Ho,backendName:"cpu",kernelFunc:Vj};var zj=we(Ko,r=>Math.cosh(r)),$2={kernelName:Ko,backendName:"cpu",kernelFunc:zj};function Wj(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=le([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let V=h>1?(P-R)*(l-1)/(h-1):0,z=g>1?(M-D)*(m-1)/(g-1):0;for(let U=0;U1?R*(l-1)+U*V:.5*(R+P)*(l-1);if(K<0||K>l-1){for(let H=0;H1?D*(m-1)+ee*z:.5*(D+M)*(m-1);if(oe<0||oe>m-1){for(let ce=0;ce1?D*(m-1)+H*z:.5*(D+M)*(m-1);if(q<0||q>m-1){for(let oe=0;oex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,w=b.top,k=d.outChannels/d.inChannels,_=new st(d.outShape,n.dtype),E=t.data.get(n.dataId).values,A=t.data.get(s.dataId).values,R=_.values;for(let D=0;D=d.inHeight)continue;let H=U*l[0],q=P+K*c[1];for(let Z=0;Z=d.inWidth)continue;let ie=H+J*l[1],ce=q+te*d.inChannels,ye=ee,Ne=ie;for(let Ie=0;Ie{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:w,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:A,dilationWidth:R,outShape:D}=S.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),P=y.sizeFromShape(D),M=D.length,L=y.getArrayFromDType(o.dtype,P);for(let z=0;z=0&&te=0&&ceee&&(ee=Ie)}}}let oe=y.locToIndex([z,U,H,Z],M,y.computeStrides(D));L[oe]=ee}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),D,o.dtype),shape:D,dtype:o.dtype}}};var V2={kernelName:$b,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${$b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&J=0&&ieq&&(q=ce,Z=oe,ee=te)}}}P[Z][ee][H]+=D[L][V][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var z2={kernelName:_b,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${_b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&J=0&&ieq&&(q=ce,Z=J,ee=ie)}}}P[L][Z][ee][H]+=D[L][V][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function za(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;j(n,"sum");let i;n.dtype==="bool"?i=vo({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=ir({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=S.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=St({inputs:{x:i},backend:t,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,p)),S.assertAxesAreInnerMostDims("sum",l,m.shape.length);let[d,f]=S.computeOutAndReduceShapes(m.shape,l),h=S.upcastType(m.dtype,"int32"),g=Kp(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let w=0;w=0&&(m=za({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var U2={kernelName:ii,backendName:"cpu",kernelFunc:Yj};function Qj(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;j([o,n],"eluGrad");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=1?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var G2={kernelName:Nm,backendName:"cpu",kernelFunc:Qj};var Zj=S.ERF_P,Jj=S.ERF_A1,eX=S.ERF_A2,tX=S.ERF_A3,rX=S.ERF_A4,oX=S.ERF_A5,nX=we(da,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+Zj*t);return e*(1-((((oX*o+rX)*o+tX)*o+eX)*o+Jj)*o*Math.exp(-t*t))}),H2={kernelName:da,backendName:"cpu",kernelFunc:nX};function ec(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Le({inputs:{x:n},backend:t,attrs:{shape:i}})}var K2={kernelName:Ss,backendName:"cpu",kernelFunc:ec};var sX=Be((r,e)=>r/e),yl=Qe(Zo,sX),bl={kernelName:Zo,backendName:"cpu",kernelFunc:yl};function vf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&CMath.floor(r/e)),mX=Qe(nn,lX,null,"int32"),Y2={kernelName:nn,backendName:"cpu",kernelFunc:mX};function dX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=MS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;if(c==="NCHW"&&a.shape.length===1&&a.shape[0]!==1){let x=Le({inputs:{x:a},backend:t,attrs:{shape:[a.shape[0],1,1]}});h=Xs({inputs:{a:h,b:x},backend:t}),t.disposeIntermediateTensorInfo(x)}else h=Xs({inputs:{a:h,b:a},backend:t});t.disposeIntermediateTensorInfo(g)}if(d){let g=h;if(c==="NCHW"&&d==="prelu"&&i.shape.length===1&&i.shape[0]!==1){let x=Le({inputs:{x:i},backend:t,attrs:{shape:[i.shape[0],1,1]}});h=Du(t,h,d,x,f),t.disposeIntermediateTensorInfo(x)}else h=Du(t,h,d,i,f);t.disposeIntermediateTensorInfo(g)}return h}var Q2={kernelName:go,backendName:"cpu",kernelFunc:dX};function fX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=LS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;h=Xs({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(d){let g=h;h=Du(t,h,d,i,f),t.disposeIntermediateTensorInfo(g)}return h}var Z2={kernelName:xo,backendName:"cpu",kernelFunc:fX};function hX(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=y.sizeFromShape(o.shape),a=n.shape,i=a[a.length-1],[p,u,c,l]=S.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(p,o.dtype,[]);let m=t.data.get(n.dataId).values,d=t.bufferSync(o),f=pf(m,d,o.dtype,u,i,c,l,o.shape,s);return t.makeTensorInfo(p,o.dtype,f.values)}var J2={kernelName:an,backendName:"cpu",kernelFunc:hX};function gX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o;j([n,s],"gatherV2");let p=y.parseAxisParam(a,n.shape)[0],u=t.data.get(s.dataId).values,c=n.shape[p];for(let w=0;w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=S.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Le({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Le({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=cf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var e_={kernelName:Is,backendName:"cpu",kernelFunc:gX};function xX(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Le({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=vf(i,!0,t),u=Le({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var t_={kernelName:pi,backendName:"cpu",kernelFunc:xX};var yX=we(ha,r=>Number.isFinite(r)?1:0,"bool"),r_={kernelName:ha,backendName:"cpu",kernelFunc:yX};var 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3:return d8(r,e);default:return h8(r,e)}}function V5(r,e,t=!1,o){let n="";t?n+=_E(r,o):n+=ic(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=b8(r,e):n+=C8(r,e)),n}function z5(r,e,t){switch(r.length){case 0:return EE();case 1:return Q5(r,e,t);case 2:return s8(r,e,t);case 3:return J5(r,e,t);default:return t8(r,e,t)}}function W5(r,e,t){switch(r.length){case 0:return EE();case 1:return Z5(r,e,t);case 2:return a8(r,e,t);case 3:return e8(r,e,t);case 4:return r8(r,e,t);case 5:return o8(r,e);case 6:return n8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function U5(r){return` +`;var{getBroadcastDims:R$}=S;function D$(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push("uniform int outShape;");break;case 2:o.push("uniform ivec2 outShape;"),o.push("uniform int outShapeStrides;");break;case 3:o.push("uniform ivec3 outShape;"),o.push("uniform ivec2 outShapeStrides;");break;case 4:o.push("uniform ivec4 outShape;"),o.push("uniform ivec3 outShapeStrides;");break;default:break}o.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let n=o.join(` +`),s=r.map(d=>a8(d,e,t.packedInputs,t.enableShapeUniforms)).join(` +`),a=e.texShape,i=wt(),p=p8(i),u,c,l=m8(i);return e.isPacked?(u=i8(e.logicalShape,a,t.enableShapeUniforms),c=l8(i)):(u=u8(e.logicalShape,a,t.enableShapeUniforms),c=c8(i)),t.packedInputs&&(l+=g8),[l,p,c,n,u,s,t.userCode].join(` +`)}function uc(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return _8(r,e);case 1:return E8(r,e);case 2:return R8(r,e);case 3:return F8(r,e);case 4:return P8(r,e);case 5:return M8(r);case 6:return L8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function F$(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return T8(r);case 1:return $8(r,e);case 2:return A8(r,e);case 3:return D8(r,e);default:return O8(r,e)}}function a8(r,e,t=!1,o){let n="";t?n+=F$(r,o):n+=uc(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=B8(r,e):n+=V8(r,e)),n}function i8(r,e,t){switch(r.length){case 0:return O$();case 1:return x8(r,e,t);case 2:return k8(r,e,t);case 3:return b8(r,e,t);default:return S8(r,e,t)}}function u8(r,e,t){switch(r.length){case 0:return O$();case 1:return y8(r,e,t);case 2:return N8(r,e,t);case 3:return C8(r,e,t);case 4:return w8(r,e,t);case 5:return I8(r,e);case 6:return v8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function p8(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } - `}function G5(r){return` + `}function c8(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } - `}function H5(r){return` + `}function l8(r){return` void setOutput(vec4 val) { ${r.output} = val; } - `}function q5(r){return`${r.version} + `}function m8(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; @@ -217,10 +217,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${K5} - ${j5} - ${X5} - `}var K5=` + ${d8} + ${f8} + ${h8} + `}var d8=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -232,7 +232,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,j5=` +`,f8=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -240,7 +240,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,X5=` +`,h8=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -249,7 +249,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Y5=` +`,g8=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -260,11 +260,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function EE(){return` +`;function O$(){return` int getOutputCoords() { return 0; } - `}function Q5(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?` + `}function x8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -293,7 +293,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${o[0]}, ${o[1]})); return 2 * (resTexRC.x * ${o[1]} + resTexRC.y); } - `}function Z5(r,e,t){return e[0]===1?t?` + `}function y8(r,e,t){return e[0]===1?t?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -321,7 +321,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${e[0]}, ${e[1]})); return resTexRC.x * ${e[1]} + resTexRC.y; } - `}function J5(r,e,t){if(t)return` + `}function b8(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -352,15 +352,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function e8(r,e,t){if(t)return` + `}function C8(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Au(["r","c","d"],r)} + ${Pu(["r","c","d"],r)} return ivec3(r, c, d); } -`;let o=us(["r","c","d"],r);return` +`;let o=ps(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -368,7 +368,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${o} return ivec3(r, c, d); } - `}function t8(r,e,t){if(t)return` + `}function S8(r,e,t){if(t)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -409,15 +409,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${r.length}(${p}); } - `}function r8(r,e,t){if(t)return` + `}function w8(r,e,t){if(t)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Au(["r","c","d","d2"],r)} + ${Pu(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } - `;let o=us(["r","c","d","d2"],r);return` + `;let o=ps(["r","c","d","d2"],r);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${o} return ivec4(r, c, d, d2); } - `}function o8(r,e){let t=us(["r","c","d","d2","d3"],r);return` + `}function I8(r,e){let t=ps(["r","c","d","d2","d3"],r);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function n8(r,e){let t=us(["r","c","d","d2","d3","d4"],r);return` + `}function v8(r,e){let t=ps(["r","c","d","d2","d3","d4"],r);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); @@ -448,7 +448,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function s8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?` + `}function k8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -481,7 +481,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function a8(r,e,t){return y.arraysEqual(r,e)?t?` + `}function N8(r,e,t){return y.arraysEqual(r,e)?t?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -535,15 +535,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function Ru(r){return`offset${r}`}function i8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=St();return` + `}function Mu(r){return`offset${r}`}function T8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=wt();return` vec4 ${t}() { return ${o.texture2D}(${e}, halfCR); } - `}function u8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return` + `}function _8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return` float ${o}() { return sampleTexture(${t}, halfCR); } - `;let a=Ru(t);if(e)return` + `;let a=Mu(t);if(e)return` float ${o}() { vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a}); return sampleTexture(${t}, uv); @@ -553,7 +553,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${i}, ${p}, ${a}); return sampleTexture(${t}, uv); } - `}function p8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=St();if(e)return` + `}function $8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=wt();if(e)return` vec4 ${o}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -566,15 +566,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a[0]}, ${a[1]}, index); return ${s.texture2D}(${t}, uv); } - `}function c8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return` + `}function E8(r,e){let t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return` float ${o}(int index) { - ${uc(r)} + ${pc(r)} } `;let n=r.shapeInfo.texShape,s=n[0],a=n[1];if(a===1&&s===1)return` float ${o}(int index) { return sampleTexture(${t}, halfCR); } - `;let i=Ru(t);return a===1?e?` + `;let i=Mu(t);return a===1?e?` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0])); return sampleTexture(${t}, uv); @@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${a}, index + ${i}); return sampleTexture(${t}, uv); } - `}function l8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=St();if(s!=null&&y.arraysEqual(t,s))return e?` + `}function A8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=wt();if(s!=null&&y.arraysEqual(t,s))return e?` vec4 ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); @@ -628,7 +628,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col); return ${p.texture2D}(${o}, uv); } - `}function m8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return` + `}function R8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return` float ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); return sampleTexture(${o}, uv); @@ -638,17 +638,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0); return sampleTexture(${o}, uv); } - `}let{newShape:a,keptDims:i}=y.squeezeShape(t),p=a;if(p.length=1?c="coords = 0;":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(` + `}function B8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=R$(r.shapeInfo.logicalShape,e.logicalShape),p=$e(a),u=a-s,c,l=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(` `);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,C)=>`coords.${l[C+u]}`).join(", ");let d="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)d=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)a===1?d=` @@ -965,20 +965,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${o}(${m}); ${d} } - `}function C8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return` + `}function V8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return` float ${n}() { return sampleTexture(${t}, resultUV); } - `;let u=_e(p),c=NE(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=["x","y","z","w","u","v"];i===0?m="":p<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(` + `;let u=$e(p),c=R$(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=["x","y","z","w","u","v"];i===0?m="":p<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(` `);let f="";return p<2&&i>0?f="coords":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(", "),` float ${n}() { ${u} coords = getOutputCoords(); 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p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Of(r.packedInputs,a.shape,p),m="",d="",f="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=S.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),w=r.packedInputs||c.length>2?"":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`}else{let p=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n+`${O().getNumber("WEBGL_VERSION")}`,s}function lt(r){return O().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Pf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Au(["r","c","d"],e):us(["r","c","d"],e)} + ${this.enableShapeUniforms?Pu(["r","c","d"],e):ps(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}};var Mf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Mi.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` + `}};var Mf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Au(["r","c","d"],e):us(["r","c","d"],e)} + ${this.enableShapeUniforms?Pu(["r","c","d"],e):ps(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1016,26 +1016,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}};var Lf=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=` - ${Df} + `}};var Lf=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=` + ${Ff} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}};var 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if(O().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(o="EXT_color_buffer_float",Wr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=fw(this.gl),this.indexBuffer=hw(this.gl),this.framebuffer=tw(this.gl),this.textureConfig=vl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),gw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),xw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),yw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Iw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),ww(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),bw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Nw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return Tw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=vw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>_w(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=dw(t));let o=XS(t);pe(t,()=>t.attachShader(o,this.vertexShader)),pe(t,()=>t.attachShader(o,e)),YS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),pe(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(Sw(t,n,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&kl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(pe(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&kl(this.gl,this.program)),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?rw(this.gl,e,t):ow(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),nw(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Qs(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&kl(this.gl,this.program),rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=tc(this.gl,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=G8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;"setTimeoutCustom"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Nl(this.gl,e,this.framebuffer),this.debug&&rc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Nl(this.gl,this.outputTexture,this.framebuffer),this.debug&&rc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Nl(n,e,this.framebuffer),this.debug&&rc(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,o)),pe(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function G8(r){let e=0;for(;e`${r}.${t}`)}function Et(r,e){return e===1?[r]:Ew(r,e)}function $E(r,e){if(r===1)return"rc";let t="";for(let o=0;o= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}};var lc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}};var mc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` ${s} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1146,8 +1146,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${n>0?"}":""} `}this.userCode=` - ${k8(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?sc():nc(e)} + ${H8(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?ac():sc(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1162,12 +1162,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function k8(r,e){return` + `}};function H8(r,e){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${e?kE(["r","c","d"],"inputShape"):us(["r","c","d"],r)} + ${e?A$(["r","c","d"],"inputShape"):ps(["r","c","d"],r)} return ivec3(r, c, d); } - `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=k$(t,o),s=N$(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=v$(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=k$(o,n),a=N$(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=v$(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function N8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function v$(r,e,t,o,n){let s=T8(e,o),a;if(n){let[p,u]=Ys(r[0],r[1]);a=p*u}else{let[p,u]=$u(r[0],r[1]);a=p*u}let i=N8(t,s);return a*i}function T8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function _8(r){return O().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function k$(r,e){if(r===ir.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ir.RENDER||r==null)return _8(e);if(r===ir.DOWNLOAD||r===ir.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function N$(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` + `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=AE(t,o),s=RE(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=EE(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=AE(o,n),a=RE(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=EE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function K8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function EE(r,e,t,o,n){let s=q8(e,o),a;if(n){let[p,u]=Qs(r[0],r[1]);a=p*u}else{let[p,u]=Ou(r[0],r[1]);a=p*u}let i=K8(t,s);return a*i}function q8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function j8(r){return O().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function AE(r,e){if(r===ur.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ur.RENDER||r==null)return j8(e);if(r===ur.DOWNLOAD||r===ur.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function RE(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,11 +1178,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Bt="if (isnan(x)) return x;",T$="return x;",Cw="return abs(x);";var _$="return (x >= 0.0) ? x : (exp(x) - 1.0);",E$=Bt+` + `}},zt="if (isnan(x)) return x;",DE="return x;",Aw="return abs(x);";var FE="return (x >= 0.0) ? x : (exp(x) - 1.0);",OE=zt+` return (x < 0.0) ? 0.0 : x; -`,$$=Bt+` +`,PE=zt+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Qs="return x;",A$="return 1.0 / (1.0 + exp(-1.0 * x));";var F$="return x;",D$=` +`,Zs="return x;",ME="return 1.0 / (1.0 + exp(-1.0 * x));";var BE="return x;",VE=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,O$=` +`,zE=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1201,7 +1201,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,P$=` +`,WE=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1211,7 +1211,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,M$="return 1.0 / (1.0 + exp(-1.0 * x));",Ar=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` +`,UE="return 1.0 / (1.0 + exp(-1.0 * x));",Er=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1222,17 +1222,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var Qf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let t=e.length,o=$t("rc",t),n=_e(t),s=I$(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` + `}};var Qf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.length,o=Et("rc",t),n=$e(t),s=$E(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } - `}};var $8=Lt.whereImpl,A8=1e-7,R8=1e-4,Zf={};function F8(r){return r in Zf||(Zf[r]={}),Zf[r]}var D8=O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),O8=600;function P8(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*O8/1024/1024}var Bi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Fu)t=e;else{let o=Wr(O().getNumber("WEBGL_VERSION"),e);t=new Fu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Wr(O().getNumber("WEBGL_VERSION"));t=new Fu(o),this.binaryCache=F8(O().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=P8(),this.texData=new Do(this,cr())}nextDataId(){return Bi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=rc(t),c=new vl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||O().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ir.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ir.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Qs):m=new Jt(i,Qs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Qs):f=new Jt(n,Qs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool("DEBUG")&&!O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&O().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&O().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Cl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Ar(s,Qs):d=new Jt(s,Qs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=cr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D8){return O().getBool("WEBGL_CPU_FORWARD")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Va(e.shape),...za(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Va(t),...za(t)],a=new lc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=rc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Cl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Mi.DENSE){let x=a!=null?a:Cl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Li(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=FE(e,c,l),d=this.getAndSaveBinary(m,()=>AE(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get("ENGINE_COMPILE_ONLY")||RE(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=O().getBool("DEBUG");O().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(O().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=jS(o,p),t.texShape=l),s!=null){let m=rc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ys(l[0],l[1])),p?d=new Vf(m,g):d=new vl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ir.PIXELS:C.usage=ir.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,O().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=M8(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await CC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?($f(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=tw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromTexture(e,t,o){let{texture:n,height:s,width:a,channels:i}=e,p=cr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,t,o,s,a,i);return cr().makeTensorFromDataId(u,t,o,p)}};Bi.nextDataId=0;function M8(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Bi,2);var L9e={forceHalfFloat:L$};var mc=` + `}};var Y8=Vt.whereImpl,Q8=1e-7,Z8=1e-4,Zf={};function J8(r){return r in Zf||(Zf[r]={}),Zf[r]}var eY=O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),tY=600;function rY(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*tY/1024/1024}var Vi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Lu)t=e;else{let o=zr(O().getNumber("WEBGL_VERSION"),e);t=new Lu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=zr(O().getNumber("WEBGL_VERSION"));t=new Lu(o),this.binaryCache=J8(O().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=rY(),this.texData=new Fo(this,sr())}nextDataId(){return Vi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=oc(t),c=new Tl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||O().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ur.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Er(i,Zs):m=new Jt(i,Zs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Er(n,Zs):f=new Jt(n,Zs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool("DEBUG")&&!O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&O().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&O().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Il(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;pe(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Er(s,Zs):d=new Jt(s,Zs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=sr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=eY){return O().getBool("WEBGL_CPU_FORWARD")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ua(e.shape),...Ga(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ua(t),...Ga(t)],a=new mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=oc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Il(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Li.DENSE){let x=a!=null?a:Il(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Bi(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=B$(e,c,l),d=this.getAndSaveBinary(m,()=>M$(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get("ENGINE_COMPILE_ONLY")||L$(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=O().getBool("DEBUG");O().set("DEBUG",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(O().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Q8:Z8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=sw(o,p),t.texShape=l),s!=null){let m=oc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Qs(l[0],l[1])),p?d=new Vf(m,g):d=new Tl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ur.PIXELS:C.usage=ur.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,O().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=oY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await AC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Ef(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=mw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromGPUData(e,t,o){e.channels=e.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=e,p=sr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,t,o,s,a,i);return sr().makeTensorFromDataId(u,t,o,p)}};Vi.nextDataId=0;function oY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Vi,2);var CJe={forceHalfFloat:GE};var dc=` if (isnan(a)) return a; if (isnan(b)) return b; -`;var io=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` +`;var io=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,17 +1242,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var Zs=` + `}};var Js=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`;var To=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ct(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` +`;var _o=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=lt(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` - ${_e(s)} coords = getOutputCoords(); + ${$e(s)} coords = getOutputCoords(); `,s===1)this.enableShapeUniforms?a+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; @@ -1261,7 +1261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let p=$t("coords",s);this.enableShapeUniforms?a+=` + `;else{let p=Et("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var B$={kernelName:mo,backendName:"webgl",kernelFunc:At};function Rr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var V$={kernelName:ei,backendName:"webgl",kernelFunc:Rr};var Sw="return (a < 0.) ? b * a : a;",ww=` + `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HE={kernelName:mo,backendName:"webgl",kernelFunc:At};function Ar(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KE={kernelName:ri,backendName:"webgl",kernelFunc:Ar};var Rw="return (a < 0.) ? b * a : a;",Dw=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(ww,n.shape,a.shape):new io(Sw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],"float32");return t.disposeIntermediateTensorInfo(a),p}var z$={kernelName:mn,backendName:"webgl",kernelFunc:B8};var Iw="return (a < 0.) ? b * a : a;",vw=` +`;function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _o(Dw,n.shape,a.shape):new io(Rw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],"float32");return t.disposeIntermediateTensorInfo(a),p}var qE={kernelName:ln,backendName:"webgl",kernelFunc:sY};var Fw="return (a < 0.) ? b * a : a;",Ow=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function V8(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(vw,o.shape,n.shape):new io(Iw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],"float32")}var W$={kernelName:Rn,backendName:"webgl",kernelFunc:V8};var _o="if (isnan(x)) return x;";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,$],dt(w.dtype,k.dtype))}),b=Rr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?S.fromUint8ToStringArray(f):f,x=p.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,d;return m?d=new To(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Wa(r,e=!1){if(r==="linear")return e?F$:T$;if(r==="relu")return e?O$:E$;if(r==="elu")return e?D$:_$;if(r==="relu6")return e?P$:$$;if(r==="prelu")return e?vw:Iw;if(r==="leakyrelu")return e?ww:Sw;if(r==="sigmoid")return e?M$:A$;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var dc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ct(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { +`;function aY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _o(Ow,o.shape,n.shape):new io(Fw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],"float32")}var jE={kernelName:Rn,backendName:"webgl",kernelFunc:aY};var $o="if (isnan(x)) return x;";function he({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Er(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,E],dt(w.dtype,k.dtype))}),b=Ar({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?S.fromUint8ToStringArray(f):f,x=p.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,d;return m?d=new _o(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ha(r,e=!1){if(r==="linear")return e?BE:DE;if(r==="relu")return e?zE:OE;if(r==="elu")return e?VE:FE;if(r==="relu6")return e?WE:PE;if(r==="prelu")return e?Ow:Fw;if(r==="leakyrelu")return e?Dw:Rw;if(r==="sigmoid")return e?UE:ME;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var fc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=lt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { @@ -1305,16 +1305,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i} }`:g=`vec4 activation(vec4 x) { ${i} - }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let C="rc.x",w="rc.x";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Li(n.shape,p)&&!(c.texture!==null&&Li(c.shape,p))?H$(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var q$={kernelName:Ns,backendName:"webgl",kernelFunc:te};var _l=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` + `}};var XE="return a * b;";function El(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=S.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),u=new $l(Pw.REAL,o.shape,n.shape),c=new $l(Pw.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Ar({inputs:{real:m,imag:d},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),[u,c]=aE(o.shape,n.shape,i.values,p.values,s),l=t.makeTensorInfo(c,s),m=t.texData.get(l.dataId);return m.values=u,l}let a;return O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new _o(XE,o.shape,n.shape):a=new io(XE,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var YE={kernelName:kn,backendName:"webgl",kernelFunc:El};function QE(r,e,t){let o=[Ua(r.shape),...Ga(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Ua(e),...Ga(e)],a=new mc(s,o),i=!0,p=[o],u=t.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function re(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Bi(n.shape,p)&&!(c.texture!==null&&Bi(c.shape,p))?QE(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZE={kernelName:_s,backendName:"webgl",kernelFunc:re};var Al=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${u}); } - `}};function W8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Gr(r,e,t,o){let n=W8(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=_e(this.rank),s=bw("rc",this.rank),a=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=$e(this.rank),s=Ew("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),A=te({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,W=p==="leakyrelu",V=p!=null?Wa(p,!0):null,U=M||L||W||V!=null,q;if((d===1||f===1)&&P>Nw&&U===!1){let j=$,X=A;t&&(j=xt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),R.push(j)),o&&(X=xt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(X));let Z=f!==1,ee=f===1,Y=j;Z&&(Y=te({inputs:{x:j},backend:n,attrs:{shape:[D,P,1]}}),R.push(Y));let J=f===1?2:1,ie=X;ee&&(ie=te({inputs:{x:X},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let pe=Tl({inputs:{a:Y,b:ie},backend:n});q=Ou({inputs:{x:pe},backend:n,attrs:{axis:J,keepDims:!0}}),R.push(pe)}else{let j=dt(r.dtype,e.dtype),X=new dc(k,_,[D,d,f],t,o,M,V,L,W),Z=[$,A];if(s!=null&&Z.push(s),L&&Z.push(a),W){let ee=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));Z.push(ee),R.push(ee)}q=n.runWebGLProgram(X,Z,j)}let H=te({inputs:{x:q},backend:n,attrs:{shape:w}});R.push(q);for(let j of R)n.disposeIntermediateTensorInfo(j);return H}function G8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Pu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var Y$={kernelName:fo,backendName:"webgl",kernelFunc:G8};var Q$="return abs(x);";function H8(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Kf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,Q$):n=new Jt(o.shape,Q$),t.runWebGLProgram(n,[o],o.dtype)}var Z$={kernelName:gs,backendName:"webgl",kernelFunc:H8};var q8=Bt+` + `}};function zi(r,e,t){let o=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new th(r.shape,e):new eh(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function JE(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,p=S.getAxesPermutation(i,s),u=p!=null,c=r;u&&(c=zi(r,p,o),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims("sum",i,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,i),d=l;t&&(d=S.expandShapeToKeepDim(l,a));let f=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/f,x=re({inputs:{x:c},attrs:{shape:[g,f]},backend:o}),b=Ta(r.dtype),C=Ur(x,b,"sum",o),w=re({inputs:{x:C},attrs:{shape:d},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(C),u&&o.disposeIntermediateTensorInfo(c),w}function Vu(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return JE(n,s,a,t)}var eA={kernelName:Kn,backendName:"webgl",kernelFunc:Vu};function yt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and 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b;",gY=tt({opSnippet:iA,packedOpSnippet:iA,supportsComplex:!0,cpuKernelImpl:V$}),uA={kernelName:eo,backendName:"webgl",kernelFunc:gY};var rh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back vec4 result = ${n}; setOutput(result); } - `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool("WEBGL_PACK")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var oA={kernelName:Mo,backendName:"webgl",kernelFunc:nh};function Q8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,"all",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Lo,backendName:"webgl",kernelFunc:Q8};function Z8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,"any",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Bo,backendName:"webgl",kernelFunc:Z8};var sh=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool("WEBGL_PACK")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Po,backendName:"webgl",kernelFunc:nh};function xY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=yt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=re({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Ur(h,h.dtype,"all",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=re({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=re({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Mo,backendName:"webgl",kernelFunc:xY};function yY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=yt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=re({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Ur(h,h.dtype,"any",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=re({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=re({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var lA={kernelName:Lo,backendName:"webgl",kernelFunc:yY};var sh=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back } setOutput(float(bestIndex)); } - `}};var ah=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=_e(p),c=$t("coords",p),l,m;if(a===1){m=p+1;let A=_e(m);l=` + `}};var ah=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=$e(p),c=Et("coords",p),l,m;if(a===1){m=p+1;let A=$e(m);l=` ${A} sourceLocR = ${A}(${c.join()}, 0); ++${c[p-1]}; ${A} sourceLocG = ${A}(${c.join()}, 0); @@ -1575,7 +1575,7 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back ${u} sourceLocA = coords; --${c[p-1]}; ${u} sourceLocB = coords; - --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(A=>"int "+A),g=$t("sourceLocR",m-1).concat("inIdx.r"),x=$t("sourceLocG",m-1).concat("inIdx.g"),b=$t("sourceLocB",m-1).concat("inIdx.b"),C=$t("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",k=n?"":` + --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(A=>"int "+A),g=Et("sourceLocR",m-1).concat("inIdx.r"),x=Et("sourceLocG",m-1).concat("inIdx.g"),b=Et("sourceLocB",m-1).concat("inIdx.b"),C=Et("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), @@ -1583,7 +1583,7 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,$=n?"":` + hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); @@ -1592,7 +1592,7 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } - ${$} + ${E} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[p-1]} < ${i[p-1]-1}; @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,back } setOutput(bestIndex); } - `}};function aA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=aA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function 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CY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=ih(t,p,a[0],"min");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:ei,backendName:"webgl",kernelFunc:CY};var SY=zt+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,rY=ge({opSnippet:tY}),cA={kernelName:ia,backendName:"webgl",kernelFunc:rY};var oY=Bt+"return log(x + sqrt(x * x + 1.0));",nY=ge({opSnippet:oY}),lA={kernelName:ua,backendName:"webgl",kernelFunc:nY};var sY=Bt+` +`,wY=he({opSnippet:SY}),gA={kernelName:ua,backendName:"webgl",kernelFunc:wY};var IY=zt+"return log(x + sqrt(x * x + 1.0));",vY=he({opSnippet:IY}),xA={kernelName:pa,backendName:"webgl",kernelFunc:vY};var kY=zt+` return atan(x); 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a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let A=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,AY=he({opSnippet:EY}),CA={kernelName:la,backendName:"webgl",kernelFunc:AY};var cs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let A=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); @@ -1690,7 +1690,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let C="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,_=a%4,$=` + `;return}let C="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / max(count, 1.0)");let k=Math.floor(a/4)*4,_=a%4,E=` if (${h}) { avgValue += dot(values, ones); } else { @@ -1745,7 +1745,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam getValue(batch, xR, xC + 3 * ${c}, d) ); - ${$} + ${E} } int xC = xCCorner + ${k}; @@ -1757,7 +1757,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam initializationValue ); - ${$} + ${E} } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -1766,7 +1766,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam initializationValue ); - ${$} + ${E} } else if (${_===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), @@ -1775,12 +1775,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam initializationValue ); - ${$} + ${E} } } setOutput(${w}); } - `}},zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",w="0.0";if(C||(w="-1.0 / 1e-20"),o){let D=">=";this.userCode=` + `}},Wi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",w="0.0";if(C||(w="-1.0 / 1e-20"),o){let D=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let k="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let $=Math.floor(a/4)*4,A=a%4,R=` + `;return}let k="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / max(count, 1.0)");let E=Math.floor(a/4)*4,A=a%4,R=` if (${C}) { avgValue += dot(values, ones); } else { @@ -1897,7 +1897,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam continue; } - for (int wC = 0; wC < ${$}; wC += 4) { + for (int wC = 0; wC < ${E}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( @@ -1910,7 +1910,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${R} } - int xC = xCCorner + ${$}; + int xC = xCCorner + ${E}; if (${A===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1940,10 +1940,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${R} } } - setOutput(${_}); } + setOutput(${_}); } - `}};function mY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;is(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,"avg",!1);return t.runWebGLProgram(l,[n],"float32")}var hA={kernelName:zo,backendName:"webgl",kernelFunc:mY};function dY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new zi(l,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var gA={kernelName:ip,backendName:"webgl",kernelFunc:dY};var uh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` + `}};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,"avg",!1);return t.runWebGLProgram(l,[n],"float32")}var SA={kernelName:Vo,backendName:"webgl",kernelFunc:RY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new Wi(l,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var wA={kernelName:dp,backendName:"webgl",kernelFunc:DY};var uh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(dotProd); } - `}};function fY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Im,backendName:"webgl",kernelFunc:fY};function hY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;is([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:wm,backendName:"webgl",kernelFunc:hY};function gY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Pu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var bA={kernelName:Wo,backendName:"webgl",kernelFunc:gY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:vm,backendName:"webgl",kernelFunc:FY};function OY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;us([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:mp,backendName:"webgl",kernelFunc:OY};function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return zu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:zo,backendName:"webgl",kernelFunc:PY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam setOutput((x - mean) * inv + offset); } - `}};var xY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool("WEBGL_PACK_NORMALIZATION")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:an,backendName:"webgl",kernelFunc:xY};var mh=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=_e(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=yY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Tw[i]} = start[${i}] + coords.${Tw[i]};`);n=` + `}};var MY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool("WEBGL_PACK_NORMALIZATION")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:sn,backendName:"webgl",kernelFunc:MY};var mh=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=$e(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=LY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Lw[i]} = start[${i}] + coords.${Lw[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${n} setOutput(getSource(${o})); } - `}},Tw=["x","y","z","w","u","v"];function yY(r){if(r===1)return"sourceLoc";if(r<=6)return Tw.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=_e(this.rank),o=$t("coords",this.rank),n=$t("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` + `}},Lw=["x","y","z","w","u","v"];function LY(r){if(r===1)return"sourceLoc";if(r<=6)return Lw.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=$e(this.rank),o=Et("coords",this.rank),n=Et("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${p} setOutput(result); } - `}};function 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kh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` + `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Mw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Bi(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let A=zu({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let 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k=re({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],"float32",E),R=re({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i==="leakyrelu",L=i?Ha(i,!0):null,V=new fc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),z=f?[R,k]:[k,R];if(n&&z.push(n),P&&z.push(s),M){let H=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(H),w.push(H)}let U=o.runWebGLProgram(V,z,"float32"),K=re({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return K}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l==="channelsLast"&&O().getBool("WEBGL_EXP_CONV")){let h=new xc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],"float32",g)}else if(O().getBool("WEBGL_CONV_IM2COL"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new gc(m);d=t.runWebGLProgram(h,[n,s],"float32")}let f=re({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var WA={kernelName:Uo,backendName:"webgl",kernelFunc:XY};var kh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2843,12 +2843,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(dotProd); } - `}};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],"float32")}var MA={kernelName:cp,backendName:"webgl",kernelFunc:$Y};function AY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],"float32")}var LA={kernelName:Ho,backendName:"webgl",kernelFunc:AY};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],"float32")}var BA={kernelName:lp,backendName:"webgl",kernelFunc:RY};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:vm,backendName:"webgl",kernelFunc:FY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:mp,backendName:"webgl",kernelFunc:DY};var OY=_o+` + `}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],"float32")}var UA={kernelName:oi,backendName:"webgl",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],"float32")}var GA={kernelName:Go,backendName:"webgl",kernelFunc:QY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],"float32")}var HA={kernelName:gp,backendName:"webgl",kernelFunc:ZY};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],"float32")}var KA={kernelName:km,backendName:"webgl",kernelFunc:JY};function eQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],"float32")}var qA={kernelName:xp,backendName:"webgl",kernelFunc:eQ};var tQ=$o+` return cos(x); -`,PY=ge({opSnippet:OY}),WA={kernelName:qo,backendName:"webgl",kernelFunc:PY};var MY=` +`,rQ=he({opSnippet:tQ}),jA={kernelName:Ho,backendName:"webgl",kernelFunc:rQ};var oQ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,LY=ge({opSnippet:MY}),UA={kernelName:Ko,backendName:"webgl",kernelFunc:LY};var Eh=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,nQ=he({opSnippet:oQ}),XA={kernelName:Ko,backendName:"webgl",kernelFunc:nQ};var $h=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${C}); void main() { @@ -2909,20 +2909,20 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${this.op}= getX(${QA(s,"coords",this.op)}); } setOutput(val); } - `}};function HA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function qA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function $h(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=xt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new El(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new 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Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ti,backendName:"webgl",kernelFunc:WY};var Ah=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=` + `}};function QA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Eh(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=yt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Rl(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new Rl(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=yt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function aQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Prod,n,t,s,a,i)}var JA={kernelName:qo,backendName:"webgl",kernelFunc:aQ};function iQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Sum,n,t,s,a,i)}var eR={kernelName:jo,backendName:"webgl",kernelFunc:iQ};function uQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Kf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=z$(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var tR={kernelName:ni,backendName:"webgl",kernelFunc:uQ};var Ah=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2941,7 +2941,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:Qo,backendName:"webgl",kernelFunc:UY};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",c="";o&&(n?u=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function pQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var rR={kernelName:Yo,backendName:"webgl",kernelFunc:pQ};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",c="";o&&(n?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?u=`float activation(float a) { @@ -2994,7 +2994,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${c} setOutput(result); } - `}};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=` + `}};var bc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new yc(l):m=new xc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var QA={kernelName:Zo,backendName:"webgl",kernelFunc:GY};var Rh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=` + `}};function cQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new bc(l):m=new yc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var oR={kernelName:Qo,backendName:"webgl",kernelFunc:cQ};var Rh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3218,7 +3218,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(dotProd); } - `}},Fh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` + `}},Dh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${i}); void main() { @@ -3263,13 +3263,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(dotProd); } - `}};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],"float32")}var ZA={kernelName:dp,backendName:"webgl",kernelFunc:HY};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Fh(l);return t.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:fp,backendName:"webgl",kernelFunc:qY};var Dh=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function lQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],"float32")}var nR={kernelName:yp,backendName:"webgl",kernelFunc:lQ};function mQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Dh(l);return t.runWebGLProgram(m,[n,s],"float32")}var sR={kernelName:bp,backendName:"webgl",kernelFunc:mQ};var Fh=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function KY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var eR={kernelName:hp,backendName:"webgl",kernelFunc:KY};var Oh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=` + `}};function dQ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=re({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Fh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=re({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var aR={kernelName:si,backendName:"webgl",kernelFunc:dQ};var Oh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); const float neg_infinity = -3.4e38; @@ -3307,7 +3307,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam float result = curVal; setOutput(result); } - `}};function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var tR={kernelName:gp,backendName:"webgl",kernelFunc:jY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ou({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var rR={kernelName:ri,backendName:"webgl",kernelFunc:XY};var YY="return (x >= 0.0) ? x : (exp(x) - 1.0);",QY=` + `}};function fQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],"float32");let m=re({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var iR={kernelName:ai,backendName:"webgl",kernelFunc:fQ};function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Vu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var uR={kernelName:ii,backendName:"webgl",kernelFunc:hQ};var gQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",xQ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3316,12 +3316,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,ZY=ge({opSnippet:YY,packedOpSnippet:QY}),oR={kernelName:en,backendName:"webgl",kernelFunc:ZY};var JY="return (b >= 1.0) ? a : a * (b + 1.0);",eQ=` +`,yQ=he({opSnippet:gQ,packedOpSnippet:xQ}),pR={kernelName:Jo,backendName:"webgl",kernelFunc:yQ};var bQ="return (b >= 1.0) ? a : a * (b + 1.0);",CQ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,tQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(eQ,o.shape,n.shape):new io(JY,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},nR={kernelName:km,backendName:"webgl",kernelFunc:tQ};var rQ=` +`,SQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _o(CQ,o.shape,n.shape):new io(bQ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},cR={kernelName:Nm,backendName:"webgl",kernelFunc:SQ};var wQ=` return vec4(equal(a, b)); -`,oQ="return float(a == b);",nQ=tt({opSnippet:oQ,packedOpSnippet:rQ,dtype:"bool",cpuKernelImpl:BE}),sR={kernelName:tn,backendName:"webgl",kernelFunc:nQ};var sQ=` +`,IQ="return float(a == b);",vQ=tt({opSnippet:IQ,packedOpSnippet:wQ,dtype:"bool",cpuKernelImpl:H$}),lR={kernelName:en,backendName:"webgl",kernelFunc:vQ};var kQ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3336,9 +3336,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,aQ=ge({opSnippet:sQ}),aR={kernelName:ma,backendName:"webgl",kernelFunc:aQ};var iQ=_o+` +`,NQ=he({opSnippet:kQ}),mR={kernelName:da,backendName:"webgl",kernelFunc:NQ};var TQ=$o+` return exp(x); -`,uQ=` +`,_Q=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3347,7 +3347,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,Aw=ge({opSnippet:iQ,packedOpSnippet:uQ,cpuKernelImpl:VE,dtype:"float32"}),iR={kernelName:rn,backendName:"webgl",kernelFunc:Aw};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uR={kernelName:bs,backendName:"webgl",kernelFunc:Ph};var pR="return exp(x) - 1.0;",pQ=ge({opSnippet:pR,packedOpSnippet:pR,cpuKernelImpl:zE}),cR={kernelName:da,backendName:"webgl",kernelFunc:pQ};var $l=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,Ww=he({opSnippet:TQ,packedOpSnippet:_Q,cpuKernelImpl:K$,dtype:"float32"}),dR={kernelName:tn,backendName:"webgl",kernelFunc:Ww};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),re({inputs:{x:s},backend:o,attrs:{shape:i}})}var fR={kernelName:Ss,backendName:"webgl",kernelFunc:Ph};var hR="return exp(x) - 1.0;",$Q=he({opSnippet:hR,packedOpSnippet:hR,cpuKernelImpl:q$}),gR={kernelName:fa,backendName:"webgl",kernelFunc:$Q};var Dl=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3380,12 +3380,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new $l("real",p,e),c=new $l("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Rr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function cQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var lR={kernelName:oi,backendName:"webgl",kernelFunc:cQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=re({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new Dl("real",p,e),c=new Dl("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Ar({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=re({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function EQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var xR={kernelName:ui,backendName:"webgl",kernelFunc:EQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Ga(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var mR={kernelName:Cs,backendName:"webgl",kernelFunc:Ga};var Bh=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function qa(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var yR={kernelName:ws,backendName:"webgl",kernelFunc:qa};var Bh=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3399,7 +3399,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } setOutput(outputValue); } - `}};var dR={kernelName:on,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var fR="return floor(x);",lQ=ge({opSnippet:fR,packedOpSnippet:fR,cpuKernelImpl:WE}),hR={kernelName:nn,backendName:"webgl",kernelFunc:lQ};var mQ=` + `}};var bR={kernelName:rn,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var CR="return floor(x);",AQ=he({opSnippet:CR,packedOpSnippet:CR,cpuKernelImpl:j$}),SR={kernelName:on,backendName:"webgl",kernelFunc:AQ};var RQ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3409,7 +3409,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam } else { return NAN; } -`,dQ=` +`,DQ=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3430,7 +3430,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,fQ=tt({opSnippet:mQ,packedOpSnippet:dQ,dtype:"int32"}),gR={kernelName:sn,backendName:"webgl",kernelFunc:fQ};var Vh=class{constructor(e){this.variableNames=["A"];let t=St(),[o,n]=e;this.outputShape=e,this.userCode=` +`,FQ=tt({opSnippet:RQ,packedOpSnippet:DQ,dtype:"int32"}),wR={kernelName:nn,backendName:"webgl",kernelFunc:FQ};var Vh=class{constructor(e){this.variableNames=["A"];let t=wt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3452,7 +3452,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}};var zh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=St(),[o,n]=e;this.outputShape=e,this.userCode=` + `}};var zh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=wt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,zQ=` +`,i7=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3526,18 +3526,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,WQ=ge({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:YE}),AR={kernelName:hn,backendName:"webgl",kernelFunc:WQ};var UQ=_o+` +`,u7=he({opSnippet:a7,packedOpSnippet:i7,cpuKernelImpl:rE}),MR={kernelName:fn,backendName:"webgl",kernelFunc:u7};var p7=$o+` return log(1.0 + x); -`,GQ=ge({opSnippet:UQ}),RR={kernelName:ga,backendName:"webgl",kernelFunc:GQ};var HQ="return float(a >= 1.0 && b >= 1.0);",qQ=` +`,c7=he({opSnippet:p7}),LR={kernelName:xa,backendName:"webgl",kernelFunc:c7};var l7="return float(a >= 1.0 && b >= 1.0);",m7=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,KQ=tt({opSnippet:HQ,packedOpSnippet:qQ,dtype:"bool"}),FR={kernelName:gn,backendName:"webgl",kernelFunc:KQ};var jQ="return float(!(x >= 1.0));",XQ=ge({opSnippet:jQ}),DR={kernelName:xn,backendName:"webgl",kernelFunc:XQ};var YQ="return float(a >= 1.0 || b >= 1.0);",QQ=` +`,d7=tt({opSnippet:l7,packedOpSnippet:m7,dtype:"bool"}),BR={kernelName:hn,backendName:"webgl",kernelFunc:d7};var f7="return float(!(x >= 1.0));",h7=he({opSnippet:f7}),VR={kernelName:gn,backendName:"webgl",kernelFunc:h7};var g7="return float(a >= 1.0 || b >= 1.0);",x7=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); 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int end = ${i}; @@ -3830,7 +3830,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam ${s} coords = outC - start; setOutput(getX(${p})); } - `}};var Yh=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=_e(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=$t("rc",n),u=$t("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` + `}};var Yh=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=$e(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=Et("rc",n),u=Et("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; 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int batch = coords[0]; @@ -3912,11 +3912,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}};var g7=` + `}};var P7=` if (a == b) { return 1.0; }; -return a / b;`,x7=` +return a / b;`,M7=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3934,9 +3934,9 @@ return a / b;`,x7=` } return result; -`,Ow=tt({opSnippet:g7,packedOpSnippet:x7,checkOutOfBounds:!0}),JR={kernelName:Jo,backendName:"webgl",kernelFunc:Ow};var eF="return a - b;",Pw=tt({opSnippet:eF,packedOpSnippet:eF,supportsComplex:!0,cpuKernelImpl:b$}),tF={kernelName:Xn,backendName:"webgl",kernelFunc:Pw};function Mw(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Dw({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:t,attrs:{shape:p}}),c=Pw({inputs:{a:n,b:u},backend:t}),l=Aw({inputs:{x:c},backend:t}),m=Ou({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:t,attrs:{shape:p}}),f=Ow({inputs:{a:l,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var rF={kernelName:qn,backendName:"webgl",kernelFunc:Mw};function y7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Mw({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qh(u,c,s),m=[[a]],d=t.runWebGLProgram(l,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var oF={kernelName:Sp,backendName:"webgl",kernelFunc:y7};var b7=Bt+` +`,Kw=tt({opSnippet:P7,packedOpSnippet:M7,checkOutOfBounds:!0}),sD={kernelName:Zo,backendName:"webgl",kernelFunc:Kw};var aD="return a - b;",qw=tt({opSnippet:aD,packedOpSnippet:aD,supportsComplex:!0,cpuKernelImpl:kE}),iD={kernelName:Yn,backendName:"webgl",kernelFunc:qw};function jw(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Hw({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=re({inputs:{x:i},backend:t,attrs:{shape:p}}),c=qw({inputs:{a:n,b:u},backend:t}),l=Ww({inputs:{x:c},backend:t}),m=Vu({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=re({inputs:{x:m},backend:t,attrs:{shape:p}}),f=Kw({inputs:{a:l,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var uD={kernelName:qn,backendName:"webgl",kernelFunc:jw};function L7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:jw({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qh(u,c,s),m=[[a]],d=t.runWebGLProgram(l,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var pD={kernelName:wp,backendName:"webgl",kernelFunc:L7};var B7=zt+` return -x; 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d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},uF={kernelName:En,backendName:"webgl",kernelFunc:_7};function Al(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ua({inputs:{input:o},backend:t}),s=Al({inputs:{x:n},backend:t}),a=Mu({inputs:{input:o},backend:t}),i=Al({inputs:{x:a},backend:t}),p=Rr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ga({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var pF={kernelName:Fs,backendName:"webgl",kernelFunc:Al};function cF(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ua({inputs:{input:o},backend:t}),s=cF({inputs:{x:n},backend:t}),a=Mu({inputs:{input:o},backend:t}),i=Al({inputs:{x:a},backend:t}),p=Rr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ga({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var lF={kernelName:Is,backendName:"webgl",kernelFunc:cF};function E7(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ph({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Ph({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=$w({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var mF={kernelName:vs,backendName:"webgl",kernelFunc:E7};var Jh=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=_e(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}};var j7=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Zh(u,a,i,p),l=re({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=re({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},fD={kernelName:$n,backendName:"webgl",kernelFunc:j7};function Fl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ka({inputs:{input:o},backend:t}),s=Fl({inputs:{x:n},backend:t}),a=Wu({inputs:{input:o},backend:t}),i=Fl({inputs:{x:a},backend:t}),p=Ar({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return qa({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var hD={kernelName:Os,backendName:"webgl",kernelFunc:Fl};function gD(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ka({inputs:{input:o},backend:t}),s=gD({inputs:{x:n},backend:t}),a=Wu({inputs:{input:o},backend:t}),i=Fl({inputs:{x:a},backend:t}),p=Ar({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return qa({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var xD={kernelName:ks,backendName:"webgl",kernelFunc:gD};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ph({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Ph({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=zw({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var yD={kernelName:Ns,backendName:"webgl",kernelFunc:X7};var Jh=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=$e(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${a}; int end = ${i}; @@ -3978,7 +3978,7 @@ return a / b;`,x7=` setOutput(getX(${p})); } } - `}};var eg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=_e(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=$t("rc",n),u=$t("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; + `}};var eg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=$e(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=Et("rc",n),u=Et("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; @@ -4002,7 +4002,7 @@ return a / b;`,x7=` ${f} setOutput(result); } - `}};var Lw=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ga({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},dF={kernelName:$n,backendName:"webgl",kernelFunc:Lw};var $7=` + `}};var Xw=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return qa({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},bD={kernelName:En,backendName:"webgl",kernelFunc:Xw};var Y7=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4011,7 +4011,7 @@ return a / b;`,x7=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,A7=` +`,Q7=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -4027,11 +4027,11 @@ return a / b;`,x7=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+Zs+` + `+Js+` return result; -`,R7=tt({opSnippet:$7,packedOpSnippet:A7}),fF={kernelName:An,backendName:"webgl",kernelFunc:R7};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=S.getAxesPermutation(c,i),m=n;l!=null&&(m=xt({inputs:{x:n},backend:t,attrs:{perm:l}}),c=S.getInnerMostAxes(c.length,i),p.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=o$(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=ka(n.dtype),C=Gr(x,b,"prod",t);d=te({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=S.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var hF={kernelName:Fn,backendName:"webgl",kernelFunc:F7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=n$(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var gF={kernelName:wp,backendName:"webgl",kernelFunc:D7};function O7(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=s$(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var xF={kernelName:Ip,backendName:"webgl",kernelFunc:O7};function 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eZ(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=cE(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var wD={kernelName:Ip,backendName:"webgl",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lE(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var ID={kernelName:vp,backendName:"webgl",kernelFunc:tZ};function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=mE(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var vD={kernelName:kp,backendName:"webgl",kernelFunc:rZ};var Yw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=dE(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},kD={kernelName:Ts,backendName:"webgl",kernelFunc:Yw};var oZ="return 1.0 / x;",nZ=he({opSnippet:oZ}),ND={kernelName:Fn,backendName:"webgl",kernelFunc:nZ};var sZ=zt+` return (x < 0.0) ? 0.0 : x; -`,V7=` +`,aZ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4041,9 +4041,9 @@ return a / b;`,x7=` result.a = isNaN.a ? x.a : result.a; return result; -`,z7=ge({opSnippet:B7,packedOpSnippet:V7}),SF={kernelName:On,backendName:"webgl",kernelFunc:z7};var W7=Bt+` +`,iZ=he({opSnippet:sZ,packedOpSnippet:aZ}),TD={kernelName:On,backendName:"webgl",kernelFunc:iZ};var uZ=zt+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,U7=` +`,pZ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4053,7 +4053,7 @@ return a / b;`,x7=` result.a = isNaN.a ? x.a : result.a; return result; -`,G7=ge({opSnippet:W7,packedOpSnippet:U7}),wF={kernelName:Ln,backendName:"webgl",kernelFunc:G7};var tg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,cZ=he({opSnippet:uZ,packedOpSnippet:pZ}),_D={kernelName:Ln,backendName:"webgl",kernelFunc:cZ};var tg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4163,7 +4163,7 @@ return a / b;`,x7=` setOutput(newValue); } - `}};function H7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var IF={kernelName:Mn,backendName:"webgl",kernelFunc:H7};var og=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var $D={kernelName:Mn,backendName:"webgl",kernelFunc:lZ};var og=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4244,7 +4244,7 @@ return a / b;`,x7=` setOutput(accumulator); } - `}};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var vF={kernelName:$m,backendName:"webgl",kernelFunc:q7};var ng=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function mZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var ED={kernelName:Am,backendName:"webgl",kernelFunc:mZ};var ng=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4307,7 +4307,7 @@ return a / b;`,x7=` setOutput(newValue); } - `}};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var kF={kernelName:Pn,backendName:"webgl",kernelFunc:K7};var ag=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var AD={kernelName:Pn,backendName:"webgl",kernelFunc:dZ};var ag=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4377,17 +4377,17 @@ return a / b;`,x7=` setOutput(accumulator); } - `}};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var NF={kernelName:Em,backendName:"webgl",kernelFunc:j7};var ig=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` + `}};function fZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var RD={kernelName:Em,backendName:"webgl",kernelFunc:fZ};var ig=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } - `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(","),a=_e(o);this.userCode=` + `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(","),a=$e(o);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var ug=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=$t("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=_e(o);o===1?this.userCode=` + `}};var ug=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Et("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=$e(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4415,7 +4415,7 @@ return a / b;`,x7=` } setOutput(result); } - `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var TF={kernelName:Bn,backendName:"webgl",kernelFunc:X7};var pg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` + `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var DD={kernelName:Bn,backendName:"webgl",kernelFunc:hZ};var pg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4434,7 +4434,7 @@ return a / b;`,x7=` } setOutput(outputValue); } - `}};var _F={kernelName:es,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Y7=` + `}};var FD={kernelName:ts,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var gZ=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4449,7 +4449,7 @@ return a / b;`,x7=` return base + 1.0; } } -`,Q7=ge({opSnippet:Y7}),EF={kernelName:Ca,backendName:"webgl",kernelFunc:Q7};var Z7="return inversesqrt(x);",J7=ge({opSnippet:Z7,cpuKernelImpl:u$}),$F={kernelName:Vn,backendName:"webgl",kernelFunc:J7};var Cc=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let p=_e(s.length),u=_e(a.length),c="";o===1?c="i":o===2&&(c="i, j");let l=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let d=`getUpdates(${m})`,f=t>1?"strides[j]":"strides";this.userCode=` +`,xZ=he({opSnippet:gZ}),OD={kernelName:Vn,backendName:"webgl",kernelFunc:xZ};var yZ="return inversesqrt(x);",bZ=he({opSnippet:yZ,cpuKernelImpl:fE}),PD={kernelName:zn,backendName:"webgl",kernelFunc:bZ};var Sc=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let p=$e(s.length),u=$e(a.length),c="";o===1?c="i":o===2&&(c="i, j");let l=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let d=`getUpdates(${m})`,f=t>1?"strides[j]":"strides";this.userCode=` ${p} strides = ${p}(${s}); void main() { @@ -4469,7 +4469,7 @@ return a / b;`,x7=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Cc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var AF={kernelName:zn,backendName:"webgl",kernelFunc:eZ};var cg=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let 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cg=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=O().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4494,7 +4494,7 @@ return a / b;`,x7=` setOutput(float(findBound(batch, value))); } - `}};function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new cg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],"int32",p)}var RF={kernelName:ii,backendName:"webgl",kernelFunc:tZ};var lg=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,nZ=ge({opSnippet:oZ}),DF={kernelName:Xi,backendName:"webgl",kernelFunc:nZ};var sZ=_o+` +`,vZ=he({opSnippet:IZ}),VD={kernelName:Ca,backendName:"webgl",kernelFunc:vZ};var kZ=$o+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,aZ=` +`,NZ=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4522,15 +4522,15 @@ return a / b;`,x7=` result.a = isNaN.a ? x.a : result.a; return result; -`,iZ=ge({opSnippet:sZ,packedOpSnippet:aZ,cpuKernelImpl:c$}),OF={kernelName:Un,backendName:"webgl",kernelFunc:iZ};var uZ=` +`,TZ=he({opSnippet:kZ,packedOpSnippet:NZ,cpuKernelImpl:gE}),zD={kernelName:Gn,backendName:"webgl",kernelFunc:TZ};var _Z=` if (isnan(x)) { return 0.0; } return sign(x); 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function S3(){let[r,e]=await Promise.all([O().getAsync("WASM_HAS_SIMD_SUPPORT"),O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((t,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(".worker.js")){let u=C3.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?b3(r,e,Fl!=null?Fl:p):p+i},eI&&(n.instantiateWasm=Cee(b3(r,e,Fl!=null?Fl:"")));let s=!1;n.onAbort=()=>{if(s||Ol)return;Ol=!0,o({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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ds=O();ds.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);ds.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);ds.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);ds.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);ds.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);ds.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);ds.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);ds.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>0);ds.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);var kg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Ng=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,o=!1){let n=$3(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(a),a}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,o){if(this.freeBuffers.size===0)return;let n=$3(t,o);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(n),a=s.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");s.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,o){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,o)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function $3(r,e){return`${r}_${e}`}var Tg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=A3(o),a=e*t*s,i=E3(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e,t,o,n,s){if(this.freeTextures.size===0)return;let a=E3(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=A3(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function E3(r,e,t,o){return`${r}_${e}_${t}_${o}`}function A3(r){if(r==="rgba8unorm")return 16;throw new Error(`${r} is not supported!`)}function R3(r,e){if(Math.max(...r)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}var Ic=(r,e,t)=>t==="int32"?`atomicAdd(${r}, bitcast(${e}));`:` + { + var oldValue = 0; + loop { + let newValueF32 = bitcast(oldValue) + (${e}); + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(${r}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;var M3=(r,e,t,o)=>{let n={dtype:o.dtype,shape:o.shape},s=Yee(t,n,e),a=r.createShaderModule({code:s,label:e.constructor.name});return r.createComputePipeline({compute:{module:a,entryPoint:"_start"},label:e.constructor.name,layout:"auto"})};function Rt(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Ao(r){if(r===0)return"x";if(r===1)return"y";if(r===2)return"z";if(r===3)return"w";if(r===4)return"u";if(r===5)return"v";throw Error(`Index ${r} is not yet supported`)}function Q(...r){let e;switch(r.length){case 0:e=` fn main() `;break;case 1:e=` fn main(${r[0]} : i32) - `;break;default:throw Error("Unreachable")}return e}function T3(r){let e;return e=` - ${Eee()} + `;break;default:throw Error("Unreachable")}return e}function D3(r,e){let t;return t=` + ${Xee(e)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @@ -4888,12 +4900,9 @@ return a / b;`,x7=` workgroupId = WorkgroupId; ${r?"main(getGlobalIndex());":"main();"}; } - `,e}function Eee(){return` - @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ) -`}function $ee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(` - const workgroupSizeX = ${t.workgroupSize[0]}u; - const workgroupSizeY = ${t.workgroupSize[1]}u; - const workgroupSizeZ = ${t.workgroupSize[2]}u; + `,t}function Xee(r){return` + @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]}) +`}function Yee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(` var localId: vec3; var localIndex: u32; @@ -4903,8 +4912,8 @@ return a / b;`,x7=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${F3(t)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + - workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} + + ${B3(t)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } @@ -4915,23 +4924,23 @@ return a / b;`,x7=` outShapeStrides : vec2, }; - @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>; + @group(0) @binding(0) var result: array<${vc(e.dtype,t.isVec4)}>; @group(0) @binding(2) var uniforms: Uniform; - `);let f=$3(t);return[_3,o.join(` -`),E3(e.shape),t.getUserCode(),T3(f)].join(` + `);let f=P3(t);return[F3,o.join(` +`),O3(e.shape),t.getUserCode(),D3(f,t)].join(` `)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=` - outShapeStrides: ${p}, `,t.size&&(s+="size : i32, "),t.uniforms&&(s+=t.uniforms),s+="};",s=Lee(s),o.push(s),t.atomic?o.push(` + outShapeStrides: ${p}, `,t.size&&(s+="size : i32, "),t.uniforms&&(s+=t.uniforms),s+="};",s=nte(s),o.push(s),t.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` - @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>; + @group(0) @binding(0) var result: array<${vc(e.dtype,t.isVec4)}>; `),t.variableNames.forEach((f,h)=>{o.push(` - @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:wc(r[h].dtype,t.isVec4)}>; + @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:vc(r[h].dtype,t.isVec4)}>; `)}),s!==""&&o.push(` @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms; - `);let u=Oee(e.shape,t.dispatchLayout),c=[_3+Aee,o.join(` -`),E3(e.shape),u,Pee(e.shape.length)];t.atomic||c.push(Mee(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>Dee(f,e.shape,t.variableTypes?t.variableTypes[h]==="vec4":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(` -`);c.push(l),c.push(t.getUserCode());let m=$3(t);return c.push(T3(m)),c.join(` -`)}function R3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=F3(r)?"flatDispatch":"";return n+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+e.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,n}var _3=` + `);let u=tte(e.shape,t.dispatchLayout),c=[F3,o.join(` +`)+Qee,O3(e.shape),u,rte(e.shape.length)];t.atomic||c.push(ote(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>ete(f,e.shape,t.variableTypes?t.variableTypes[h]==="vec4":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(` +`);c.push(l),c.push(t.getUserCode());let m=P3(t);return c.push(D3(m,t)),c.join(` +`)}function L3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=B3(r)?"flatDispatch":"";return n+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+e.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,n}var F3=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -4987,21 +4996,22 @@ return a / b;`,x7=` return (floatToUint & 0x7fffffffu) > 0x7f800000u; } fn isnanVec4(val : vec4) -> vec4 { - return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3])); + let floatToUint: vec4 = bitcast>(val); + return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } -`,Aee=` +`,Qee=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function E3(r){let e=r.length;if(e<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 { +`;function O3(r){let e=r.length;if(e<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); - }`;let s;return s="var index2 = index;"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`;return`${p}; ${u};`}).join(""),` + }`;let s;return s="var index2 = index;"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`;return`${p}; ${u};`}).join(""),` fn getCoordsFromIndex(index : i32) -> ${o} { ${s} return ${o}(${n.join(",")}); } - `}function Ree(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s="get"+t.charAt(0).toUpperCase()+t.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return e?` + `}function Zee(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s="get"+t.charAt(0).toUpperCase()+t.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return e?` fn ${s}() -> vec4 { return vec4(${t}[0]); } @@ -5019,7 +5029,7 @@ return a / b;`,x7=` return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})]); } - `}function Fee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?` + `}function Jee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?` fn ${a}Index(globalIndex : i32) -> vec4 { return vec4(${n}[globalIndex]); } @@ -5051,8 +5061,8 @@ return a / b;`,x7=` fn ${a}Coords(coords : ${u}) -> f32{ return get${s}(); } - `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${$o(g+l)} = 0;`).join(` -`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${$o(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?` + `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${Ao(g+l)} = 0;`).join(` +`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${Ao(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?` fn ${a}Index(globalIndex : i32) -> vec4 { var coords = getCoordsFromIndex(globalIndex); ${m} @@ -5076,13 +5086,13 @@ return a / b;`,x7=` ${m} return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]); } -`}function Dee(r,e,t,o){let n=Ree(r,t);return r.shape.length<=e.length&&(n+=Fee(r,e,t,o)),n}function Oee(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{ +`}function ete(r,e,t,o){let n=Zee(r,t);return r.shape.length<=e.length&&(n+=Jee(r,e,t,o)),n}function tte(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let i="",p=[t,o,n];for(let m=0;m ${c} { + `;let i="",p=[t,o,n];for(let m=0;m ${c} { ${i} -`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function Pee(r){let e="";switch(r){case 0:case 1:e+=` +`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function rte(r){let e="";switch(r){case 0:case 1:e+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { return coords; } @@ -5116,7 +5126,7 @@ return a / b;`,x7=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function F3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function wc(r,e){return r==="float32"?e?"vec4":"f32":r==="int32"||r==="bool"?e?"vec4":"i32":r}function Mee(r,e,t){let o=r.length,n=wc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { + `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function B3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function vc(r,e){return r==="float32"?e?"vec4":"f32":r==="int32"||r==="bool"?e?"vec4":"i32":r}function ote(r,e,t){let o=r.length,n=vc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : vec4) { @@ -5144,31 +5154,19 @@ return a / b;`,x7=` let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex, value); } - `}return s}function Lee(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function $3(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var nI={};Ue(nI,{ArrayBufferToTypedArray:()=>oI,GPUBytesPerElement:()=>rI,MatMulProgramType:()=>Ao,computeDispatch:()=>re,computeWorkPerThreadForConv2d:()=>Ll,computeWorkgroupInfoForMatMul:()=>tI,computeWorkgroupSizeForConv2d:()=>Ml,flatDispatchLayout:()=>ue,isWebGPUSupported:()=>Bl,tilesFitEvenlyIntoShape:()=>Vee});var zu=r=>{let e=1;for(let t=0;tt%r[o]===0)}function re(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(zu(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(zu(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(zu(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function tI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Ml(r,e,t=!1){if(t)return[8,8,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Ll(r,e,t=!1){if(t)return[4,4,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ue(r){return{x:r.map((e,t)=>t)}}function rI(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function oI(r,e){if(e==="float32")return new Float32Array(r);if(e==="int32")return new Int32Array(r);if(e==="bool"||e==="string")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${e}`)}function Bl(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Ao;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Ao||(Ao={}));var zee=O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Wee=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Ui=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Bl())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Do(this,cr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Ui.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=oI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=cr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=rI(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[];e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4});let s=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(s,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(s,c,p.data.length).set(p.data):new Float32Array(s,c,p.data.length).set(p.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,s,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Wee(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h="int32";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=R3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=A3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=zee){return O().getBool("WEBGPU_CPU_FORWARD")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let r={powerPreference:O().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has("timestamp-query-inside-passes")&&(t.requiredFeatures=["timestamp-query-inside-passes"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Ui(n,s)},3);var ye;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.EQUAL=5]="EQUAL",r[r.GREATER=6]="GREATER",r[r.GREATER_EQUAL=7]="GREATER_EQUAL",r[r.INT_DIV=8]="INT_DIV",r[r.LESS=9]="LESS",r[r.LESS_EQUAL=10]="LESS_EQUAL",r[r.LOGICAL_AND=11]="LOGICAL_AND",r[r.MAX=12]="MAX",r[r.MIN=13]="MIN",r[r.MOD=14]="MOD",r[r.MUL=15]="MUL",r[r.NOT_EQUAL=16]="NOT_EQUAL",r[r.POW=17]="POW",r[r.PRELU=18]="PRELU",r[r.SQUARED_DIFFERENCE=19]="SQUARED_DIFFERENCE",r[r.SUB=20]="SUB"})(ye||(ye={}));var D3=` + `}return s}function nte(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function P3(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var hI={};Ge(hI,{GPUBytesPerElement:()=>_g,MatMulProgramType:()=>Ro,assertNotComplex:()=>fI,computeDispatch:()=>Y,computeWorkPerThreadForConv2d:()=>Vl,computeWorkgroupInfoForMatMul:()=>dI,computeWorkgroupSizeForConv2d:()=>Bl,flatDispatchLayout:()=>ae,isWebGPUSupported:()=>zl,tilesFitEvenlyIntoShape:()=>ate});var Ku=r=>{let e=1;for(let t=0;tt%r[o]===0)}function Y(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ku(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ku(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ku(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function dI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Bl(r,e,t=!1){if(t)return[8,8,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Vl(r,e,t=!1){if(t)return[4,4,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ae(r){return{x:r.map((e,t)=>t)}}function _g(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function zl(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function fI(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Ro;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Ro||(Ro={}));var ite=O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),ute=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Gi=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!zl())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Fo(this,sr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Gi.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=_g(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=_g(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type==="int32"?new Int32Array(a,l,u.data.length).set(u.data):u.type==="uint32"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=ute(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h="int32";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=L3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=M3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=ite){return O().getBool("WEBGPU_CPU_FORWARD")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let r={powerPreference:O().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has("timestamp-query-inside-passes")&&(t.requiredFeatures=["timestamp-query-inside-passes"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Gi(n,s)},3);var ge;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.EQUAL=5]="EQUAL",r[r.GREATER=6]="GREATER",r[r.GREATER_EQUAL=7]="GREATER_EQUAL",r[r.INT_DIV=8]="INT_DIV",r[r.LESS=9]="LESS",r[r.LESS_EQUAL=10]="LESS_EQUAL",r[r.LOGICAL_AND=11]="LOGICAL_AND",r[r.LOGICAL_OR=12]="LOGICAL_OR",r[r.MAX=13]="MAX",r[r.MIN=14]="MIN",r[r.MOD=15]="MOD",r[r.MUL=16]="MUL",r[r.NOT_EQUAL=17]="NOT_EQUAL",r[r.POW=18]="POW",r[r.PRELU=19]="PRELU",r[r.SQUARED_DIFFERENCE=20]="SQUARED_DIFFERENCE",r[r.SUB=21]="SUB"})(ge||(ge={}));var V3=` if (isnan(a)) { return a; } if (isnan(b)) { return b; } - `,O3=` - if (isNaN.r) { - resultTemp.r = valueForNaN; - } - if (isNaN.g) { - resultTemp.g = valueForNaN; - } - if (isNaN.b) { - resultTemp.b = valueForNaN; - } - if (isNaN.a) { - resultTemp.a = valueForNaN; - } - `,aI=` - let isNaN = isnanVec4(a) | isnanVec4(b); - ${O3} - `,Uee="return a + b;",Gee="return areal * breal - aimag * bimag;",Hee="return areal * bimag + aimag * breal;",qee="return a / b;",Kee="return f32(a == b);",jee="return vec4(a == b);",Xee="return f32(a > b);",Yee="return vec4(a > b);",Qee="return f32(a >= b);",Zee="return vec4(a >= b);",Jee=` + `,$g=` + resultTemp = select( + resultTemp, vec4(valueForNaN), + vec4(isNaN) | isnanVec4(a) | isnanVec4(b)); + `,pte="return a + b;",cte="return areal * breal - aimag * bimag;",lte="return areal * bimag + aimag * breal;",mte="return a / b;",dte="return f32(a == b);",fte="return vec4(a == b);",hte="return f32(a > b);",gte="return vec4(a > b);",xte="return f32(a >= b);",yte="return vec4(a >= b);",bte=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); -`,ete=` +`,Cte=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); @@ -5189,9 +5187,10 @@ return a / b;`,x7=` resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); -`,tte="return f32(a < b);",rte="return vec4(a < b);",ote="return f32(a <= b);",nte="return vec4(a <= b);",ste="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",ate=`return (vec4(a >= vec4(1.0)) * - vec4(b >= vec4(1.0)));`,ite=` - ${D3} +`,Ste="return f32(a < b);",wte="return vec4(a < b);",Ite="return f32(a <= b);",vte="return vec4(a <= b);",kte="return f32(a >= 1.0 && b >= 1.0);",Nte=`return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`,Tte="return f32(a >= 1.0 || b >= 1.0);",_te=`return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`,$te=` + ${V3} if (b == 0.) { return uniforms.NAN; } @@ -5201,23 +5200,11 @@ return a / b;`,x7=` } else { return (resultTemp + b) % b; } -`,ute=` +`,Ete=` + let isNaN = !vec4(b); let valueForNaN = uniforms.NAN; var resultTemp = vec4(a % b); - ${aI} - - if (b[0] == 0.) { - resultTemp[0] = uniforms.NAN; - } - if (b[1] == 0.) { - resultTemp[1] = uniforms.NAN; - } - if (b[2] == 0.) { - resultTemp[2] = uniforms.NAN; - } - if (b[3] == 0.) { - resultTemp[3] = uniforms.NAN; - } + ${$g} if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { resultTemp[0] = (resultTemp[0] + b[0]) % b[0]; @@ -5233,18 +5220,18 @@ return a / b;`,x7=` } return resultTemp; -`,pte="return a * b;",cte=` +`,Ate="return a * b;",Rte=` if (isnan(a) || isnan(b)) { return 1.0; } return f32(a != b); -`,lte=` +`,Dte=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; - ${aI} + ${$g} return resultTemp; -`,mte=` +`,Fte=` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } @@ -5255,7 +5242,7 @@ return a / b;`,x7=` return pow(abs(a), b); } return sign(a) * pow(abs(a), b); -`,dte=` +`,Ote=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -5277,39 +5264,39 @@ return a / b;`,x7=` } let isNaN = (a < vec4(0.0)) & (floor(b) < b); let valueForNaN = uniforms.NAN; - ${O3} + ${$g} return resultTemp; -`,fte="if (a < 0.0) { return b * a; } return a;",hte=` +`,Pte="if (a < 0.0) { return b * a; } return a;",Mte=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,gte="return (a - b) * (a - b);",xte="return a - b;";function sI(r,e,t="uniforms.NAN"){let o=e?aI:D3;return e?` +`,Lte="return (a - b) * (a - b);",Bte="return a - b;";function gI(r,e,t="uniforms.NAN"){let o=e?$g:V3;return e?` let valueForNaN = ${t}; var resultTemp = vec4(${r}(a, b)); `+o+` return resultTemp; `:o+` return ${r}(a, b); - `}function Ic(r,e){switch(r){case ye.ADD:return Uee;case ye.ATAN2:return sI("atan2",e);case ye.COMPLEX_MULTIPLY_IMAG:return Hee;case ye.COMPLEX_MULTIPLY_REAL:return Gee;case ye.DIV:return qee;case ye.EQUAL:return e?jee:Kee;case ye.GREATER:return e?Yee:Xee;case ye.GREATER_EQUAL:return e?Zee:Qee;case ye.INT_DIV:return e?ete:Jee;case ye.LESS:return e?rte:tte;case ye.LESS_EQUAL:return e?nte:ote;case ye.LOGICAL_AND:return e?ate:ste;case ye.MAX:return sI("max",e);case ye.MIN:return sI("min",e);case ye.MOD:return e?ute:ite;case ye.MUL:return pte;case ye.NOT_EQUAL:return e?lte:cte;case ye.POW:return e?dte:mte;case ye.PRELU:return e?hte:fte;case ye.SQUARED_DIFFERENCE:return gte;case ye.SUB:return xte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.RSQRT=27]="RSQRT",r[r.SIN=28]="SIN",r[r.SINH=29]="SINH",r[r.SIGMOID=30]="SIGMOID",r[r.SQRT=31]="SQRT",r[r.SQUARE=32]="SQUARE",r[r.TAN=33]="TAN",r[r.TANH=34]="TANH",r[r.TO_INT=35]="TO_INT"})(Q||(Q={}));var yte="return abs(a);",bte=` + `}function kc(r,e){switch(r){case ge.ADD:return pte;case ge.ATAN2:return gI("atan2",e);case ge.COMPLEX_MULTIPLY_IMAG:return lte;case ge.COMPLEX_MULTIPLY_REAL:return cte;case ge.DIV:return mte;case ge.EQUAL:return e?fte:dte;case ge.GREATER:return e?gte:hte;case ge.GREATER_EQUAL:return e?yte:xte;case ge.INT_DIV:return e?Cte:bte;case ge.LESS:return e?wte:Ste;case ge.LESS_EQUAL:return e?vte:Ite;case ge.LOGICAL_AND:return e?Nte:kte;case ge.LOGICAL_OR:return e?_te:Tte;case ge.MAX:return gI("max",e);case ge.MIN:return gI("min",e);case ge.MOD:return e?Ete:$te;case ge.MUL:return Ate;case ge.NOT_EQUAL:return e?Dte:Rte;case ge.POW:return e?Ote:Fte;case ge.PRELU:return e?Mte:Pte;case ge.SQUARED_DIFFERENCE:return Lte;case ge.SUB:return Bte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var X;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.ROUND=27]="ROUND",r[r.RSQRT=28]="RSQRT",r[r.SELU=29]="SELU",r[r.SIGMOID=30]="SIGMOID",r[r.SIGN=31]="SIGN",r[r.SIN=32]="SIN",r[r.SINH=33]="SINH",r[r.SOFTPLUS=34]="SOFTPLUS",r[r.SQRT=35]="SQRT",r[r.SQUARE=36]="SQUARE",r[r.STEP=37]="STEP",r[r.TAN=38]="TAN",r[r.TANH=39]="TANH",r[r.TO_INT=40]="TO_INT"})(X||(X={}));var Vte="return abs(a);",zte=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); -`,Cte=` +`,Wte=` if (a < 1.) { return uniforms.NAN; } return acosh(a); -`,Ste=` +`,Ute=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); -`,wte="return asinh(a);",Ite=` +`,Gte="return asinh(a);",Hte=` if (isnan(a)) { return uniforms.NAN; } return atan(a); -`,vte=` +`,Kte=` if (abs(a) > 1.) { return uniforms.NAN; } @@ -5320,10 +5307,10 @@ return a / b;`,x7=` return -uniforms.INFINITY; } return atanh(a); -`,kte="return ceil(a);",Nte="return cos(a);",Tte=` +`,qte="return ceil(a);",jte="return cos(a);",Xte=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; -`,_te="return exp(a) - 1.0;",Ete="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",$te=` +`,Yte="return exp(a) - 1.0;",Qte="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Zte=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -5338,7 +5325,7 @@ return a / b;`,x7=` resFloat.a = a.a; } return resFloat; -`,Ate=` +`,Jte=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -5353,195 +5340,217 @@ return a / b;`,x7=` let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); -`,Rte="return exp(a);",Fte="return floor(a);",Dte="return f32(!isnan(a) && !isinf(a));",Ote="return f32(isinf(a));",Pte="return f32(isnan(a));",Mte="return a;",Lte=`if (a < 0.0) { return uniforms.NAN; } - return log(a);`,Bte=` +`,ere="return exp(a);",tre="return floor(a);",rre="return f32(!isnan(a) && !isinf(a));",ore="return f32(isinf(a));",nre="return f32(isnan(a));",sre="return a;",are=`if (a < 0.0) { return uniforms.NAN; } + return log(a);`,ire=` if (isnan(a)) { return a; } return log(1.0 + a); -`,Vte="return f32(!(a >= 1.0));",zte="return -a;",Wte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ute=` +`,ure="return f32(!(a >= 1.0));",pre="return -a;",cre="if (a < 0.0) { return uniforms.alpha * a; } return a;",lre=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Gte="return 1.0 / a;",Hte="return select(a, 0.0, a < 0.0);",qte="return clamp(a, 0.0, 6.0);",Kte="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",jte=` +`,mre="return 1.0 / a;",dre="return select(a, 0.0, a < 0.0);",fre="return clamp(a, 0.0, 6.0);",hre="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",gre=` return select(a, vec4(0.0), a < vec4(0.0)); -`,Xte="return inverseSqrt(a);",Yte="return 1.0 / (1.0 + exp(-1.0 * a));",Qte="return sin(a);",Zte=` +`,xre="return round(a);",yre="return inverseSqrt(a);",bre=` + if (a >= 0.0) { + return ${S.SELU_SCALE} * a; + } else { + return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0); + } +`,Cre="return 1.0 / (1.0 + exp(-1.0 * a));",Sre="return sign(a);",wre="return sin(a);",Ire=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; -`,Jte="return sqrt(a);",ere="return a * a;",tre="return tan(a);",rre=` +`,vre=` + let epsilon = 1.1920928955078125e-7; + let threshold = log(epsilon) + 2.0; + + let too_large = a > -threshold; + let too_small = a < threshold; + let exp_a = exp(a); + + if (too_large) { + return a; + } else if (too_small) { + return exp_a; + } else { + return log(exp_a + 1.0); + } +`,kre="return sqrt(a);",Nre="return a * a;",Tre=` + if (isnan(a)) { + return a; + } + + return select(uniforms.stepAlpha, 1.0, a > 0.0); +`,_re="return tan(a);",$re=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,ore="return f32(i32((a)));";function Ha(r,e){switch(r){case Q.ABS:return yte;case Q.ACOS:return bte;case Q.ACOSH:return Cte;case Q.ASIN:return Ste;case Q.ASINH:return wte;case Q.ATAN:return Ite;case Q.ATANH:return vte;case Q.COS:return Nte;case Q.COSH:return Tte;case Q.CEIL:return kte;case Q.ELU:return e?$te:Ete;case Q.ERF:return Ate;case Q.EXP:return Rte;case Q.EXPM1:return _te;case Q.FLOOR:return Fte;case Q.IS_FINITE:return Dte;case Q.IS_INF:return Ote;case Q.IS_NAN:return Pte;case Q.LINEAR:return Mte;case Q.LOG:return Lte;case Q.LOG1P:return Bte;case Q.LOGICAL_NOT:return Vte;case Q.NEG:return zte;case Q.LEAKYRELU:return e?Ute:Wte;case Q.RECIPROCAL:return Gte;case Q.RELU:return e?jte:Hte;case Q.RELU6:return e?Kte:qte;case Q.RSQRT:return Xte;case Q.SIGMOID:return Yte;case Q.SIN:return Qte;case Q.SINH:return Zte;case Q.SQRT:return Jte;case Q.SQUARE:return ere;case Q.TAN:return tre;case Q.TANH:return rre;case Q.TO_INT:return ore;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${r}-component is not supported.`)}};function ur(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=Ha(Q.LINEAR);else if(r==="relu")n=Ha(Q.RELU,t);else if(r==="elu")n=Ha(Q.ELU,t);else if(r==="relu6")n=Ha(Q.RELU6,t);else if(r==="prelu")n=Ic(ye.PRELU,t);else if(r==="sigmoid")n=Ha(Q.SIGMOID,t);else if(r==="leakyrelu")n=Ha(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i="";return e?i=` +`,Ere="return f32(i32((a)));";function ja(r,e){switch(r){case X.ABS:return Vte;case X.ACOS:return zte;case X.ACOSH:return Wte;case X.ASIN:return Ute;case X.ASINH:return Gte;case X.ATAN:return Hte;case X.ATANH:return Kte;case X.COS:return jte;case X.COSH:return Xte;case X.CEIL:return qte;case X.ELU:return e?Zte:Qte;case X.ERF:return Jte;case X.EXP:return ere;case X.EXPM1:return Yte;case X.FLOOR:return tre;case X.IS_FINITE:return rre;case X.IS_INF:return ore;case X.IS_NAN:return nre;case X.LINEAR:return sre;case X.LOG:return are;case X.LOG1P:return ire;case X.LOGICAL_NOT:return ure;case X.NEG:return pre;case X.LEAKYRELU:return e?lre:cre;case X.RECIPROCAL:return mre;case X.RELU:return e?gre:dre;case X.RELU6:return e?hre:fre;case X.ROUND:return xre;case X.RSQRT:return yre;case X.SELU:return bre;case X.SIGMOID:return Cre;case X.SIGN:return Sre;case X.SIN:return wre;case X.SINH:return Ire;case X.SOFTPLUS:return vre;case X.SQRT:return kre;case X.SQUARE:return Nre;case X.STEP:return Tre;case X.TAN:return _re;case X.TANH:return $re;case X.TO_INT:return Ere;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${r}-component is not supported.`)}};function pr(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=ja(X.LINEAR);else if(r==="relu")n=ja(X.RELU,t);else if(r==="elu")n=ja(X.ELU,t);else if(r==="relu6")n=ja(X.RELU6,t);else if(r==="prelu")n=kc(ge.PRELU,t);else if(r==="sigmoid")n=ja(X.SIGMOID,t);else if(r==="leakyrelu")n=ja(X.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i="";return e?i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); ${n} }`:i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { ${n} - }`,i}function Hr(r,e){return` + }`,i}function Gr(r,e){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${e?"value = activation(value, coords);":""} - `}function iI(r,e,t,o,n=!1,s=!1,a=!1,i=1){y.assert(t&&i===1||!t,()=>`transposeA ${t} is not compatible with component size ${i}`);let p=` - let batch = ${r?"0":"batchIn"}; - ${t?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} + `}function xI(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` + ${r?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} - `,u=o?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` - fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { - var value = ${kt(i)}(0.0); - let col = colIn * ${i}; - ${n&&a?p:` - ${t?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} + `,i=e?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` + fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} { + var value = ${kt(s)}(0.0); + let col = colIn * ${s}; + ${t&&n?a:` + ${r?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { - ${p} + ${a} } `} return value; } - fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { - let col = colIn * ${i}; - let batch = ${e?"0":"batchIn"}; - var value = ${kt(i)}(0.0); - ${u} + fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} { + let col = colIn * ${s}; + var value = ${kt(s)}(0.0); + ${i} return value; } - `}function Vl(r,e,t,o,n,s,a=!1,i=!1,p=!1,u=1){return` - ${iI(t,o,n,s,a,i,p,u)} - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) { - let col = colIn * ${u}; - ${a&&i?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} + `}function Wl(r,e,t,o,n=!1,s=!1,a=!1,i=1){return` + ${xI(t,o,n,s,a,i)} + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) { + let col = colIn * ${i}; + ${n&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${Hr(r,e)} + ${Gr(r,e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } - `}var nre=r=>r?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + `}var Are=(r,e)=>r?` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, - globalRowStart / innerElementSize + inputCol); + globalRowStart / ${e} + inputCol); `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, - kStart / innerElementSize + inputCol); - `,sre=(r,e)=>r?` - let ACached0 = mm_Asub[k * innerElementSize][localRow]; - let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; - let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; - ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} - for (var i = 0; i < rowPerThread; i = i + 1) { + kStart / ${e} + inputCol); + `,Rre=(r,e,t)=>r?` + let ACached0 = mm_Asub[k * ${e}][localRow]; + let ACached1 = mm_Asub[k * ${e} + 1][localRow]; + let ACached2 = mm_Asub[k * ${e} + 2][localRow]; + ${e===3?"":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`} + for (var i = 0; i < ${t}; i++) { acc[i] = BCached0 * ACached0[i] + acc[i]; acc[i] = BCached1 * ACached1[i] + acc[i]; acc[i] = BCached2 * ACached2[i] + acc[i]; ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} }`:` - for (var i = 0; i < rowPerThread; i = i + 1) { + for (var i = 0; i < ${t}; i++) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached0 * ACached.x + acc[i]; acc[i] = BCached1 * ACached.y + acc[i]; acc[i] = BCached2 * ACached.z + acc[i]; ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} - }`;function Wu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. - Otherwise, innerElementSize ${l} must be 3 or 4. - tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),` - var mm_Asub : array, ${u/l}>, ${c}>; - var mm_Bsub : array, ${p/r[0]}>, ${o}>; + }`;function qu(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=e[1]*r[1],u=e[0]*r[0],c=t?p:o,l=t?o:p,m=c/e[0],d=o/e[1],f=r[1];return y.assert((t&&m===4&&r[1]===4||!t&&(m===3||m===4))&&c%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4. + Otherwise, innerElementSize ${m} must be 3 or 4. + tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),` + var mm_Asub : array, ${c/m}>, ${l}>; + var mm_Bsub : array, ${u/r[0]}>, ${o}>; - const rowPerThread = ${r[1]}; - const colPerThread = ${r[0]}; - const innerElementSize = ${l}; - const tileInner = ${o}; - - ${se()} { + ${Q()} { let localRow = i32(localId.y); - let tileRow = ${a?"0":"localRow * rowPerThread"}; + let tileRow = ${a?"0":`localRow * ${f}`}; let tileCol = i32(localId.x); - let globalRow = ${a?"0":"i32(globalId.y) * rowPerThread"}; + let globalRow = ${a?"0":`i32(globalId.y) * ${f}`}; let globalCol = i32(globalId.x); let batch = ${n?"0":"i32(globalId.z)"}; - let globalRowStart = i32(workgroupId.y) * ${i}; + let batchA = ${n||!i?"batch":"batch % uniforms.aShape[0]"}; + let batchB = ${n||!i?"batch":"batch % uniforms.bShape[0]"}; + let globalRowStart = i32(workgroupId.y) * ${p}; - let numTiles = ${n?`${Math.ceil(s/o)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; + let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; - var acc: array, rowPerThread>; + var acc: array, ${f}>; // Loop over shared dimension. - let tileRowB = localRow * ${m}; - for (var t = 0; t < numTiles; t = t + 1) { + let tileRowB = localRow * ${d}; + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${nre(t)} + ${Are(t,m)} } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${d}; innerRow++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol); + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol); } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { - let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; - let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; - let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; - ${l===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + for (var k = 0; k < ${o/m}; k++) { + let BCached0 = mm_Bsub[k * ${m}][tileCol]; + let BCached1 = mm_Bsub[k * ${m} + 1][tileCol]; + let BCached2 = mm_Bsub[k * ${m} + 2][tileCol]; + ${m===3?"":`let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`} - ${sre(t,l)} + ${Rre(t,m,f)} } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var P3=r=>r?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + }`}var z3=r=>r?` + mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, + mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,are=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Uu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=r[1]*e[1],p=r[0]*e[0],u=t?i:o,c=t?o:i;y.assert(c%e[1]===0&&u%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let l=c/e[1],m=u/e[0],d=o/e[1],f=a?` + `,Dre=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function ju(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?` let localRow = i32(localId.y); let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${i}; - let globalColStart = i32(workgroupId.x) * ${p}; + let globalRowStart = i32(workgroupId.y) * ${p}; + let globalColStart = i32(workgroupId.x) * ${u}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { - ${P3(t)} + for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { + ${z3(t)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalColStart + inputCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${o}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } @@ -5549,58 +5558,58 @@ return a / b;`,x7=` } workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { let gRow = globalRowStart + localRow + innerRow * ${e[1]}; - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${e[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } `:` - let tileRow = i32(localId.y) * rowPerThread; - let tileCol = i32(localId.x) * colPerThread; + let tileRow = i32(localId.y) * ${h}; + let tileCol = i32(localId.x) * ${g}; - let globalRow = i32(globalId.y) * rowPerThread; - let globalCol = i32(globalId.x) * colPerThread; - let globalRowStart = i32(workgroupId.y) * ${i}; + let globalRow = i32(globalId.y) * ${h}; + let globalCol = i32(globalId.x) * ${g}; + let globalRowStart = i32(workgroupId.y) * ${p}; - let tileRowA = i32(localId.y) * ${l}; - let tileColA = i32(localId.x) * ${m}; - let tileRowB = i32(localId.y) * ${d}; + let tileRowA = i32(localId.y) * ${m}; + let tileColA = i32(localId.x) * ${d}; + let tileRowB = i32(localId.y) * ${f}; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${m}; innerRow++) { + for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${P3(t)} + ${z3(t)} } } // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${f}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, + mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol + innerCol); } } - kStart = kStart + tileInner; + kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. - var BCached : array; - for (var k = 0; k < tileInner; k = k + 1) { - for (var inner = 0; inner < colPerThread; inner = inner + 1) { + var BCached : array; + for (var k = 0; k < ${o}; k++) { + for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - ${are(t)} - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + ${Dre(t)} + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } @@ -5609,72 +5618,72 @@ return a / b;`,x7=` workgroupBarrier(); } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `;return` - var mm_Asub : array, ${c}>; - var mm_Bsub : array, ${o}>; - const rowPerThread = ${r[1]}; - const colPerThread = ${r[0]}; - const tileInner = ${o}; + var mm_Asub : array, ${l}>; + var mm_Bsub : array, ${o}>; - ${se()} { + ${Q()} { let batch = ${n?"0":"i32(globalId.z)"}; - let numTiles = ${n?`${Math.ceil(s/o)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; + let batchA = ${n||!i?"batch":"batch % uniforms.aShape[0]"}; + let batchB = ${n||!i?"batch":"batch % uniforms.bShape[0]"}; + let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; - var acc : array, rowPerThread>; + var acc : array, ${h}>; // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + for (var innerRow = 0; innerRow < ${h}; innerRow++) { + for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = 0.0; } } - ${f} + ${x} } - `}var ire=r=>r?` - mm_readA(batch, colA, globalRow), - mm_readA(batch, colA + 1, globalRow), - mm_readA(batch, colA + 2, globalRow), - mm_readA(batch, colA + 3, globalRow) + `}var Fre=r=>r?` + mm_readA(batchA, colA, globalRow), + mm_readA(batchA, colA + 1, globalRow), + mm_readA(batchA, colA + 2, globalRow), + mm_readA(batchA, colA + 3, globalRow) `:` - mm_readA(batch, globalRow, colA), - mm_readA(batch, globalRow, colA + 1), - mm_readA(batch, globalRow, colA + 2), - mm_readA(batch, globalRow, colA + 3) - `;function ure(r,e=!1){return y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`),` - const tileSize = ${r[0]*4}; + mm_readA(batchA, globalRow, colA), + mm_readA(batchA, globalRow, colA + 1), + mm_readA(batchA, globalRow, colA + 2), + mm_readA(batchA, globalRow, colA + 3) + `;function Ore(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return` var mm_Asub : array, ${r[0]}>; - ${se()} { + ${Q()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); - let numTiles = (uniforms.dimInner - 1) / tileSize + 1; + let numTiles = (uniforms.dimInner - 1) / ${t} + 1; let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { + for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. - let colA = t * tileSize + tileCol * 4; - mm_Asub[tileCol] = vec4(${ire(e)}); + let colA = t * ${t} + tileCol * 4; + mm_Asub[tileCol] = vec4(${Fre(e)}); workgroupBarrier(); // Compute acc values for a single thread. - for (var k = 0; k < tileSize / 4; k = k + 1) { - let rowB = t * tileSize + k * 4; - let BCached = vec4(mm_readB(batch, rowB, globalCol), - mm_readB(batch, rowB + 1, globalCol), - mm_readB(batch, rowB + 2, globalCol), - mm_readB(batch, rowB + 3, globalCol)); + for (var k = 0; k < ${t/4}; k++) { + let rowB = t * ${t} + k * 4; + let BCached = vec4(mm_readB(batchB, rowB, globalCol), + mm_readB(batchB, rowB + 1, globalCol), + mm_readB(batchB, rowB + 2, globalCol), + mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); @@ -5685,28 +5694,30 @@ return a / b;`,x7=` mm_write(batch, globalRow, globalCol, acc); } - `}var _g=class{constructor(e,t,o,n,s=!1,a=!1,i=null,p=null,u=null,c=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];if(this.isVec4=(l%4===0&&!s||t[1]%4===0&&s)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!s,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let f=tI(t[1],l,t[2],s);this.workgroupSize=f.workgroupSize,this.elementsPerThread=f.elementsPerThread}this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let m=i!=null,d=u!=null;m&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=c,this.transposeA=s,this.transposeB=a,this.addBias=m,this.activation=p,this.hasPreluActivationWeights=d,this.batchAEqualOne=o,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` - ${ur(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?ure(this.workgroupSize,this.transposeA):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)} - `}};function pre(){return` - var sumValues : array; - ${se()} { + `}var Eg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=dI(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` + ${pr(this.activation,this.hasPreluActivationWeights,this.isVec4)} + ${Wl(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Ore(this.workgroupSize,this.transposeA):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}};function Pre(r){return` + var sumValues : array; + ${Q()} { let coords = getOutputCoords(); let batch = coords[0]; + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; - for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) { - let dataA = mm_readA(batch, row, k); - let dataB = mm_readB(batch, k, col); + for (var k = i32(localId.x); k < Length; k = k + ${r}) { + let dataA = mm_readA(batchA, row, k); + let dataB = mm_readB(batchB, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); - for(var currentSize = workgroupSizeX / 2u; currentSize > 1u; + for(var currentSize = ${r/2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { @@ -5720,11 +5731,11 @@ return a / b;`,x7=` mm_write(batch, row, col, sum); } } - `}var Eg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize);let u=a!=null,c=p!=null;u&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=t,this.batchBEqualOne=o,this.shaderKey=`matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` - ${ur(this.activation,this.hasPreluActivationWeights)} - ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} - ${pre()} - `}};function cre(r){let e=r[1],t=r[0],o=e>t?e:t;return` + `}var Ag=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return` + ${pr(this.activation,this.hasPreluActivationWeights)} + ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Pre(this.workgroupSize[0])} + `}};function Mre(r){let e=r[1],t=r[0],o=e>t?e:t;return` var mm_Asub : array, ${e}>; var mm_Bsub : array, ${o}>; @@ -5734,12 +5745,14 @@ return a / b;`,x7=` // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. - ${se()} { + ${Q()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); + let batchA = batch % uniforms.aShape[0]; + let batchB = batch % uniforms.bShape[0]; // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${o} + 1; @@ -5747,9 +5760,9 @@ return a / b;`,x7=` var globalColA = tileCol; var globalRowB = 0; - var regA = mm_readA(batch, globalRow, globalColA); - var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + var regA = mm_readA(batchA, globalRow, globalColA); + var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; @@ -5760,9 +5773,9 @@ return a / b;`,x7=` workgroupBarrier(); - regA = mm_readA(batch, globalRow, globalColA); - regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); - regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); + regA = mm_readA(batchA, globalRow, globalColA); + regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); + regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; @@ -5774,59 +5787,48 @@ return a / b;`,x7=` mm_write(batch, globalRow, globalCol, acc); } - `}var $g=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` - ${ur(this.activation,this.hasPreluActivationWeights)} - ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} - ${cre(this.workgroupSize)} - `}};var Ag=class{constructor(e,t,o,n,s=!1,a=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(s&&this.outputShape[1]%4===0||!s&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=re(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=s,this.transposeB=a,this.batchAEqualOne=o,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=n=>` - for (var i = 0; i < ${n}; i = i + 1) - { - var oldValue = atomicLoad(&(result[flatIndex + i])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + ${n>1?"value[i]":"value"}; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } - } - `,t=this.isVec4?4:1;return` - ${iI(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)} - fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t)}) { - let col = colIn * ${t}; + `}var Rg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` + ${pr(this.activation,this.hasPreluActivationWeights)} + ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Mre(this.workgroupSize)} + `}};var Dg=class{constructor(e,t,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Y(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return` + ${xI(!1,this.transposeB,!1,!1,!1,e)} + fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) { + let col = colIn * ${e}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. - ${e(t)} + for (var i = 0; i < ${e}; i = i + 1) { + ${Ic("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + } } } - ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},Rg=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` - ${ur(this.activation,this.hasPreluActivationWeights)} - ${se("index")} { + ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},Fg=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` + ${pr(this.activation,this.hasPreluActivationWeights)} + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${Hr(this.addBias,this.activation)} + ${Gr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}};var Fg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` - ${se("index")} { + `}};var Og=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function dr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Fg(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var M3={kernelName:Cs,backendName:"webgpu",kernelFunc:dr};function de(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var L3={kernelName:Ns,backendName:"webgpu",kernelFunc:de};function Gu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=br.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=de({inputs:{x:r},backend:n,attrs:{shape:k}}),A=de({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=x===1,M=b===1,L=[$,A],W=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],V,U,q=[D,d,f],H=O().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(H<0){let X=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Z=X>0?X:n.thresholdToIncreaseWorkgroups,ee=D*Math.ceil(d/32)*Math.ceil(f/32);ee<=Z||d<=8&&ee<=Z*2?D*d*f<=128?H=Ao.MatMulReduceProgram:D===1&&m>=2e3?H=Ao.MatMulSplitKProgram:H=Ao.MatMulSmallOutputSizeProgram:H=Ao.MatMulPackedProgram}switch(H){case Ao.MatMulReduceProgram:V=new Eg(q,P,M,t,o,s,p,a);break;case Ao.MatMulSplitKProgram:{if(U=dr({backend:n,attrs:{shape:q,value:0,dtype:r.dtype}}),V=new Ag(q,m,P,M,t,o),s||p){U=n.runWebGPUProgram(V,L,r.dtype,W,U);let Z=new Rg(U.shape,s,p,a),ee=null,Y=[U];s&&Y.push(s),a&&Y.push(a),p==="leakyrelu"&&(ee=[{type:"float32",data:[i]}],Z.uniforms+=" alpha : f32,");let J=n.runWebGPUProgram(Z,Y,U.dtype,ee);R.push(U);let ie=de({inputs:{x:J},backend:n,attrs:{shape:w}});R.push(J);for(let pe of R)n.disposeData(pe.dataId);return ie}break}case Ao.MatMulSmallOutputSizeProgram:V=new $g(k,_,q,t,o,s,p,a);break;case Ao.MatMulPackedProgram:let X=n.adapterInfo.isIntel();V=new _g(k,q,P,M,t,o,s,p,a,X);break;default:throw new Error(`Unsupported MatMulProgramType ${H}.`)}s&&L.push(s),a&&L.push(a),p==="leakyrelu"&&(W.push({type:"float32",data:[i]}),V.uniforms+=" alpha : f32,"),U=n.runWebGPUProgram(V,L,r.dtype,W,U);let j=de({inputs:{x:U},backend:n,attrs:{shape:w}});R.push(U);for(let X of R)n.disposeData(X.dataId);return j}function lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Gu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var B3={kernelName:fo,backendName:"webgpu",kernelFunc:lre};var zl=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function fr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Og(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var W3={kernelName:ws,backendName:"webgpu",kernelFunc:fr};function me(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var U3={kernelName:_s,backendName:"webgpu",kernelFunc:me};function Xu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=me({inputs:{x:r},backend:n,attrs:{shape:k}}),A=me({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=[E,A],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,V,z=[D,d,f],U=O().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let H=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),q=H>0?H:n.thresholdToIncreaseWorkgroups,Z=D*Math.ceil(d/32)*Math.ceil(f/32);Z<=q||d<=8&&Z<=q*2?D*d*f<=128?U=Ro.MatMulReduceProgram:D===1&&m>=2e3?U=Ro.MatMulSplitKProgram:U=Ro.MatMulSmallOutputSizeProgram:U=Ro.MatMulPackedProgram}switch(U){case Ro.MatMulReduceProgram:L=new Ag(z,t,o,s,p,a);break;case Ro.MatMulSplitKProgram:{if(V=fr({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Dg(z,m,t,o),s||p){V=n.runWebGPUProgram(L,P,r.dtype,M,V);let q=new Fg(V.shape,s,p,a),Z=null,ee=[V];s&&ee.push(s),a&&ee.push(a),p==="leakyrelu"&&(Z=[{type:"float32",data:[i]}],q.uniforms+=" alpha : f32,");let oe=n.runWebGPUProgram(q,ee,V.dtype,Z);R.push(V);let J=me({inputs:{x:oe},backend:n,attrs:{shape:w}});R.push(oe);for(let te of R)n.disposeData(te.dataId);return J}break}case Ro.MatMulSmallOutputSizeProgram:L=new Rg(k,_,z,t,o,s,p,a);break;case Ro.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Eg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&P.push(s),a&&P.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),V=n.runWebGPUProgram(L,P,r.dtype,M,V);let K=me({inputs:{x:V},backend:n,attrs:{shape:w}});R.push(V);for(let H of R)n.disposeData(H.dataId);return K}function Lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Xu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var G3={kernelName:ho,backendName:"webgpu",kernelFunc:Lre};var Ul=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Ic(this.op,!1)} + ${kc(this.op,!1)} } - ${se("index")} { + ${Q("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); @@ -5835,16 +5837,19 @@ return a / b;`,x7=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}};var Hu=class{constructor(e,t,o){this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",o=` + `}};var Yu=class{constructor(e,t,o){this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",o=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${Ic(this.op,this.isVec4)} + let isNaN = false; + { + ${kc(this.op,this.isVec4)} + } }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${o} var sharedBuf : array; - ${se("index")} { + ${Q("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { @@ -5860,25 +5865,25 @@ return a / b;`,x7=` } `}else e=` ${o} - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); let b = getBByOutputIndex(index); setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function Ft(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var V3={kernelName:mo,backendName:"webgpu",kernelFunc:Ft};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:t}),p=Ft({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var z3={kernelName:ei,backendName:"webgpu",kernelFunc:po};var Ro=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let o=128;this.workgroupSize=[o,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function Dt(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var H3={kernelName:mo,backendName:"webgpu",kernelFunc:Dt};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var K3={kernelName:ri,backendName:"webgpu",kernelFunc:po};var Hr=class{constructor(e,t,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${Ha(this.op,!1)} + ${ja(this.op,!1)} } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } - `}};function Se({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Ro(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function ot({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ye.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new zl(ye.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new zl(ye.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?S.fromUint8ToStringArray(l):l,f=a.dtype==="string"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:W3,castImpl:U3,ceilImpl:G3,concatImpl:H3,equalImpl:q3,expImpl:K3,expm1Impl:j3,floorImpl:X3,gatherNdImpl:Y3,gatherV2Impl:Q3,greaterEqualImpl:Z3,greaterImpl:J3,lessEqualImpl:eM,lessImpl:tM,logImpl:rM,maxImpl:oM,maximumImpl:nM,minimumImpl:sM,multiplyImpl:aM,negImpl:iM,notEqualImpl:uM,prodImpl:pM,rangeImpl:cM,rsqrtImpl:lM,scatterImpl:mM,simpleAbsImpl:dM,sliceImpl:fM,stridedSliceImpl:hM,stringNGramsImpl:gM,subImpl:xM,tileImpl:yM,topKImpl:bM,transposeImpl:CM,uniqueImpl:kNt}=Qp;var mre=Se({opType:Q.ABS,cpuKernelImpl:dM}),SM={kernelName:gs,backendName:"webgpu",kernelFunc:mre};var dre=Se({opType:Q.ACOS}),wM={kernelName:sa,backendName:"webgpu",kernelFunc:dre};var fre=Se({opType:Q.ACOSH}),IM={kernelName:aa,backendName:"webgpu",kernelFunc:fre};var hre=ot({opType:ye.ADD,cpuKernelImpl:W3,supportsComplex:!0}),vM={kernelName:eo,backendName:"webgpu",kernelFunc:hre};var Dg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` - ${se("index")} { + `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Hr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function Ze({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ge.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new Ul(ge.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new Ul(ge.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?S.fromUint8ToStringArray(l):l,f=a.dtype==="string"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:q3,castImpl:j3,ceilImpl:X3,concatImpl:Y3,equalImpl:Q3,expImpl:Z3,expm1Impl:J3,floorImpl:eM,gatherNdImpl:tM,gatherV2Impl:rM,greaterEqualImpl:oM,greaterImpl:nM,lessEqualImpl:sM,lessImpl:aM,logImpl:iM,maxImpl:uM,maximumImpl:pM,minimumImpl:cM,multiplyImpl:lM,negImpl:mM,notEqualImpl:dM,prodImpl:fM,rangeImpl:hM,rsqrtImpl:gM,scatterImpl:xM,simpleAbsImpl:yM,sliceImpl:bM,stridedSliceImpl:CM,stringNGramsImpl:SM,subImpl:wM,tileImpl:IM,topKImpl:vM,transposeImpl:kM,uniqueImpl:iTt}=Zp;var Bre=xe({opType:X.ABS,cpuKernelImpl:yM}),NM={kernelName:ys,backendName:"webgpu",kernelFunc:Bre};var Vre=xe({opType:X.ACOS}),TM={kernelName:aa,backendName:"webgpu",kernelFunc:Vre};var zre=xe({opType:X.ACOSH}),_M={kernelName:ia,backendName:"webgpu",kernelFunc:zre};var Wre=Ze({opType:ge.ADD,cpuKernelImpl:q3,supportsComplex:!0}),$M={kernelName:eo,backendName:"webgpu",kernelFunc:Wre};var Pg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` + ${Q("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { @@ -5889,12 +5894,11 @@ return a / b;`,x7=` } } } - `}};function gre(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Dg(s);return t.runWebGPUProgram(a,o,n)}var kM={kernelName:Mo,backendName:"webgpu",kernelFunc:gre};var Og=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),` - const tileSize = ${this.workgroupSize[0]}; + `}};function Ure(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Pg(s);return t.runWebGPUProgram(a,o,n)}var EM={kernelName:Po,backendName:"webgpu",kernelFunc:Ure};var Mg=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; - ${se()} { - var x = i32(workgroupId.x) * tileSize + i32(localId.x); - var y = i32(workgroupId.y) * tileSize + i32(localId.y); + ${Q()} { + var x = i32(workgroupId.x) * ${e} + i32(localId.x); + var y = i32(workgroupId.y) * ${e} + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { @@ -5902,15 +5906,15 @@ return a / b;`,x7=` } workgroupBarrier(); - x = i32(workgroupId.y) * tileSize + i32(localId.x); - y = i32(workgroupId.x) * tileSize + i32(localId.y); + x = i32(workgroupId.y) * ${e} + i32(localId.x); + y = i32(workgroupId.x) * ${e} + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); } } - `}};var Pg=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o"} bestValue) - { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="0.0");let o=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` + { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="0.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` - var xBestValues : array; + var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } - ${se("index")} { - let outputIndex = index / i32(workgroupSizeX); + ${Q("index")} { + let outputIndex = index / ${o}; let offset = getOffset(outputIndex); var bestValue = ${t}; let Length = uniforms.reduceSize; - let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX); + let WorkPerThread = DIV_CEIL(u32(Length), ${o}u); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { + k = k + ${o}) { let candidate = f32(x[offset + k]); ${e} } xBestValues[localId.x] = bestValue; workgroupBarrier(); - var reduceSize = min(u32(Length), workgroupSizeX); + var reduceSize = min(u32(Length), ${o}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -5965,29 +5969,29 @@ return a / b;`,x7=` } if (localId.x == 0u && outputIndex < uniforms.size) { - ${o} + ${n} } } - `}};function qr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=oM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=pM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o==="mean"?"float32":ka(r.dtype),w=[{type:"int32",data:[h]}],k=new Mg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=de({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"all",t)}var TM={kernelName:Lo,backendName:"webgpu",kernelFunc:yre};function bre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"any",t)}var _M={kernelName:Bo,backendName:"webgpu",kernelFunc:bre};var vc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ue(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type="plain",this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=re(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${$o(this.inputShape.length-1)}`,t=()=>{let o="";if(this.outputShape.length===1)this.inputShape.length!==1&&(o+="outputCoords,");else for(let n=0;nn.disposeData(h.dataId)),f}function Hre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,"all",t)}var RM={kernelName:Mo,backendName:"webgpu",kernelFunc:Hre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,"any",t)}var DM={kernelName:Lo,backendName:"webgpu",kernelFunc:Kre};var Nc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ae(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type="plain",this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ao(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s u32 { return ((a - 1u) / b + 1u); } ${` - var xBestIndices : array; - var xBestValues : array; + var xBestIndices : array; + var xBestValues : array; `} - ${se("index")} { - let outputIndex = index / i32(workgroupSizeX); - let reduceLength = ${e()}; + ${Q("index")} { + let outputIndex = index / ${e}; + let reduceLength = ${t()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; let outputCoords = getCoordsFromIndex(outputIndex); for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size; - k = k + i32(workgroupSizeX)) { - let candidate = getX(${t()} k); + k = k + ${e}) { + let candidate = getX(${o()} k); if (!isnan(candidate) && candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = k; @@ -5997,7 +6001,7 @@ return a / b;`,x7=` xBestIndices[localId.x] = bestIndex; workgroupBarrier(); - var reduceSize = min(u32(reduceLength), workgroupSizeX); + var reduceSize = min(u32(reduceLength), ${e}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); @@ -6018,14 +6022,14 @@ return a / b;`,x7=` } } `:` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; - var bestValue = getX(${t()} 0); - let reduceLength = ${e()}; + var bestValue = getX(${o()} 0); + let reduceLength = ${t()}; for (var i = 1; i < reduceLength; i++) { - let candidate = getX(${t()} i); + let candidate = getX(${o()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; @@ -6034,8 +6038,8 @@ return a / b;`,x7=` setOutputAtIndexI32(index, bestIndex); } } - `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var EM={kernelName:Vo,backendName:"webgpu",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var $M={kernelName:Za,backendName:"webgpu",kernelFunc:Sre};var wre=Se({opType:Q.ASIN}),AM={kernelName:ia,backendName:"webgpu",kernelFunc:wre};var Ire=Se({opType:Q.ASINH}),RM={kernelName:ua,backendName:"webgpu",kernelFunc:Ire};var vre=Se({opType:Q.ATAN}),FM={kernelName:pa,backendName:"webgpu",kernelFunc:vre};var kre=ot({opType:ye.ATAN2}),DM={kernelName:la,backendName:"webgpu",kernelFunc:kre};var Nre=Se({opType:Q.ATANH}),OM={kernelName:ca,backendName:"webgpu",kernelFunc:Nre};var Wl=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),` - ${se("index")} { + `}};function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var FM={kernelName:Bo,backendName:"webgpu",kernelFunc:qre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var OM={kernelName:ei,backendName:"webgpu",kernelFunc:jre};var Xre=xe({opType:X.ASIN}),PM={kernelName:ua,backendName:"webgpu",kernelFunc:Xre};var Yre=xe({opType:X.ASINH}),MM={kernelName:pa,backendName:"webgpu",kernelFunc:Yre};var Qre=xe({opType:X.ATAN}),LM={kernelName:ca,backendName:"webgpu",kernelFunc:Qre};var Zre=Ze({opType:ge.ATAN2}),BM={kernelName:ma,backendName:"webgpu",kernelFunc:Zre};var Jre=xe({opType:X.ATANH}),VM={kernelName:la,backendName:"webgpu",kernelFunc:Jre};var Gl=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6067,8 +6071,8 @@ return a / b;`,x7=` setOutputAtIndex(index, ${t}); } } - `}};var Lg=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` - ${se("index")} { + `}};var Vg=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; @@ -6082,8 +6086,47 @@ return a / b;`,x7=` setOutputAtIndex(index, value); } } - `}};function Ul(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return qr(n,s,a,"max",t)}var PM={kernelName:yn,backendName:"webgpu",kernelFunc:Ul};function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"mean",t)}var MM={kernelName:Sn,backendName:"webgpu",kernelFunc:uI};function Bg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Ft({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=de({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=uI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=Ul({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=de({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Lg(e):(t==="avg"?n=new Wl(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Wl(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,"avg",t)}var LM={kernelName:zo,backendName:"webgpu",kernelFunc:Tre};function _re(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Gu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var BM={kernelName:Wo,backendName:"webgpu",kernelFunc:_re};var Vg=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Rt(this.rank),t=Ere(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`),` - ${se("index")} { + `}};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Kr(n,s,a,"max",t)}var zM={kernelName:yn,backendName:"webgpu",kernelFunc:Hl};function yI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,"mean",t)}var WM={kernelName:Sn,backendName:"webgpu",kernelFunc:yI};function zg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Dt({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=me({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=yI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=Hl({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=me({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Vg(e):(t==="avg"?n=new Gl(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Gl(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function eoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,"avg",t)}var UM={kernelName:Vo,backendName:"webgpu",kernelFunc:eoe};var Wg=class{constructor(e){this.variableNames=["dy"],this.uniforms=`stride : vec2, pads : vec2, dilation : vec2, filterDims : vec2, + outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avg_pool2d_backprop"}getUserCode(){return` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let batch = coords[0]; + let d = coords[3]; + + let dyRCCorner = vec2(coords.yz) - uniforms.pads; + let dyRCorner = dyRCCorner.x; + let dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) { + let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]); + + if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { + continue; + } + let idyR = i32(dyR); + + for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) { + let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]); + + if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { + continue; + } + let idyC = i32(dyC); + + let dyValue = getDy(batch, idyR, idyC, d); + + dotProd = dotProd + dyValue * uniforms.avgMultiplier; + } + } + setOutputAtIndex(index, dotProd); + } + } + `}};function toe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fI([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new Wg(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var GM={kernelName:mp,backendName:"webgpu",kernelFunc:toe};function roe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Xu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var HM={kernelName:zo,backendName:"webgpu",kernelFunc:roe};var Ug=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Rt(this.rank),t=ooe(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`),` + ${Q("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); @@ -6092,59 +6135,44 @@ return a / b;`,x7=` setOutputAtIndex(index, getSource(${t})); } } - `}},pI=["x","y","z","w","u","v"];function Ere(r){if(r===1)return"sourceLoc";if(r<=6)return pI.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function ds(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.tensorMap.get(n.dataId),m=fM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Vg(i,p),c=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var VM={kernelName:_s,backendName:"webgpu",kernelFunc:ds};var $re=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=de({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=de({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ds({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},zM={kernelName:xs,backendName:"webgpu",kernelFunc:$re};var Are=` + `}},bI=["x","y","z","w","u","v"];function ooe(r){if(r===1)return"sourceLoc";if(r<=6)return bI.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function fs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.tensorMap.get(n.dataId),m=bM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Ug(i,p),c=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KM={kernelName:Es,backendName:"webgpu",kernelFunc:fs};var noe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=me({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),x=fs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},qM={kernelName:bs,backendName:"webgpu",kernelFunc:noe};var soe=` fn bincount_write(index: i32, value: f32) { - var oldValue = atomicLoad(& (result[index])); - var exchanged = false; - for (; !exchanged;) { - let newValueF32 = bitcast(oldValue) + value; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak( - &(result[index]), oldValue, newValue); - oldValue = res.old_value; - exchanged = res.exchanged; - } + ${Ic("&result[index]","value","float32")} } -`,Rre=` +`,aoe=` fn bincount_write(index: i32, value: f32) { - result[index] = value; + atomicStore(&result[index], bitcast(value)); } -`,kc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` - ${this.binaryOutput?Rre:Are} - ${se("index")} { +`,Tc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` + ${this.binaryOutput?aoe:soe} + ${Q("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(index))":"1."}; + let value = ${this.binaryOutput?1:this.hasWeights?"getW(index)":"1."}; bincount_write(indexVal, value); } }`:`let coord = getCoordsFromIndex(index); if (coordsInBounds2D(coord, uniforms.xShape)) { let indexVal = i32(getX(coord[0], coord[1])); if (indexVal < uniforms.binCountSize) { - let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(coord[0], coord[1]))":"1."}; + let value = ${this.binaryOutput?1:this.hasWeights?"getW(coord[0], coord[1])":"1."}; bincount_write(coord.x * uniforms.binCountSize + indexVal, value); } }`} } - `}};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=dr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new kc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var WM={kernelName:Ja,backendName:"webgpu",kernelFunc:Fre};var cI=ot({opType:ye.NOT_EQUAL,dtype:"bool",cpuKernelImpl:uM}),UM={kernelName:Nn,backendName:"webgpu",kernelFunc:cI};function qa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var GM={kernelName:ai,backendName:"webgpu",kernelFunc:qa};function HM(r,e){let t=new Ro(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ft({inputs:{x:n},backend:t});let a=Vr(n.shape),i=lI({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=qa({inputs:{input:n},backend:t}),i=lI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=U3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return HM(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=cI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var qM={kernelName:co,backendName:"webgpu",kernelFunc:lI};var Dre=Se({opType:Q.CEIL,cpuKernelImpl:G3}),KM={kernelName:Uo,backendName:"webgpu",kernelFunc:Dre};var zg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` - ${se("index")} { + `}};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=fr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Tc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var jM={kernelName:ti,backendName:"webgpu",kernelFunc:ioe};var CI=Ze({opType:ge.NOT_EQUAL,dtype:"bool",cpuKernelImpl:dM}),XM={kernelName:Nn,backendName:"webgpu",kernelFunc:CI};function Xa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var YM={kernelName:di,backendName:"webgpu",kernelFunc:Xa};function QM(r,e){let t=new Hr(r.shape,X.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function SI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Dt({inputs:{x:n},backend:t});let a=Br(n.shape),i=SI({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Xa({inputs:{input:n},backend:t}),i=SI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=j3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return QM(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=CI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var ZM={kernelName:co,backendName:"webgpu",kernelFunc:SI};var uoe=xe({opType:X.CEIL,cpuKernelImpl:X3}),JM={kernelName:Wo,backendName:"webgpu",kernelFunc:uoe};var Gg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + ${Q("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); - var clampedValue : vec4; - for (var i = 0; i < 4; i = i + 1) { - if (isnan(value[i])) { - clampedValue[i] = value[i]; - } else { - clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); - } - } - + var clampedValue = clamp( + value, vec4(uniforms.minVal), vec4(uniforms.maxVal)); + clampedValue = select(clampedValue, value, isnanVec4(value)); setOutputAtIndex(index, clampedValue); } } - `}};var Wg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` - ${se("index")} { + `}};var Hg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + ${Q("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { @@ -6154,8 +6182,8 @@ return a / b;`,x7=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function Ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new zg(n.shape):i=new Wg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var jM={kernelName:lo,backendName:"webgpu",kernelFunc:Ore};var Ug=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;s`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sqa({inputs:{input:C},backend:t})),h=r.map(C=>qu({inputs:{input:C},backend:t})),g=Nc(f,e,t),x=Nc(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return de({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=H3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Ug(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=de({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Pre(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>de({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function mI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Nc(p,s,t)}var YM={kernelName:ys,backendName:"webgpu",kernelFunc:mI};function Mre(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?` + `}};function Qu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var tL={kernelName:ci,backendName:"webgpu",kernelFunc:Qu};function _c(r,e,t){let o=r[0].dtype;if(o==="complex64"){let f=r.map(C=>Xa({inputs:{input:C},backend:t})),h=r.map(C=>Qu({inputs:{input:C},backend:t})),g=_c(f,e,t),x=_c(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return me({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=Y3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Kg(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=me({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function coe(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function wI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):_c(p,s,t)}var rL={kernelName:Cs,backendName:"webgpu",kernelFunc:wI};function loe(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6217,13 +6245,13 @@ return a / b;`,x7=` if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } - return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),$=r?kt(p):kt(i);return` - ${ur(s,a,u===4,4)} + return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),E=r?kt(p):kt(i);return` + ${pr(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} { ${r?C:w} } - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} { + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} { ${r?w:C} } @@ -6234,14 +6262,14 @@ return a / b;`,x7=` var value = valueIn; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${d} - ${Hr(n,s)} + ${Gr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var Gg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),s&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Uu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` - ${Mre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} + }`}var qg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),s&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):ju(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + ${loe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} - `}};var Hg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` - ${ur(this.activation,this.hasPreluActivationWeights,!1,4)} + `}};var jg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + ${pr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -6262,11 +6290,11 @@ return a / b;`,x7=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${Hr(this.addBias,this.activation)} + ${Gr(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } - ${se("index")} { + ${Q("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} @@ -6286,9 +6314,9 @@ return a / b;`,x7=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}};var qg=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, - inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` - ${se("index")} { + `}};var Xg=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, + inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` + ${Q("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; @@ -6310,7 +6338,97 @@ return a / b;`,x7=` setOutputAtIndex(index, value); } } - `}};function Kg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=de({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=de({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Kg(s.shape,p);x!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Kg(n.shape,p);x!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Gu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=de({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Bre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b==="channelsLast",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],$=new qg(_,C),A=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],R=o.runWebGPUProgram($,[r],r.dtype,A),D=[];D.push(R);let P=de({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Kg(s.shape,C);U!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Kg(n.shape,C);U!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let W=Gu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),V=de({inputs:{x:W},backend:o,attrs:{shape:t.outShape}});D.push(W);for(let U of D)o.disposeData(U.dataId);return V}function jg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat==="channelsLast",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return Lre({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return Bre({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Hg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[$]},{type:"int32",data:[A]});let R=o.adapterInfo.isIntel();g=new Gg(t,_,$,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=de({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=de({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Vre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return jg({x:n,filter:s,convInfo:m,backend:o})}var QM={kernelName:Go,backendName:"webgpu",kernelFunc:Vre};function zre(r=4){let e=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function Yg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function moe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=me({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=me({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Yg(s.shape,p);x!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Yg(n.shape,p);x!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Xu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=me({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function doe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b==="channelsLast",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],E=new Xg(_,C),A=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],R=o.runWebGPUProgram(E,[r],r.dtype,A),D=[];D.push(R);let P=me({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Yg(s.shape,C);U!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Yg(n.shape,C);U!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let V=Xu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=me({inputs:{x:V},backend:o,attrs:{shape:t.outShape}});D.push(V);for(let U of D)o.disposeData(U.dataId);return z}function Qg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat==="channelsLast",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return moe({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return doe({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new jg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[A]});let R=o.adapterInfo.isIntel();g=new qg(t,_,E,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=me({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=me({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function foe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return Qg({x:n,filter:s,convInfo:m,backend:o})}var oL={kernelName:Uo,backendName:"webgpu",kernelFunc:foe};var Zg=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return` + ${Q("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let batch = coords[0]; + let d1 = coords[${o}]; + + let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { + let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); + let wRPerm = uniforms.filterDims.x - 1 - wR; + if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR = i32(dyR); + + for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { + let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); + let wCPerm = uniforms.filterDims.y - 1 - wC; + if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC = i32(dyC); + + for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { + if (${this.isChannelsLast}) { + let xValue = getDy(batch, idyR, idyC, d2); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } else { + let xValue = getDy(batch, d2, idyR, idyC); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } + + } + } + } + setOutputAtIndex(index, dotProd); + } + } + `}},Jg=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pad : vec2, stride : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + ${Q("index")} { + if(index < uniforms.size) { + let coords = getCoordsFromIndex(index); + let wR = coords[0]; + let wC = coords[1]; + let d1 = coords[2]; + let d2 = coords[3]; + + // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var b = 0; b < uniforms.batchSize; b = b + 1) { + for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) { + let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0]; + if (xR < 0 || xR >= uniforms.inHeight) { + continue; + } + + for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) { + let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1]; + + if (xC < 0 || xC >= uniforms.inWidth) { + continue; + } + + if (${this.isChannelsLast}) { + let dyValue = getDy(b, yR, yC, d2); + let xValue = getX(b, xR, xC, d1); + dotProd = dotProd + xValue * dyValue; + } else { + let dyValue = getDy(b, d2, yR, yC); + let xValue = getX(b, d1, xR, xC); + dotProd = dotProd + xValue * dyValue; + } + } + } + } + setOutputAtIndex(index, dotProd); + } + } + `}};function hoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Jg(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var nL={kernelName:oi,backendName:"webgpu",kernelFunc:hoe};function goe(r=4){let e=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6373,60 +6491,11 @@ return a / b;`,x7=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; } - }`}var Xg=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize):Uu(this.elementsPerThread,this.workgroupSize);return` - ${zre(this.isVec4?4:1)} + }`}var ex=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize):ju(this.elementsPerThread,this.workgroupSize);return` + ${goe(this.isVec4?4:1)} ${e} - `}};var Yg=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return` - ${se("index")} { - if(index < uniforms.size) { - let coords = getCoordsFromIndex(index); - let batch = coords[0]; - let d1 = coords[${o}]; - - let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads; - let dyRCorner = dyCorner.x; - let dyCCorner = dyCorner.y; - - // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). - // ? = to be determined. : = across all values in that axis. - var dotProd = 0.0; - for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { - let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); - let wRPerm = uniforms.filterDims.x - 1 - wR; - if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || - wRPerm < 0) { - continue; - } - let idyR = i32(dyR); - - for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { - let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); - let wCPerm = uniforms.filterDims.y - 1 - wC; - if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || - fract(dyC) > 0.0 || wCPerm < 0) { - continue; - } - let idyC = i32(dyC); - - for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { - if (${this.isChannelsLast}) { - let xValue = getDy(batch, idyR, idyC, d2); - let wValue = getW(wRPerm, wCPerm, d1, d2); - dotProd = dotProd + xValue * wValue; - } else { - let xValue = getDy(batch, d2, idyR, idyC); - let wValue = getW(wRPerm, wCPerm, d1, d2); - dotProd = dotProd + xValue * wValue; - } - - } - } - } - setOutputAtIndex(index, dotProd); - } - } - `}};function Wre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Yg(m);else{f=new Xg(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var ZM={kernelName:Ho,backendName:"webgpu",kernelFunc:Wre};var Ure=Se({opType:Q.COS}),JM={kernelName:qo,backendName:"webgpu",kernelFunc:Ure};var Gre=Se({opType:Q.COSH}),eL={kernelName:Ko,backendName:"webgpu",kernelFunc:Gre};var Qg=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` - ${se("index")} { + `}};function xoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Zg(m);else{f=new ex(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var sL={kernelName:Go,backendName:"webgpu",kernelFunc:xoe};var yoe=xe({opType:X.COS}),aL={kernelName:Ho,backendName:"webgpu",kernelFunc:yoe};var boe=xe({opType:X.COSH}),iL={kernelName:Ko,backendName:"webgpu",kernelFunc:boe};var tx=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); @@ -6481,24 +6550,24 @@ return a / b;`,x7=` } } } - `}};var Hre=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Qg(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],"float32",l)},tL={kernelName:Yo,backendName:"webgpu",kernelFunc:Hre};var Ku;(function(r){r.Prod="*",r.Sum="+"})(Ku||(Ku={}));var Gl=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Ku.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${rL(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` - ${se("index")} { + `}};var Coe=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new tx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],"float32",l)},uL={kernelName:Xo,backendName:"webgpu",kernelFunc:Coe};var Zu;(function(r){r.Prod="*",r.Sum="+"})(Zu||(Zu={}));var Kl=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Zu.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${pL(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` + ${Q("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${oL(e,"coords",this.op)}; + let end = ${cL(e,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${oL(e,"coords",this.op)} = idx; - val ${this.op}= getX(${rL(e,"coords",this.op)}); + ${cL(e,"coords",this.op)} = idx; + val ${this.op}= getX(${pL(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function rL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function oL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zg(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Gl(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Gl(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Prod,n,t,s,a,i)}var nL={kernelName:jo,backendName:"webgpu",kernelFunc:qre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Sum,n,t,s,a,i)}var sL={kernelName:Xo,backendName:"webgpu",kernelFunc:Kre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=dr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new kc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var aL={kernelName:ti,backendName:"webgpu",kernelFunc:jre};var Jg=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` - ${se("index")} { + `}};function pL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function cL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function rx(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Kl(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Kl(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Soe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Prod,n,t,s,a,i)}var lL={kernelName:qo,backendName:"webgpu",kernelFunc:Soe};function woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Sum,n,t,s,a,i)}var mL={kernelName:jo,backendName:"webgpu",kernelFunc:woe};function Ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=fr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Tc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var dL={kernelName:ni,backendName:"webgpu",kernelFunc:Ioe};var ox=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6517,8 +6586,8 @@ return a / b;`,x7=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Xre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new Jg(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iL={kernelName:Qo,backendName:"webgpu",kernelFunc:Xre};var ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` - ${ur(this.activation,this.hasPreluActivation,!1,4)} + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new ox(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var fL={kernelName:Yo,backendName:"webgpu",kernelFunc:voe};var nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + ${pr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -6531,7 +6600,7 @@ return a / b;`,x7=` return value; } - ${se()} { + ${Q()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pad; @@ -6574,13 +6643,13 @@ return a / b;`,x7=` value = fma(xVal, wVal, value); } } - ${Hr(this.addBias,this.activation)} + ${Gr(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};var Tc=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return` - ${ur(this.activation,this.hasPreluActivation,!0,4)} + `}};var $c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` + ${pr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -6589,14 +6658,12 @@ return a / b;`,x7=` return value; } - const strideHeight = ${this.convInfo.strideHeight}; - const strideWidth = ${this.convInfo.strideWidth}; - ${se()} { + ${Q()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; - let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad; + let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; @@ -6616,7 +6683,7 @@ return a / b;`,x7=` for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { - dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]); + dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]); } } } @@ -6626,16 +6693,16 @@ return a / b;`,x7=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${Hr(this.addBias,this.activation)} + ${Gr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}};var _c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, - filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` - ${ur(this.activation,this.hasPreluActivation,!1,4)} + `}};var Ec=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, + filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` + ${pr(this.activation,this.hasPreluActivation,!1,4)} - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; @@ -6694,11 +6761,51 @@ return a / b;`,x7=` } } } - ${Hr(this.addBias,this.activation)} + ${Gr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function Yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Tc(d):(g=new _c(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uL={kernelName:Zo,backendName:"webgpu",kernelFunc:Yre};var dI=ot({opType:ye.MUL,cpuKernelImpl:aM,supportsComplex:!0}),pL={kernelName:kn,backendName:"webgpu",kernelFunc:dI};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"sum",t)}var cL={kernelName:Hn,backendName:"webgpu",kernelFunc:Hl};function Qre(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Hl({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var lL={kernelName:ri,backendName:"webgpu",kernelFunc:Qre};var Zre=Se({opType:Q.ELU}),mL={kernelName:en,backendName:"webgpu",kernelFunc:Zre};var Jre=ot({opType:ye.EQUAL,dtype:"bool",cpuKernelImpl:q3}),dL={kernelName:tn,backendName:"webgpu",kernelFunc:Jre};var eoe=Se({opType:Q.ERF}),fL={kernelName:ma,backendName:"webgpu",kernelFunc:eoe};var fI=Se({opType:Q.EXP,cpuKernelImpl:K3,dtype:"float32"}),hL={kernelName:rn,backendName:"webgpu",kernelFunc:fI};function tx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),de({inputs:{x:s},backend:o,attrs:{shape:i}})}var gL={kernelName:bs,backendName:"webgpu",kernelFunc:tx};var toe=Se({opType:Q.EXPM1,cpuKernelImpl:j3}),xL={kernelName:da,backendName:"webgpu",kernelFunc:toe};var ql=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new $c(d):(g=new Ec(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var hL={kernelName:Qo,backendName:"webgpu",kernelFunc:koe};var sx=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); + setOutputAtIndex(index, value); + } + } + `}};function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=me({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new sx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=me({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var gL={kernelName:si,backendName:"webgpu",kernelFunc:Noe};var ax=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + ${Q("index")} { + if (index < uniforms.size) { + let neg_infinity = -3.4e38; + let coords = getOutputCoords(); + let batch = coords.x; + let d1 = coords.w; + let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad; + let hBeg = outTopLeftCorner.x; + let wBeg = outTopLeftCorner.y; + + var curVal = neg_infinity; + for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) { + let hIn = hBeg + h * uniforms.dilation[0]; + + if (hIn >= 0 && hIn < uniforms.xShape[1]) { + for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) { + let wIn = wBeg + w * uniforms.dilation[1]; + + if (wIn >= 0 && wIn < uniforms.xShape[2]) { + let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1); + if (val > curVal) { + curVal = val; + } + } + } + } + } + + setOutputAtIndex(index, curVal); + } + } + `}};function Toe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var xL={kernelName:ai,backendName:"webgpu",kernelFunc:Toe};var II=Ze({opType:ge.MUL,cpuKernelImpl:lM,supportsComplex:!0}),yL={kernelName:kn,backendName:"webgpu",kernelFunc:II};function ql(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,"sum",t)}var bL={kernelName:Kn,backendName:"webgpu",kernelFunc:ql};function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=ql({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var CL={kernelName:ii,backendName:"webgpu",kernelFunc:_oe};var $oe=xe({opType:X.ELU}),SL={kernelName:Jo,backendName:"webgpu",kernelFunc:$oe};var Eoe=Ze({opType:ge.EQUAL,dtype:"bool",cpuKernelImpl:Q3}),wL={kernelName:en,backendName:"webgpu",kernelFunc:Eoe};var Aoe=xe({opType:X.ERF}),IL={kernelName:da,backendName:"webgpu",kernelFunc:Aoe};var vI=xe({opType:X.EXP,cpuKernelImpl:Z3,dtype:"float32"}),vL={kernelName:tn,backendName:"webgpu",kernelFunc:vI};function ix(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),me({inputs:{x:s},backend:o,attrs:{shape:i}})}var kL={kernelName:Ss,backendName:"webgpu",kernelFunc:ix};var Roe=xe({opType:X.EXPM1,cpuKernelImpl:J3}),NL={kernelName:fa,backendName:"webgpu",kernelFunc:Roe};var jl=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -6725,14 +6832,14 @@ return a / b;`,x7=` return result; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function rx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=de({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ql("real",u),l=new ql("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=de({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function roe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!1,t)}var yL={kernelName:oi,backendName:"webgpu",kernelFunc:roe};var ox=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` - ${se("index")} { + `}};function ux(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new jl("real",u),l=new jl("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=me({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function Doe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!1,t)}var TL={kernelName:ui,backendName:"webgpu",kernelFunc:Doe};var px=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; @@ -6740,9 +6847,9 @@ return a / b;`,x7=` setOutputAtIndex(index, outputValue); } } - `}};var bL={kernelName:on,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ooe=Se({opType:Q.FLOOR,cpuKernelImpl:X3}),CL={kernelName:nn,backendName:"webgpu",kernelFunc:ooe};var noe=ot({opType:ye.INT_DIV,dtype:"int32"}),SL={kernelName:sn,backendName:"webgpu",kernelFunc:noe};var nx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}};var _L={kernelName:rn,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Foe=xe({opType:X.FLOOR,cpuKernelImpl:eM}),$L={kernelName:on,backendName:"webgpu",kernelFunc:Foe};var Ooe=Ze({opType:ge.INT_DIV,dtype:"int32"}),EL={kernelName:nn,backendName:"webgpu",kernelFunc:Ooe};var cx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; - ${se("index")} { + ${Q("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); @@ -6752,8 +6859,8 @@ return a / b;`,x7=` } } } - `}};var wL={kernelName:Zi,backendName:"webgpu",kernelFunc:soe},Ec,hI=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),sx=new Map;function soe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!="undefined"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d){let D=n;if(!sx.has(D)||sx.get(D).expired){let P={source:D};sx.set(D,t.device.importExternalTexture(P))}b={width:c,height:l,format:null,usage:null,texture:sx.get(D)}}else{if(f){let L=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ec==null||L!==hI)&&(hI=L,Ec=document.createElement("canvas").getContext("2d",{willReadFrequently:hI})),Ec.canvas.width=c,Ec.canvas.height=l,Ec.drawImage(n,0,0,c,l),n=Ec.canvas}let D=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,P="rgba8unorm",M=t.textureManager.acquireTexture(m[1],m[0],P,D);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:P,usage:D,texture:M}}let C=y.sizeFromShape(m),w=y.computeStrides(m),k=new nx(m,s,d),_=[{type:"uint32",data:[C]},{type:"uint32",data:[s]},{type:"uint32",data:[...w]}],$=t.makeTensorInfo([l,c],"int32"),A=t.tensorMap.get($.dataId);A.resourceInfo=b;let R=t.runWebGPUProgram(k,[$],"int32",_);return t.disposeData($.dataId),R}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let w=0;w(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}};var IL={kernelName:an,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new ax(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function aoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return jg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var vL={kernelName:ho,backendName:"webgpu",kernelFunc:aoe};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=S.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],w;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?w=new Tc(h,x,m,b):(w=new _c(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),w.uniforms+=" alpha : f32,"),t.runWebGPUProgram(w,g,"float32",C)}var kL={kernelName:go,backendName:"webgpu",kernelFunc:ioe};var ix=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` - ${se("index")} { + `}};var RL={kernelName:sn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new mx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Moe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return Qg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var DL={kernelName:go,backendName:"webgpu",kernelFunc:Moe};function Loe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=S.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],w;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?w=new $c(h,x,m,b):(w=new Ec(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),w.uniforms+=" alpha : f32,"),t.runWebGPUProgram(w,g,"float32",C)}var FL={kernelName:xo,backendName:"webgpu",kernelFunc:Loe};var dx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; @@ -6779,8 +6886,8 @@ return a / b;`,x7=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function uoe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=de({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=de({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=Y3(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new ix(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=de({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var NL={kernelName:un,backendName:"webgpu",kernelFunc:uoe};var ux=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=poe(this.aShape);return` - ${se("index")} { + `}};function Boe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=me({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=me({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=tM(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new dx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=me({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var OL={kernelName:an,backendName:"webgpu",kernelFunc:Boe};var fx=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=Voe(this.aShape);return` + ${Q("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); @@ -6788,8 +6895,79 @@ return a / b;`,x7=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function poe(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=de({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var TL={kernelName:Ss,backendName:"webgpu",kernelFunc:gI};var coe=ot({opType:ye.GREATER,cpuKernelImpl:J3,dtype:"bool"}),_L={kernelName:pn,backendName:"webgpu",kernelFunc:coe};var loe=ot({opType:ye.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Z3}),EL={kernelName:cn,backendName:"webgpu",kernelFunc:loe};function moe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!0,t)}var $L={kernelName:ni,backendName:"webgpu",kernelFunc:moe};var doe=Se({opType:Q.IS_FINITE,dtype:"bool"}),AL={kernelName:fa,backendName:"webgpu",kernelFunc:doe};var foe=Se({opType:Q.IS_INF,dtype:"bool"}),RL={kernelName:ha,backendName:"webgpu",kernelFunc:foe};var hoe=Se({opType:Q.IS_NAN,dtype:"bool"}),FL={kernelName:ln,backendName:"webgpu",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Ro(n.shape,Q.LEAKYRELU);return i.uniforms="alpha : f32,",t.runWebGPUProgram(i,[n],"float32",a)}var DL={kernelName:mn,backendName:"webgpu",kernelFunc:goe};var xoe=ot({opType:ye.LESS,dtype:"bool",cpuKernelImpl:tM}),OL={kernelName:dn,backendName:"webgpu",kernelFunc:xoe};var yoe=ot({opType:ye.LESS_EQUAL,dtype:"bool",cpuKernelImpl:eM}),PL={kernelName:fn,backendName:"webgpu",kernelFunc:yoe};var boe=Se({opType:Q.LOG,cpuKernelImpl:rM}),ML={kernelName:hn,backendName:"webgpu",kernelFunc:boe};var Coe=Se({opType:Q.LOG1P}),LL={kernelName:ga,backendName:"webgpu",kernelFunc:Coe};var Soe=ot({opType:ye.LOGICAL_AND,dtype:"bool"}),BL={kernelName:gn,backendName:"webgpu",kernelFunc:Soe};var woe=Se({opType:Q.LOGICAL_NOT}),VL={kernelName:xn,backendName:"webgpu",kernelFunc:woe};var Ioe=ot({opType:ye.MAX,cpuKernelImpl:nM}),zL={kernelName:bn,backendName:"webgpu",kernelFunc:Ioe};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,"max",t)}var WL={kernelName:Cn,backendName:"webgpu",kernelFunc:voe};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"min",t)}var UL={kernelName:wn,backendName:"webgpu",kernelFunc:koe};var Noe=ot({opType:ye.MIN,cpuKernelImpl:sM}),GL={kernelName:In,backendName:"webgpu",kernelFunc:Noe};var px=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Rt(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${se("index")} { + `}};function Voe(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new fx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var PL={kernelName:Is,backendName:"webgpu",kernelFunc:NI};var zoe=Ze({opType:ge.GREATER,cpuKernelImpl:nM,dtype:"bool"}),ML={kernelName:un,backendName:"webgpu",kernelFunc:zoe};var Woe=Ze({opType:ge.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:oM}),LL={kernelName:pn,backendName:"webgpu",kernelFunc:Woe};function Uoe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!0,t)}var BL={kernelName:pi,backendName:"webgpu",kernelFunc:Uoe};var Goe=xe({opType:X.IS_FINITE,dtype:"bool"}),VL={kernelName:ha,backendName:"webgpu",kernelFunc:Goe};var Hoe=xe({opType:X.IS_INF,dtype:"bool"}),zL={kernelName:ga,backendName:"webgpu",kernelFunc:Hoe};var Koe=xe({opType:X.IS_NAN,dtype:"bool"}),WL={kernelName:cn,backendName:"webgpu",kernelFunc:Koe};function qoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Hr(n.shape,X.LEAKYRELU,"alpha : f32,");return t.runWebGPUProgram(i,[n],"float32",a)}var UL={kernelName:ln,backendName:"webgpu",kernelFunc:qoe};var joe=Ze({opType:ge.LESS,dtype:"bool",cpuKernelImpl:aM}),GL={kernelName:mn,backendName:"webgpu",kernelFunc:joe};var Xoe=Ze({opType:ge.LESS_EQUAL,dtype:"bool",cpuKernelImpl:sM}),HL={kernelName:dn,backendName:"webgpu",kernelFunc:Xoe};var hx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + ${Q("index")} { + if (index < uniforms.size) { + setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); + } + } + `}};function Yoe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new hx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var KL={kernelName:li,backendName:"webgpu",kernelFunc:Yoe};var Qoe=xe({opType:X.LOG,cpuKernelImpl:iM}),qL={kernelName:fn,backendName:"webgpu",kernelFunc:Qoe};var Zoe=xe({opType:X.LOG1P}),jL={kernelName:xa,backendName:"webgpu",kernelFunc:Zoe};var Joe=Ze({opType:ge.LOGICAL_AND,dtype:"bool"}),XL={kernelName:hn,backendName:"webgpu",kernelFunc:Joe};var ene=xe({opType:X.LOGICAL_NOT}),YL={kernelName:gn,backendName:"webgpu",kernelFunc:ene};var tne=Ze({opType:ge.LOGICAL_OR}),QL={kernelName:xn,backendName:"webgpu",kernelFunc:tne};var ZL=` + var powValue = 0.0; + let basis = uniforms.bias + uniforms.alpha * sum; + if (uniforms.beta == 0.5) { + powValue = inverseSqrt(basis); + } else if (uniforms.beta == 1.0) { + powValue = 1.0 / basis; + } else { + powValue = exp(log(basis) * (-uniforms.beta)); + } +`,gx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` + ${Q("index")} { + if (index < uniforms.size) { + let coords = getOutputCoords(); + let b = coords[0]; + let r = coords[1]; + let c = coords[2]; + let d = coords[3]; + + let x = getX(b, r, c, d); + var sum = 0.0; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let idx = d + i; + if (idx >= 0 && idx < uniforms.xShape[3]) { + let z = getX(b, r, c, idx); + sum = sum + z * z; + } + } + ${ZL} + + setOutputAtIndex(index, x * powValue); + } + } + `}},xx=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + var lrnSub: array; + const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; + const maxAllowRadius = ${this.maxAllowRadius}; + + ${Q()} { + let localDepth = i32(localId.x); + let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; + let xDepth = workgroupDepth + localDepth - maxAllowRadius; + let b = i32(globalId.z) / uniforms.xShape[1]; + let r = i32(globalId.z) - b * uniforms.xShape[1]; + let c = i32(globalId.y); + let d = workgroupDepth + localDepth; + + var x = 0.0; + if (xDepth >= 0 && xDepth < uniforms.xShape[3]) { + x = getX(b, r, c, xDepth); + } + lrnSub[localDepth] = x; + workgroupBarrier(); + + if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) { + var sum = 0.0; + let index = localDepth + maxAllowRadius; + for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { + let z = lrnSub[index + i]; + sum = sum + z * z; + } + ${ZL} + + setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); + } + } `}};function rne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new gx(n.shape):u=new xx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var JL={kernelName:mi,backendName:"webgpu",kernelFunc:rne};var one=Ze({opType:ge.MAX,cpuKernelImpl:pM}),eB={kernelName:bn,backendName:"webgpu",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,"max",t)}var tB={kernelName:Cn,backendName:"webgpu",kernelFunc:nne};function sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,"min",t)}var rB={kernelName:wn,backendName:"webgpu",kernelFunc:sne};var ane=Ze({opType:ge.MIN,cpuKernelImpl:cM}),oB={kernelName:In,backendName:"webgpu",kernelFunc:ane};var yx=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Rt(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${Q("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${o}); @@ -6805,16 +6983,16 @@ return a / b;`,x7=` setOutputAtIndex(index, getX(${p})); } } - `}};var HL={kernelName:vn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new px(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Toe=ot({opType:ye.MOD}),qL={kernelName:ya,backendName:"webgpu",kernelFunc:Toe};function _oe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=iM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Ro(o.shape,Q.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var KL={kernelName:ws,backendName:"webgpu",kernelFunc:_oe};function Eoe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Lt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var jL={kernelName:Tn,backendName:"webgpu",kernelFunc:Eoe};function $oe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Lt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var XL={kernelName:_n,backendName:"webgpu",kernelFunc:$oe};var cx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` - ${se("index")} { + `}};var nB={kernelName:vn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var ine=Ze({opType:ge.MOD}),sB={kernelName:ya,backendName:"webgpu",kernelFunc:ine};function une(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=mM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Hr(o.shape,X.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aB={kernelName:vs,backendName:"webgpu",kernelFunc:une};function pne(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var iB={kernelName:Tn,backendName:"webgpu",kernelFunc:pne};function cne(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var uB={kernelName:_n,backendName:"webgpu",kernelFunc:cne};var bx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + ${Q("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function Aoe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new cx(u,a),l=de({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=de({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YL={kernelName:En,backendName:"webgpu",kernelFunc:Aoe};function Kl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=qa({inputs:{input:o},backend:t}),s=Kl({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var QL={kernelName:Fs,backendName:"webgpu",kernelFunc:Kl};function ZL(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=qa({inputs:{input:o},backend:t}),s=ZL({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JL={kernelName:Is,backendName:"webgpu",kernelFunc:ZL};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return tx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=tx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=mI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eB={kernelName:vs,backendName:"webgpu",kernelFunc:Roe};var lx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(","),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",p=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` - ${se("index")} { + `}};function lne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new bx(u,a),l=me({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=me({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pB={kernelName:$n,backendName:"webgpu",kernelFunc:lne};function Xl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Xa({inputs:{input:o},backend:t}),s=Xl({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var cB={kernelName:Os,backendName:"webgpu",kernelFunc:Xl};function lB(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Xa({inputs:{input:o},backend:t}),s=lB({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mB={kernelName:ks,backendName:"webgpu",kernelFunc:lB};function mne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return ix({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=ix({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=wI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dB={kernelName:Ns,backendName:"webgpu",kernelFunc:mne};var Cx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(","),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",p=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + ${Q("index")} { if (index < uniforms.size) { let start = ${s}; let end = ${a}; @@ -6828,8 +7006,8 @@ return a / b;`,x7=` } } } - `}};var xI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return dr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new lx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tB={kernelName:$n,backendName:"webgpu",kernelFunc:xI};var Foe=ot({opType:ye.POW}),rB={kernelName:An,backendName:"webgpu",kernelFunc:Foe};function Doe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Hu(ye.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var oB={kernelName:Rn,backendName:"webgpu",kernelFunc:Doe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"prod",t)}var nB={kernelName:Fn,backendName:"webgpu",kernelFunc:Ooe};var Poe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sB={kernelName:ks,backendName:"webgpu",kernelFunc:Poe};var yI=ot({opType:ye.DIV}),aB={kernelName:Jo,backendName:"webgpu",kernelFunc:yI};var Moe=Se({opType:Q.RECIPROCAL}),iB={kernelName:Dn,backendName:"webgpu",kernelFunc:Moe};var Loe=Se({opType:Q.RELU}),uB={kernelName:On,backendName:"webgpu",kernelFunc:Loe};var Boe=Se({opType:Q.RELU6}),pB={kernelName:Ln,backendName:"webgpu",kernelFunc:Boe};var mx=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` - ${se("index")} { + `}};var TI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Dt({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return fr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new Cx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fB={kernelName:En,backendName:"webgpu",kernelFunc:TI};var dne=Ze({opType:ge.POW}),hB={kernelName:An,backendName:"webgpu",kernelFunc:dne};function fne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Yu(ge.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var gB={kernelName:Rn,backendName:"webgpu",kernelFunc:fne};function hne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,"prod",t)}var xB={kernelName:Dn,backendName:"webgpu",kernelFunc:hne};var gne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yB={kernelName:Ts,backendName:"webgpu",kernelFunc:gne};var _I=Ze({opType:ge.DIV}),bB={kernelName:Zo,backendName:"webgpu",kernelFunc:_I};var xne=xe({opType:X.RECIPROCAL}),CB={kernelName:Fn,backendName:"webgpu",kernelFunc:xne};var yne=xe({opType:X.RELU}),SB={kernelName:On,backendName:"webgpu",kernelFunc:yne};var bne=xe({opType:X.RELU6}),wB={kernelName:Ln,backendName:"webgpu",kernelFunc:bne};var Sx=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6871,8 +7049,8 @@ return a / b;`,x7=` setOutputAtIndex(index, newValue); } } - `}};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new mx(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var cB={kernelName:Mn,backendName:"webgpu",kernelFunc:Voe};var dx=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` - ${se("index")} { + `}};function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new Sx(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var IB={kernelName:Mn,backendName:"webgpu",kernelFunc:Cne};var wx=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; @@ -6902,7 +7080,7 @@ return a / b;`,x7=` setOutputAtIndex(index, newValue); } } - `}};function zoe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new dx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var lB={kernelName:Pn,backendName:"webgpu",kernelFunc:zoe};var fx=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new wx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var vB={kernelName:Pn,backendName:"webgpu",kernelFunc:Sne};var Ix=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -6924,7 +7102,7 @@ return a / b;`,x7=` return reverseCoords; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); @@ -6932,9 +7110,9 @@ return a / b;`,x7=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=de({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new fx(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=de({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var mB={kernelName:Bn,backendName:"webgpu",kernelFunc:Woe};var hx=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Dt({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=me({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new Ix(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=me({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var kB={kernelName:Bn,backendName:"webgpu",kernelFunc:wne};var vx=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * @@ -6953,7 +7131,7 @@ return a / b;`,x7=` setOutputAtIndex(index, outputValue); } } - `}};var dB={kernelName:es,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new hx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Uoe=Se({opType:Q.RSQRT,cpuKernelImpl:lM}),fB={kernelName:Vn,backendName:"webgpu",kernelFunc:Uoe};var Gi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ue(e),this.dispatch=re(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` + `}};var NB={kernelName:ts,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Ine=xe({opType:X.ROUND}),TB={kernelName:Vn,backendName:"webgpu",kernelFunc:Ine};var vne=xe({opType:X.RSQRT,cpuKernelImpl:gM}),_B={kernelName:zn,backendName:"webgpu",kernelFunc:vne};var Hi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ae(e),this.dispatch=Y(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -6968,25 +7146,9 @@ return a / b;`,x7=` let d1 = index - d0 * sliceSize; return vec2(d0, d1); } - `);let i=`getUpdates(${Array.from({length:this.updatesRank},(c,l)=>`coords[${l}]`).join(", ")})`,p=(c,l)=>{let m=`atomicAdd(${c}, bitcast(${l}))`;this.type==="float32"&&(m=` - { - var oldBits = 0; - var newBits = bitcast(${l}); - loop { - let info = atomicCompareExchangeWeak(${c}, oldBits, newBits); - if (info.exchanged) { - break; - } - oldBits = info.old_value; - let oldValue = bitcast(oldBits); - let newValue = oldValue + (${l}); - newBits = bitcast(newValue); - } - } - `);let d=`atomicStore(${c}, bitcast(${l}));`;return this.sumDupeIndices?m:d};return` + `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(", ")})`;return` ${s} - - ${se("index")} { + ${Q("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; @@ -6995,12 +7157,12 @@ return a / b;`,x7=` flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${wc(this.type,!1)}(${i}); + ${vc(this.type,!1)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${p("&result[flatIndex]","updateValue")}; + ${this.sumDupeIndices?Ic("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function Goe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=de({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=de({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=dr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new Gi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=de({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var hB={kernelName:zn,backendName:"webgpu",kernelFunc:Goe};var gx=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function kne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=me({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=fr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new Hi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=me({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var $B={kernelName:Wn,backendName:"webgpu",kernelFunc:kne};var kx=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -7015,15 +7177,15 @@ return a / b;`,x7=` return right; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new gx([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],"int32",p)}var gB={kernelName:ii,backendName:"webgpu",kernelFunc:Hoe};var xx=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},IB={kernelName:Es,backendName:"webgpu",kernelFunc:Qoe};var yx=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},VB={kernelName:As,backendName:"webgpu",kernelFunc:One};var Tx=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=yM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new yx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var vB={kernelName:to,backendName:"webgpu",kernelFunc:CI};function Joe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=mM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=de({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?de({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=de({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=CI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Gi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let $=de({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),$}var kB={kernelName:li,backendName:"webgpu",kernelFunc:Joe};function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ds({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NB={kernelName:$s,backendName:"webgpu",kernelFunc:ene};var tne=Se({opType:Q.SQRT}),TB={kernelName:Gn,backendName:"webgpu",kernelFunc:tne};var _B={kernelName:mi,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Ro(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var rne=ot({opType:ye.SQUARED_DIFFERENCE}),EB={kernelName:Kn,backendName:"webgpu",kernelFunc:rne};var bx=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` - ${se("index")} { + `}};function Pne(r,e=""){if(r>=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=IM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Tx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var zB={kernelName:to,backendName:"webgpu",kernelFunc:EI};function Mne(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=xM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=me({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?me({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Dt({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=me({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=EI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Hi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let E=me({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),E}var WB={kernelName:yi,backendName:"webgpu",kernelFunc:Mne};function Lne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=fs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UB={kernelName:Rs,backendName:"webgpu",kernelFunc:Lne};var Bne=xe({opType:X.SQRT}),GB={kernelName:Hn,backendName:"webgpu",kernelFunc:Bne};var HB={kernelName:bi,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Hr(t.shape,X.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Vne=Ze({opType:ge.SQUARED_DIFFERENCE}),KB={kernelName:jn,backendName:"webgpu",kernelFunc:Vne};function zne({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Hr(o.shape,X.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qB={kernelName:fo,backendName:"webgpu",kernelFunc:zne};var _x=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function one(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=de({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=ds({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=de({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),A=le(n.shape,n.dtype,$),R=hM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let $=new bx(d),A=[{type:"int32",data:b},{type:"int32",data:w}],R=t.runWebGPUProgram($,[n],n.dtype,A);k=de({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var $B={kernelName:jn,backendName:"webgpu",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=gM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var AB={kernelName:As,backendName:"webgpu",kernelFunc:nne};var sne=Se({opType:Q.TAN}),RB={kernelName:Yn,backendName:"webgpu",kernelFunc:sne};var ane=Se({opType:Q.TANH}),FB={kernelName:Qn,backendName:"webgpu",kernelFunc:ane};var Cx=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=me({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),E=fs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=me({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),A=le(n.shape,n.dtype,E),R=CM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let E=new _x(d),A=[{type:"int32",data:b},{type:"int32",data:w}],R=t.runWebGPUProgram(E,[n],n.dtype,A);k=me({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var jB={kernelName:Xn,backendName:"webgpu",kernelFunc:Wne};function Une(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var XB={kernelName:Ds,backendName:"webgpu",kernelFunc:Une};var Gne=xe({opType:X.TAN}),YB={kernelName:Qn,backendName:"webgpu",kernelFunc:Gne};var Hne=xe({opType:X.TANH}),QB={kernelName:Zn,backendName:"webgpu",kernelFunc:Hne};var $x=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -7116,8 +7278,8 @@ return a / b;`,x7=` } } } - `}},Sx=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` - ${se("index")} { + `}},Ex=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` + ${Q("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; @@ -7175,7 +7337,7 @@ return a / b;`,x7=` } } } - `}};function $c(r,e){e!==null&&r.disposeData(e.dataId)}function DB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let A=h(),R=new Cx($),P=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,"int32",P),$c(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new Sx([c,k/2]),R=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],D=f;f=t.runWebGPUProgram($,_,"int32",R),$c(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ds({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),$c(t,x);let b=gI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});$c(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=de({inputs:{x:f},attrs:{shape:C},backend:t}),$c(t,x);let w=b;return b=de({inputs:{x:b},attrs:{shape:C},backend:t}),$c(t,w),[b,f]}var OB={kernelName:Zn,backendName:"webgpu",kernelFunc:ine};var wx=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` + `}};function Rc(r,e){e!==null&&r.disposeData(e.dataId)}function ZB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,E)=>{let A=h(),R=new $x(E),P=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,"int32",P),Rc(t,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new Ex([c,k/2]),R=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],D=f;f=t.runWebGPUProgram(E,_,"int32",R),Rc(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=fs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Rc(t,x);let b=NI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Rc(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=me({inputs:{x:f},attrs:{shape:C},backend:t}),Rc(t,x);let w=b;return b=me({inputs:{x:b},attrs:{shape:C},backend:t}),Rc(t,w),[b,f]}var JB={kernelName:Jn,backendName:"webgpu",kernelFunc:Kne};var Ax=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -7239,7 +7401,7 @@ return a / b;`,x7=` return outputValue; } - ${se("index")} { + ${Q("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; @@ -7291,4 +7453,4 @@ return a / b;`,x7=` setOutputAtIndex(index, outputValue); } } - `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let w=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",w)}var PB={kernelName:Jn,backendName:"webgpu",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:"webgpu",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB="4.1.0",lne="4.1.0",mne="4.1.0",dne="4.1.0",fne="4.1.0",hne="0.0.1-alpha.16",gne={tfjs:LB,"tfjs-core":LB,"tfjs-converter":lne,"tfjs-backend-cpu":mne,"tfjs-backend-webgl":dne,"tfjs-backend-wasm":fne,"tfjs-backend-webgpu":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,fv as acos,hv as acosh,xe as add,gv as addN,xv as all,yv as any,bv as argMax,Cv as argMin,Sv as asin,wv as asinh,Iv as atan,vv as atan2,kv as atanh,td as avgPool,_v as avgPool3d,Oie as backend,S as backend_util,Ev as basicLSTMCell,wi as batchNorm,Av as batchNorm2d,Rv as batchNorm3d,Fv as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,Dv as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Q0 as browser,le as buffer,Ke as cast,Ov as ceil,Pv as clipByValue,Br as clone,Tr as complex,gt as concat,Mv as concat1d,Lv as concat2d,Bv as concat3d,Vv as concat4d,zv as conv1d,vi as conv2d,Wv as conv2dTranspose,Uv as conv3d,Hv as conv3dTranspose,Dne as copyRegisteredKernels,qv as cos,Kv as cosh,il as cosineWindow,jv as cumprod,Xv as cumsum,Cr as customGrad,Yv as denseBincount,eC as deprecationWarn,Qv as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Zv as diag,Jv as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,j0 as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,pv as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,h0 as tensor_util,dv as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike}; + `}};function qne(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ax(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let w=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",w)}var eV={kernelName:es,backendName:"webgpu",kernelFunc:qne};function jne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var tV={kernelName:Fs,backendName:"webgpu",kernelFunc:jne};var Xne=[G3,NM,TM,_M,$M,EM,RM,DM,FM,OM,PM,MM,LM,BM,VM,UM,GM,HM,qM,jM,ZM,JM,eL,K3,rL,oL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,CL,SL,wL,IL,vL,kL,NL,TL,W3,_L,AL,$L,EL,RL,DL,FL,OL,PL,ML,LL,H3,BL,tL,VL,zL,WL,UL,GL,HL,KL,jL,qL,XL,YL,QL,JL,zM,eB,tB,WM,rB,oB,nB,sB,yL,aB,iB,uB,XM,pB,mB,dB,fB,hB,gB,xB,yB,YM,bB,CB,SB,wB,U3,IB,vB,kB,NB,TB,_B,$B,EB,AB,RB,DB,FB,OB,PB,KM,qB,jB,XB,LB,BB,VB,WB,UB,GB,HB,KB,MB,bL,YB,QB,zB,JB,eV,AM,tV,cB];for(let r of Xne)ka(r);var rV="4.2.0",Yne="4.2.0",Qne="4.2.0",Zne="4.2.0",Jne="4.2.0",ese="0.0.1-alpha.17",tse={tfjs:rV,"tfjs-core":rV,"tfjs-converter":Yne,"tfjs-backend-cpu":Qne,"tfjs-backend-webgl":Zne,"tfjs-backend-wasm":Jne,"tfjs-backend-webgpu":ese};export{ys as Abs,aa as Acos,ia as Acosh,yu as AdadeltaOptimizer,bu as AdagradOptimizer,Cu as AdamOptimizer,Su as AdamaxOptimizer,eo as Add,Po as AddN,Mo as All,Lo as Any,Bo as ArgMax,ei as ArgMin,ua as Asin,pa as Asinh,ca as Atan,ma as Atan2,la as Atanh,Vo as AvgPool,dp as AvgPool3D,vm as AvgPool3DGrad,mp as AvgPoolGrad,Ll as BackendWasm,zo as BatchMatMul,bs as BatchToSpaceND,ti as Bincount,fp as BroadcastArgs,ise as BroadcastTo,co as Cast,Wo as Ceil,lo as ClipByValue,ri as Complex,hp as ComplexAbs,Cs as Concat,Uo as Conv2D,oi as Conv2DBackpropFilter,Go as Conv2DBackpropInput,gp as Conv3D,km as Conv3DBackpropFilterV2,xp as Conv3DBackpropInputV2,Ho as Cos,Ko as Cosh,Xo as CropAndResize,qo as Cumprod,jo as Cumsum,Fo as DataStorage,ni as DenseBincount,Yo as DepthToSpace,Qo as DepthwiseConv2dNative,yp as DepthwiseConv2dNativeBackpropFilter,bp as DepthwiseConv2dNativeBackpropInput,si as Diag,ai as Dilation2D,$b as Dilation2DBackpropFilter,_b as Dilation2DBackpropInput,kb as ENV,ii as Einsum,Jo as Elu,Nm as EluGrad,Uc as Environment,en as Equal,da as Erf,tn as Exp,Ss as ExpandDims,fa as Expm1,ui as FFT,ws as Fill,rn as FlipLeftRight,on as Floor,nn as FloorDiv,Zi as FromPixels,sn as FusedBatchNorm,go as FusedConv2D,xo as FusedDepthwiseConv2D,Lu as GPGPUContext,an as GatherNd,Is as GatherV2,fl as GraphModel,un as Greater,pn as GreaterEqual,pi as IFFT,mo as Identity,ci as Imag,ha as IsFinite,ga as IsInf,cn as IsNan,Zr as KernelBackend,mi as LRN,Tm as LRNGrad,ln as LeakyRelu,mn as Less,dn as LessEqual,li as LinSpace,fn as Log,xa as Log1p,use as LogSoftmax,hn as LogicalAnd,gn as LogicalNot,xn as LogicalOr,XI as LogicalXor,pse as LowerBound,Pi as MathBackendCPU,Vi as MathBackendWebGL,yn as Max,Cn as MaxPool,Cp as MaxPool3D,$m as MaxPool3DGrad,_m as MaxPoolGrad,Sp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,wu as MomentumOptimizer,wp as Multinomial,kn as Multiply,vs as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,jb as OP_SCOPE_SUFFIX,$n as OneHot,ks as OnesLike,wr as Optimizer,cl as OptimizerConstructors,Ns as Pack,En as PadV2,cse as Pool,An as Pow,Rn as Prelu,Dn as Prod,Iu as RMSPropOptimizer,Ip as RaggedGather,vp as RaggedRange,kp as RaggedTensorToTensor,Ts as Range,Lb as Rank,di as Real,Zo as RealDiv,Fn as Reciprocal,$t as Reduction,On as Relu,Ln as Relu6,_s as Reshape,Mn as ResizeBilinear,Am as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,ts as RotateWithOffset,Vn as Round,zn as Rsqrt,Pa as SGDOptimizer,Wn as ScatterNd,fi as SearchSorted,$s as Select,Ca as Selu,Gn as Sigmoid,wa as Sign,Un as Sin,Sa as Sinh,Es as Slice,qn as Softmax,Ia as Softplus,As as SpaceToBatchND,hi as SparseFillEmptyRows,va as SparseReshape,gi as SparseSegmentMean,xi as SparseSegmentSum,yi as SparseToDense,Rs as SplitV,Hn as Sqrt,bi as Square,jn as SquaredDifference,fo as Step,Xn as StridedSlice,Ds as StringNGrams,Ci as StringSplit,Si as StringToHashBucketFast,Yn as Sub,Kn as Sum,Qn as Tan,Zn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Jn as TopK,es as Transform,ro as Transpose,Np as Unique,Fs as Unpack,Tp as UnsortedSegmentSum,lse as UpperBound,Na as Variable,Gi as WebGPUBackend,Os as ZerosLike,ho as _FusedMatMul,Yt as abs,G0 as acos,H0 as acosh,be as add,K0 as addN,q0 as all,j0 as any,X0 as argMax,Y0 as argMin,Q0 as asin,Z0 as asinh,J0 as atan,ev as atan2,tv as atanh,qm as avgPool,nv as avgPool3d,Pie as backend,S as backend_util,sv as basicLSTMCell,_i as batchNorm,iv as batchNorm2d,uv as batchNorm3d,pv as batchNorm4d,jm as batchToSpaceND,Xm as bincount,VG as booleanMaskAsync,cv as broadcastArgs,$i as broadcastTo,yr as broadcast_util,gN as browser,le as buffer,je as cast,lv as ceil,mv as clipByValue,Lr as clone,Tr as complex,xt as concat,dv as concat1d,fv as concat2d,hv as concat3d,gv as concat4d,xv as conv1d,Ei as conv2d,yv as conv2dTranspose,bv as conv3d,Sv as conv3dTranspose,bse as copyRegisteredKernels,wv as cos,Iv as cosh,al as cosineWindow,vv as cumprod,kv as cumsum,br as customGrad,Nv as denseBincount,nC as deprecationWarn,Tv as depthToSpace,Bp as depthwiseConv2d,Mq as deregisterOp,ki as device_util,_v as diag,$v as dilation2d,kie as disableDeprecationWarnings,Ot as dispose,Nie as disposeVariables,He as div,Av as divNoNan,Rv as dot,ZG as dropout,Dv as einsum,Jm as elu,vie as enableDebugMode,Iie as enableProdMode,CC as enclosingPowerOfTwo,sr as engine,O as env,Zm as equal,Fv as erf,Mv as euclideanNorm,So as exp,Ra as expandDims,Lv as expm1,ed as eye,zp as fft,Us as fill,Fie as findBackend,Oie as findBackendFactory,td as floor,Km as floorDiv,GE as forceHalfFloat,SC as fused,rd as gather,YG as gatherND,Wd as gather_util,Rie as getBackend,Eb as getGradient,Kc as getKernel,Rm as getKernelsForBackend,Kee as getThreadsCount,$w as gpgpu_util,HU as grad,KU as grads,uu as greater,od as greaterEqual,du as ifft,Ri as imag,eK as image,eH as inTopKAsync,La as io,Td as irfft,Bv as isFinite,Vv as isInf,zv as isNaN,_r as keep,Vt as kernel_impls,nd as leakyRelu,Wv as less,Vp as lessEqual,tK as linalg,Uv as linspace,A6 as loadGraphModel,R6 as loadGraphModelSync,Gv as localResponseNormalization,Da as log,sd as log1p,Hv as logSigmoid,Kv as logSoftmax,ud as logSumExp,pu as logicalAnd,pd as logicalNot,cd as logicalOr,qv as logicalXor,rK as losses,jv as lowerBound,Xe as matMul,fN as math,Gs as max,md as maxPool,Xv as maxPool3d,Yv as maxPoolWithArgmax,dd as maximum,cu as mean,Tie as memory,Qv as meshgrid,rl as min,fd as minimum,Zv as mirrorPad,Jv as mod,ek as moments,UG as movingAverage,ne as mul,tk as multiRNNCell,rk as multinomial,Cr as neg,AC as nextFrame,iu as norm,hd as notEqual,nl as oneHot,Hs as ones,ok as onesLike,N as op,nk as outerProduct,Ks as pad,sk as pad1d,ak as pad2d,ik as pad3d,uk as pad4d,pk as pool,Aa as pow,xd as prelu,Hm as print,ck as prod,_ie as profile,lk as raggedGather,mk as raggedRange,dk as raggedTensorToTensor,fk as rand,Ok as randomGamma,Id as randomNormal,Pk as randomStandardNormal,vd as randomUniform,Di as range,Aie as ready,Fa as real,Mk as reciprocal,Ti as registerBackend,gse as registerGradient,ka as registerKernel,Pq as registerOp,Fi as relu,kd as relu6,Die as removeBackend,W as reshape,no as reverse,Lk as reverse1d,Bk as reverse2d,Vk as reverse3d,zk as reverse4d,Wp as rfft,Nd as round,Wk as rsqrt,Ce as scalar,HG as scatterND,sl as scatter_util,ol as searchSorted,Uk as selu,Gk as separableConv2d,oN as serialization,Eie as setBackend,Mie as setPlatform,Hee as setThreadsCount,Uee as setWasmPath,Gee as setWasmPaths,US as setWebGLContext,Hk as setdiff1dAsync,Zp as shared,Ws as sigmoid,Kk as sign,JH as signal,qk as sin,jk as sinh,Ke as slice,Xk as slice1d,Yk as slice2d,Qk as slice3d,Zk as slice4d,ut as slice_util,Jk as softmax,id as softplus,gd as spaceToBatchND,oK as sparse,jG as sparseToDense,ZH as spectral,Oa as split,$r as sqrt,Qt as square,_d as squaredDifference,Up as squeeze,Sr as stack,$d as step,e1 as stridedSlice,nK as string,ke as sub,et as sum,Ta as sumOutType,t1 as tan,tl as tanh,nr as tensor,dr as tensor1d,Oi as tensor2d,Ed as tensor3d,r1 as tensor4d,o1 as tensor5d,n1 as tensor6d,C0 as tensor_util,Fk as test_util,Ee as tidy,Ai as tile,$ie as time,s1 as topk,YMe as train,Gp as transpose,a1 as truncatedNormal,i1 as unique,yse as unregisterGradient,xse as unregisterKernel,u1 as unsortedSegmentSum,so as unstack,dt as upcastType,p1 as upperBound,y as util,qU as valueAndGrad,jU as valueAndGrads,c1 as variable,pC as variableGrads,tse as version,F6 as version_converter,RK as version_core,pj as version_cpu,qee as version_wasm,nY as version_webgl,CJe as webgl,nc as webgl_util,hI as webgpu_util,ss as where,Rd as whereAsync,Br as zeros,Ut as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index ecf8470d..ebe296c6 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,35 @@ author: ' */ -var e="4.1.0";var s="4.1.0";var t="4.1.0";var n="4.1.0";var r="4.1.0";var i="0.0.1-alpha.16";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/package.json +var version = "4.2.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/package.json +var version2 = "4.2.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-backend-cpu/package.json +var version3 = "4.2.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-backend-webgl/package.json +var version4 = "4.2.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-backend-wasm/package.json +var version5 = "4.2.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.17_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-backend-webgpu/package.json +var version6 = "0.0.1-alpha.17"; + +// tfjs/tf-version.ts +var version7 = { + tfjs: version, + "tfjs-core": version, + "tfjs-converter": version2, + "tfjs-backend-cpu": version3, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5, + "tfjs-backend-webgpu": version6 +}; +export { + version7 as version +}; diff --git a/test/build.log b/test/build.log index 39691b62..ad64ff65 100644 --- a/test/build.log +++ b/test/build.log @@ -1,50 +1,104 @@ -2023-01-03 14:23:58 DATA:  Build {"name":"@vladmandic/human","version":"3.0.1"} -2023-01-03 14:23:58 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.1"} -2023-01-03 14:23:58 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2023-01-03 14:23:58 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.18","typescript":"4.9.4","typedoc":"0.23.23","eslint":"8.31.0"} -2023-01-03 14:23:58 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2023-01-03 14:23:58 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2023-01-03 14:23:58 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} -2023-01-03 14:23:58 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} -2023-01-03 14:23:58 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":670848,"outputBytes":317619} -2023-01-03 14:23:58 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} -2023-01-03 14:23:59 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":670852,"outputBytes":317623} -2023-01-03 14:23:59 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} -2023-01-03 14:23:59 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671800,"outputBytes":317734} -2023-01-03 14:23:59 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} -2023-01-03 14:23:59 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670594,"outputBytes":316195} -2023-01-03 14:23:59 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1138192} -2023-01-03 14:23:59 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1808116,"outputBytes":1450757} -2023-01-03 14:23:59 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1808116,"outputBytes":1894175} -2023-01-03 14:24:04 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} -2023-01-03 14:24:06 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":77,"generated":true} -2023-01-03 14:24:06 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6082,"outputBytes":2872} -2023-01-03 14:24:06 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} -2023-01-03 14:24:15 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":169,"errors":0,"warnings":0} -2023-01-03 14:24:15 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2023-01-03 14:24:15 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2023-01-03 14:24:15 INFO:  Done... -2023-01-03 14:24:15 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2023-01-03 14:24:15 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2023-01-03 14:24:15 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2023-01-03 14:24:15 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2023-01-03 14:24:15 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2023-01-03 14:24:16 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":204} -2023-01-03 14:24:16 STATE: Filter: {"input":"types/human.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.esm.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.node.d.ts"} -2023-01-03 14:24:16 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2023-01-03 14:24:16 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2023-01-03 14:24:16 STATE: Models {"folder":"./models","models":12} -2023-01-03 14:24:16 STATE: Models {"folder":"../human-models/models","models":41} -2023-01-03 14:24:16 STATE: Models {"folder":"../blazepose/model/","models":4} -2023-01-03 14:24:16 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2023-01-03 14:24:16 STATE: Models {"folder":"../efficientpose/models","models":3} -2023-01-03 14:24:16 STATE: Models {"folder":"../insightface/models","models":5} -2023-01-03 14:24:16 STATE: Models {"folder":"../movenet/models","models":3} -2023-01-03 14:24:16 STATE: Models {"folder":"../nanodet/models","models":4} -2023-01-03 14:24:17 STATE: Models: {"count":55,"totalSize":372917743} -2023-01-03 14:24:17 INFO:  Human Build complete... {"logFile":"test/build.log"} +2023-01-06 12:59:00 DATA:  Build {"name":"@vladmandic/human","version":"3.0.1"} +2023-01-06 12:59:00 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.1"} +2023-01-06 12:59:00 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2023-01-06 12:59:00 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.18","typescript":"4.9.4","typedoc":"0.23.23","eslint":"8.31.0"} +2023-01-06 12:59:00 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2023-01-06 12:59:00 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":670848,"outputBytes":317619} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":670852,"outputBytes":317623} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671800,"outputBytes":317734} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670594,"outputBytes":316195} +2023-01-06 12:59:00 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1151353} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1821277,"outputBytes":1463493} +2023-01-06 12:59:00 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1821277,"outputBytes":1915983} +2023-01-06 12:59:04 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} +2023-01-06 12:59:05 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":77,"generated":true} +2023-01-06 12:59:05 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6082,"outputBytes":2872} +2023-01-06 12:59:05 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} +2023-01-06 12:59:15 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":169,"errors":0,"warnings":0} +2023-01-06 12:59:15 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2023-01-06 12:59:15 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2023-01-06 12:59:15 INFO:  Done... +2023-01-06 12:59:15 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2023-01-06 12:59:15 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2023-01-06 12:59:15 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2023-01-06 12:59:15 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2023-01-06 12:59:15 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2023-01-06 12:59:16 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":204} +2023-01-06 12:59:16 STATE: Filter: {"input":"types/human.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.esm.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.node.d.ts"} +2023-01-06 12:59:16 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2023-01-06 12:59:16 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2023-01-06 12:59:16 STATE: Models {"folder":"./models","models":12} +2023-01-06 12:59:16 STATE: Models {"folder":"../human-models/models","models":41} +2023-01-06 12:59:16 STATE: Models {"folder":"../blazepose/model/","models":4} +2023-01-06 12:59:16 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2023-01-06 12:59:16 STATE: Models {"folder":"../efficientpose/models","models":3} +2023-01-06 12:59:16 STATE: Models {"folder":"../insightface/models","models":5} +2023-01-06 12:59:16 STATE: Models {"folder":"../movenet/models","models":3} +2023-01-06 12:59:16 STATE: Models {"folder":"../nanodet/models","models":4} +2023-01-06 12:59:16 STATE: Models: {"count":55,"totalSize":372917743} +2023-01-06 12:59:16 INFO:  Human Build complete... {"logFile":"test/build.log"} +2023-01-06 13:08:59 INFO:  @vladmandic/human version 3.0.2 +2023-01-06 13:08:59 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 +2023-01-06 13:08:59 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.2"} +2023-01-06 13:08:59 INFO:  Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2023-01-06 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b/test/test.log index ceed2afe..168fe0b6 100644 --- a/test/test.log +++ b/test/test.log @@ -1,1001 +1,1000 @@ -2022-12-29 19:35:28 INFO:  @vladmandic/human version 3.0.1 -2022-12-29 19:35:28 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 -2022-12-29 19:35:28 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]}] -2022-12-29 19:35:28 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start -2022-12-29 19:35:29 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start -2022-12-29 19:35:30 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start -2022-12-29 19:35:30 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start -2022-12-29 19:35:31 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start -2022-12-29 19:35:32 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start -2022-12-29 19:35:34 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start -2022-12-29 19:35:47 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start -2022-12-29 19:35:48 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start -2022-12-29 19:36:06 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] -2022-12-29 19:36:06 INFO:  -2022-12-29 19:36:06 INFO:  test-node-load.js start -2022-12-29 19:36:06 INFO:  test-node-load.js load start {"human":"3.0.1","tf":"4.1.0","progress":0} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":25,"progress":0.12865823126207815} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":35,"progress":0.24651061157597698} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":68,"progress":0.4726970548784964} -2022-12-29 19:36:06 DATA:  test-node-load.js load interval {"elapsed":79,"progress":0.68355493315821} -2022-12-29 19:36:06 STATE: test-node-load.js passed {"progress":1} -2022-12-29 19:36:06 INFO:  test-node-load.js load final {"progress":1} -2022-12-29 19:36:07 DATA:  test-node-load.js load interval {"elapsed":484,"progress":1} -2022-12-29 19:36:07 INFO:  -2022-12-29 19:36:07 INFO:  test-node-gear.js start -2022-12-29 19:36:07 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] -2022-12-29 19:36:08 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg -2022-12-29 19:36:08 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} -2022-12-29 19:36:08 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg -2022-12-29 19:36:08 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} -2022-12-29 19:36:08 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2022-12-29 19:36:08 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} -2022-12-29 19:36:08 INFO:  -2022-12-29 19:36:08 INFO:  test-backend-node.js start -2022-12-29 19:36:08 INFO:  test-backend-node.js test: configuration validation -2022-12-29 19:36:08 STATE: test-backend-node.js passed: configuration default validation [] -2022-12-29 19:36:08 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-12-29 19:36:08 INFO:  test-backend-node.js test: model load -2022-12-29 19:36:08 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2022-12-29 19:36:08 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-12-29 19:36:08 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-12-29 19:36:08 INFO:  test-backend-node.js test: warmup -2022-12-29 19:36:08 STATE: test-backend-node.js passed: create human -2022-12-29 19:36:08 INFO:  test-backend-node.js human version: 3.0.1 -2022-12-29 19:36:08 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 -2022-12-29 19:36:08 INFO:  test-backend-node.js tfjs version: 4.1.0 -2022-12-29 19:36:08 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} -2022-12-29 19:36:08 STATE: test-backend-node.js passed: set backend: tensorflow -2022-12-29 19:36:08 STATE: test-backend-node.js tensors 1785 -2022-12-29 19:36:08 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 -2022-12-29 19:36:08 STATE: test-backend-node.js passed: load models 11 -2022-12-29 19:36:08 STATE: test-backend-node.js passed: warmup: none default -2022-12-29 19:36:08 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-12-29 19:36:08 DATA:  test-backend-node.js result: performance: load: null total: null -2022-12-29 19:36:08 STATE: test-backend-node.js passed: warmup none result match -2022-12-29 19:36:08 STATE: test-backend-node.js event: image -2022-12-29 19:36:09 STATE: test-backend-node.js event: detect -2022-12-29 19:36:09 STATE: test-backend-node.js event: warmup -2022-12-29 19:36:09 STATE: test-backend-node.js passed: warmup: face default -2022-12-29 19:36:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2022-12-29 19:36:09 DATA:  test-backend-node.js result: performance: load: null total: 436 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: warmup face result match -2022-12-29 19:36:09 STATE: test-backend-node.js event: image -2022-12-29 19:36:09 STATE: test-backend-node.js event: detect -2022-12-29 19:36:09 STATE: test-backend-node.js event: warmup -2022-12-29 19:36:09 STATE: test-backend-node.js passed: warmup: body default -2022-12-29 19:36:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:09 DATA:  test-backend-node.js result: performance: load: null total: 332 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: warmup body result match -2022-12-29 19:36:09 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-12-29 19:36:09 INFO:  test-backend-node.js test: details verification -2022-12-29 19:36:09 STATE: test-backend-node.js start default -2022-12-29 19:36:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:09 STATE: test-backend-node.js event: image -2022-12-29 19:36:09 STATE: test-backend-node.js event: detect -2022-12-29 19:36:09 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default -2022-12-29 19:36:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:09 DATA:  test-backend-node.js result: performance: load: null total: 317 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face length 1 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face score 1 0.93 1 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face arrays 4 478 1024 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details face liveness 0.83 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details body length 1 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details body 0.92 17 6 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details hand length 1 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details hand 0.51 0.73 point -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details hand arrays 21 5 7 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details gesture length 7 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details object length 1 -2022-12-29 19:36:09 STATE: test-backend-node.js passed: details object 0.72 person -2022-12-29 19:36:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2022-12-29 19:36:10 STATE: test-backend-node.js event: image -2022-12-29 19:36:10 STATE: test-backend-node.js event: detect -2022-12-29 19:36:10 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-12-29 19:36:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2022-12-29 19:36:10 STATE: test-backend-node.js event: image -2022-12-29 19:36:10 STATE: test-backend-node.js event: detect -2022-12-29 19:36:10 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-12-29 19:36:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:11 STATE: test-backend-node.js event: image -2022-12-29 19:36:11 STATE: test-backend-node.js event: detect -2022-12-29 19:36:11 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-12-29 19:36:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:11 STATE: test-backend-node.js event: image -2022-12-29 19:36:11 STATE: test-backend-node.js event: detect -2022-12-29 19:36:11 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-12-29 19:36:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-12-29 19:36:12 STATE: test-backend-node.js event: image -2022-12-29 19:36:12 STATE: test-backend-node.js event: detect -2022-12-29 19:36:12 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-12-29 19:36:12 INFO:  test-backend-node.js test default -2022-12-29 19:36:12 STATE: test-backend-node.js start async -2022-12-29 19:36:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:12 STATE: test-backend-node.js event: image -2022-12-29 19:36:12 STATE: test-backend-node.js event: detect -2022-12-29 19:36:12 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async -2022-12-29 19:36:12 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:12 DATA:  test-backend-node.js result: performance: load: null total: 310 -2022-12-29 19:36:12 STATE: test-backend-node.js passed: default result face match 1 female 0.97 -2022-12-29 19:36:12 INFO:  test-backend-node.js test sync -2022-12-29 19:36:12 STATE: test-backend-node.js start sync -2022-12-29 19:36:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:13 STATE: test-backend-node.js event: image -2022-12-29 19:36:13 STATE: test-backend-node.js event: detect -2022-12-29 19:36:13 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync -2022-12-29 19:36:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:13 DATA:  test-backend-node.js result: performance: load: null total: 306 -2022-12-29 19:36:13 STATE: test-backend-node.js passed: default sync 1 female 0.97 -2022-12-29 19:36:13 INFO:  test-backend-node.js test: image process -2022-12-29 19:36:13 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:13 STATE: test-backend-node.js passed: image input null [1,256,256,3] -2022-12-29 19:36:13 INFO:  test-backend-node.js test: image null -2022-12-29 19:36:13 STATE: test-backend-node.js passed: invalid input could not convert input to tensor -2022-12-29 19:36:13 INFO:  test-backend-node.js test face similarity -2022-12-29 19:36:13 STATE: test-backend-node.js start face similarity -2022-12-29 19:36:13 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:13 STATE: test-backend-node.js event: image -2022-12-29 19:36:13 STATE: test-backend-node.js event: detect -2022-12-29 19:36:13 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity -2022-12-29 19:36:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-12-29 19:36:13 DATA:  test-backend-node.js result: performance: load: null total: 322 -2022-12-29 19:36:13 STATE: test-backend-node.js start face similarity -2022-12-29 19:36:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:14 STATE: test-backend-node.js event: image -2022-12-29 19:36:14 STATE: test-backend-node.js event: detect -2022-12-29 19:36:14 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity -2022-12-29 19:36:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:14 DATA:  test-backend-node.js result: performance: load: null total: 320 -2022-12-29 19:36:14 STATE: test-backend-node.js start face similarity -2022-12-29 19:36:14 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:14 STATE: test-backend-node.js event: image -2022-12-29 19:36:14 STATE: test-backend-node.js event: detect -2022-12-29 19:36:14 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-12-29 19:36:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-12-29 19:36:14 DATA:  test-backend-node.js result: performance: load: null total: 277 -2022-12-29 19:36:14 STATE: test-backend-node.js passed: face descriptor -2022-12-29 19:36:14 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} -2022-12-29 19:36:14 INFO:  test-backend-node.js test object -2022-12-29 19:36:14 STATE: test-backend-node.js start object -2022-12-29 19:36:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:14 STATE: test-backend-node.js event: image -2022-12-29 19:36:15 STATE: test-backend-node.js event: detect -2022-12-29 19:36:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:15 DATA:  test-backend-node.js result: performance: load: null total: 305 -2022-12-29 19:36:15 STATE: test-backend-node.js passed: centernet -2022-12-29 19:36:15 STATE: test-backend-node.js start object -2022-12-29 19:36:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:16 STATE: test-backend-node.js event: image -2022-12-29 19:36:16 STATE: test-backend-node.js event: detect -2022-12-29 19:36:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:16 DATA:  test-backend-node.js result: performance: load: null total: 333 -2022-12-29 19:36:16 STATE: test-backend-node.js passed: nanodet -2022-12-29 19:36:16 INFO:  test-backend-node.js test sensitive -2022-12-29 19:36:16 STATE: test-backend-node.js start sensitive -2022-12-29 19:36:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:16 STATE: test-backend-node.js event: image -2022-12-29 19:36:17 STATE: test-backend-node.js event: detect -2022-12-29 19:36:17 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive -2022-12-29 19:36:17 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:17 DATA:  test-backend-node.js result: performance: load: null total: 270 -2022-12-29 19:36:17 STATE: test-backend-node.js passed: sensitive result match -2022-12-29 19:36:17 STATE: test-backend-node.js passed: sensitive face result match -2022-12-29 19:36:17 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2022-12-29 19:36:17 STATE: test-backend-node.js passed: sensitive body result match -2022-12-29 19:36:17 STATE: test-backend-node.js passed: sensitive hand result match -2022-12-29 19:36:17 INFO:  test-backend-node.js test body -2022-12-29 19:36:17 STATE: test-backend-node.js start blazepose -2022-12-29 19:36:19 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:19 STATE: test-backend-node.js event: image -2022-12-29 19:36:19 STATE: test-backend-node.js event: detect -2022-12-29 19:36:19 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose -2022-12-29 19:36:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-12-29 19:36:19 DATA:  test-backend-node.js result: performance: load: null total: 349 -2022-12-29 19:36:19 STATE: test-backend-node.js passed: blazepose -2022-12-29 19:36:19 STATE: test-backend-node.js start efficientpose -2022-12-29 19:36:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:20 STATE: test-backend-node.js event: image -2022-12-29 19:36:20 STATE: test-backend-node.js event: detect -2022-12-29 19:36:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-12-29 19:36:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-12-29 19:36:20 DATA:  test-backend-node.js result: performance: load: null total: 320 -2022-12-29 19:36:20 STATE: test-backend-node.js passed: efficientpose -2022-12-29 19:36:20 STATE: test-backend-node.js start posenet -2022-12-29 19:36:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:21 STATE: test-backend-node.js event: image -2022-12-29 19:36:21 STATE: test-backend-node.js event: detect -2022-12-29 19:36:21 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet -2022-12-29 19:36:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-12-29 19:36:21 DATA:  test-backend-node.js result: performance: load: null total: 271 -2022-12-29 19:36:21 STATE: test-backend-node.js passed: posenet -2022-12-29 19:36:21 STATE: test-backend-node.js start movenet -2022-12-29 19:36:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:21 STATE: test-backend-node.js event: image -2022-12-29 19:36:22 STATE: test-backend-node.js event: detect -2022-12-29 19:36:22 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet -2022-12-29 19:36:22 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:22 DATA:  test-backend-node.js result: performance: load: null total: 271 -2022-12-29 19:36:22 STATE: test-backend-node.js passed: movenet -2022-12-29 19:36:22 INFO:  test-backend-node.js test face matching -2022-12-29 19:36:22 STATE: test-backend-node.js passed: face database 40 -2022-12-29 19:36:22 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} -2022-12-29 19:36:22 INFO:  test-backend-node.js test face similarity alternative -2022-12-29 19:36:22 STATE: test-backend-node.js start face embeddings -2022-12-29 19:36:22 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:22 STATE: test-backend-node.js event: image -2022-12-29 19:36:23 STATE: test-backend-node.js event: detect -2022-12-29 19:36:23 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:36:23 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:23 DATA:  test-backend-node.js result: performance: load: null total: 292 -2022-12-29 19:36:23 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} -2022-12-29 19:36:23 STATE: test-backend-node.js start face embeddings -2022-12-29 19:36:24 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:24 STATE: test-backend-node.js event: image -2022-12-29 19:36:24 STATE: test-backend-node.js event: detect -2022-12-29 19:36:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:36:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:24 DATA:  test-backend-node.js result: performance: load: null total: 300 -2022-12-29 19:36:24 STATE: test-backend-node.js passed: insightface {"embedding":512} -2022-12-29 19:36:24 INFO:  test-backend-node.js test face attention -2022-12-29 19:36:24 STATE: test-backend-node.js start face attention -2022-12-29 19:36:24 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:24 STATE: test-backend-node.js event: image -2022-12-29 19:36:24 STATE: test-backend-node.js event: detect -2022-12-29 19:36:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention -2022-12-29 19:36:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:24 DATA:  test-backend-node.js result: performance: load: null total: 288 -2022-12-29 19:36:24 STATE: test-backend-node.js passed: face attention -2022-12-29 19:36:24 INFO:  test-backend-node.js test detectors -2022-12-29 19:36:24 STATE: test-backend-node.js start detectors -2022-12-29 19:36:25 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:25 STATE: test-backend-node.js event: image -2022-12-29 19:36:25 STATE: test-backend-node.js event: detect -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors -2022-12-29 19:36:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:25 DATA:  test-backend-node.js result: performance: load: null total: 142 -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detector result face match -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detector result hand match -2022-12-29 19:36:25 INFO:  test-backend-node.js test: multi-instance -2022-12-29 19:36:25 STATE: test-backend-node.js start multi instance -2022-12-29 19:36:25 STATE: test-backend-node.js event: image -2022-12-29 19:36:25 STATE: test-backend-node.js event: detect -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detect: random multi instance -2022-12-29 19:36:25 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-12-29 19:36:25 DATA:  test-backend-node.js result: performance: load: null total: 146 -2022-12-29 19:36:25 INFO:  test-backend-node.js test: first instance -2022-12-29 19:36:25 STATE: test-backend-node.js start multi instance -2022-12-29 19:36:25 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:36:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:25 DATA:  test-backend-node.js result: performance: load: null total: 133 -2022-12-29 19:36:25 INFO:  test-backend-node.js test: second instance -2022-12-29 19:36:25 STATE: test-backend-node.js start multi instance -2022-12-29 19:36:25 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:25 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:36:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:25 DATA:  test-backend-node.js result: performance: load: null total: 134 -2022-12-29 19:36:25 INFO:  test-backend-node.js test: concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js start concurrent -2022-12-29 19:36:25 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:26 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-12-29 19:36:26 STATE: test-backend-node.js event: image -2022-12-29 19:36:26 STATE: test-backend-node.js event: image -2022-12-29 19:36:26 STATE: test-backend-node.js event: image -2022-12-29 19:36:27 STATE: test-backend-node.js event: detect -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1148 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1148 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1148 -2022-12-29 19:36:27 STATE: test-backend-node.js event: detect -2022-12-29 19:36:27 STATE: test-backend-node.js event: detect -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:27 DATA:  test-backend-node.js result: performance: load: null total: 1149 -2022-12-29 19:36:27 INFO:  test-backend-node.js test: monkey-patch -2022-12-29 19:36:27 STATE: test-backend-node.js event: image -2022-12-29 19:36:28 STATE: test-backend-node.js event: detect -2022-12-29 19:36:28 STATE: test-backend-node.js passed: monkey patch -2022-12-29 19:36:28 STATE: test-backend-node.js passed: segmentation [262144] -2022-12-29 19:36:28 STATE: test-backend-node.js passeed: equal usage -2022-12-29 19:36:28 INFO:  test-backend-node.js test: input compare -2022-12-29 19:36:28 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:28 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-12-29 19:36:28 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 -2022-12-29 19:36:28 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} -2022-12-29 19:36:28 INFO:  test-backend-node.js tensors 4453 -2022-12-29 19:36:28 INFO:  test-backend-node.js test complete: 20405 ms -2022-12-29 19:36:28 INFO:  -2022-12-29 19:36:28 INFO:  test-backend-node-gpu.js start -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js test: configuration validation -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: configuration default validation [] -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js test: model load -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js test: warmup -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: create human -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js human version: 3.0.1 -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js tfjs version: 4.1.0 -2022-12-29 19:36:29 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: set backend: tensorflow -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js tensors 1785 -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: load models 11 -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: warmup: none default -2022-12-29 19:36:29 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-12-29 19:36:29 DATA:  test-backend-node-gpu.js result: performance: load: null total: null -2022-12-29 19:36:29 STATE: test-backend-node-gpu.js passed: warmup none result match -2022-12-29 19:36:30 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: warmup -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js passed: warmup: face default -2022-12-29 19:36:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2022-12-29 19:36:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1518 -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js passed: warmup face result match -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: warmup -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js passed: warmup: body default -2022-12-29 19:36:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 137 -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js passed: warmup body result match -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-12-29 19:36:31 INFO:  test-backend-node-gpu.js test: details verification -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js start default -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:31 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default -2022-12-29 19:36:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 128 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face length 1 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details body length 1 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details hand length 1 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details gesture length 7 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details object length 1 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: details object 0.72 person -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:32 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-12-29 19:36:33 INFO:  test-backend-node-gpu.js test default -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js start async -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async -2022-12-29 19:36:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 115 -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 -2022-12-29 19:36:33 INFO:  test-backend-node-gpu.js test sync -2022-12-29 19:36:33 STATE: test-backend-node-gpu.js start sync -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 119 -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 -2022-12-29 19:36:34 INFO:  test-backend-node-gpu.js test: image process -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] -2022-12-29 19:36:34 INFO:  test-backend-node-gpu.js test: image null -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor -2022-12-29 19:36:34 INFO:  test-backend-node-gpu.js test face similarity -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js start face similarity -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 108 -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js start face similarity -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 116 -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js start face similarity -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-12-29 19:36:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 106 -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: face descriptor -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475006597943763,0.5570889682705169],"descriptors":[1024,1024,1024]} -2022-12-29 19:36:34 INFO:  test-backend-node-gpu.js test object -2022-12-29 19:36:34 STATE: test-backend-node-gpu.js start object -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 115 -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js passed: centernet -2022-12-29 19:36:35 STATE: test-backend-node-gpu.js start object -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 532 -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: nanodet -2022-12-29 19:36:36 INFO:  test-backend-node-gpu.js test sensitive -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js start sensitive -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive -2022-12-29 19:36:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 116 -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: sensitive result match -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: sensitive face result match -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: sensitive body result match -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js passed: sensitive hand result match -2022-12-29 19:36:36 INFO:  test-backend-node-gpu.js test body -2022-12-29 19:36:36 STATE: test-backend-node-gpu.js start blazepose -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose -2022-12-29 19:36:38 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-12-29 19:36:38 DATA:  test-backend-node-gpu.js result: performance: load: null total: 266 -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js passed: blazepose -2022-12-29 19:36:38 STATE: test-backend-node-gpu.js start efficientpose -2022-12-29 19:36:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:39 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-12-29 19:36:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-12-29 19:36:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1004 -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js passed: efficientpose -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js start posenet -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:40 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 155 -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: posenet -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js start movenet -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 143 -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: movenet -2022-12-29 19:36:41 INFO:  test-backend-node-gpu.js test face matching -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: face database 40 -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829337456107771}} {"second":{"index":4,"similarity":0.5002921100080195}} {"third":{"index":4,"similarity":0.5402938601022594}} -2022-12-29 19:36:41 INFO:  test-backend-node-gpu.js test face similarity alternative -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js start face embeddings -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 214 -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} -2022-12-29 19:36:41 STATE: test-backend-node-gpu.js start face embeddings -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:36:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 309 -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} -2022-12-29 19:36:42 INFO:  test-backend-node-gpu.js test face attention -2022-12-29 19:36:42 STATE: test-backend-node-gpu.js start face attention -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 221 -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: face attention -2022-12-29 19:36:43 INFO:  test-backend-node-gpu.js test detectors -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start detectors -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 42 -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detector result face match -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detector result hand match -2022-12-29 19:36:43 INFO:  test-backend-node-gpu.js test: multi-instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start multi instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detect: random multi instance -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 39 -2022-12-29 19:36:43 INFO:  test-backend-node-gpu.js test: first instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start multi instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 42 -2022-12-29 19:36:43 INFO:  test-backend-node-gpu.js test: second instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start multi instance -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 40 -2022-12-29 19:36:43 INFO:  test-backend-node-gpu.js test: concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js start concurrent -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:44 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 383 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 383 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 383 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 384 -2022-12-29 19:36:45 INFO:  test-backend-node-gpu.js test: monkey-patch -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js event: image -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js event: detect -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: monkey patch -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: segmentation [262144] -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passeed: equal usage -2022-12-29 19:36:45 INFO:  test-backend-node-gpu.js test: input compare -2022-12-29 19:36:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-12-29 19:36:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-12-29 19:36:46 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 -2022-12-29 19:36:46 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} -2022-12-29 19:36:46 INFO:  test-backend-node-gpu.js tensors 4453 -2022-12-29 19:36:46 INFO:  test-backend-node-gpu.js test complete: 16438 ms -2022-12-29 19:36:46 INFO:  -2022-12-29 19:36:46 INFO:  test-backend-node-wasm.js start -2022-12-29 19:36:46 DATA:  test-backend-node-wasm.js stdout: 2022-12-29 19:36:46 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.1.0/dist/ -2022-12-29 19:36:47 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ -2022-12-29 19:36:47 INFO:  test-backend-node-wasm.js test: configuration validation -2022-12-29 19:36:47 STATE: test-backend-node-wasm.js passed: configuration default validation [] -2022-12-29 19:36:47 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-12-29 19:36:47 INFO:  test-backend-node-wasm.js test: model load -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js test: warmup -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: create human -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js human version: 3.0.1 -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js tfjs version: 4.1.0 -2022-12-29 19:36:48 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: set backend: wasm -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js tensors 1785 -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: load models 11 -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: warmup: none default -2022-12-29 19:36:48 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-12-29 19:36:48 DATA:  test-backend-node-wasm.js result: performance: load: null total: null -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js passed: warmup none result match -2022-12-29 19:36:48 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js event: warmup -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js passed: warmup: face default -2022-12-29 19:36:49 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-12-29 19:36:49 DATA:  test-backend-node-wasm.js result: performance: load: null total: 460 -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js passed: warmup face result match -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js event: warmup -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js passed: warmup: body default -2022-12-29 19:36:49 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:49 DATA:  test-backend-node-wasm.js result: performance: load: null total: 324 -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js passed: warmup body result match -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-12-29 19:36:49 INFO:  test-backend-node-wasm.js test: details verification -2022-12-29 19:36:49 STATE: test-backend-node-wasm.js start default -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default -2022-12-29 19:36:50 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:50 DATA:  test-backend-node-wasm.js result: performance: load: null total: 321 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face length 1 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details body length 1 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details hand length 1 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details gesture length 7 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details object length 1 -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: details object 0.72 person -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:50 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:51 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-12-29 19:36:52 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-12-29 19:36:53 INFO:  test-backend-node-wasm.js test default -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js start async -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async -2022-12-29 19:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 300 -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 -2022-12-29 19:36:53 INFO:  test-backend-node-wasm.js test sync -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js start sync -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:53 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: 305 -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 -2022-12-29 19:36:54 INFO:  test-backend-node-wasm.js test: image process -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] -2022-12-29 19:36:54 INFO:  test-backend-node-wasm.js test: image null -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor -2022-12-29 19:36:54 INFO:  test-backend-node-wasm.js test face similarity -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js start face similarity -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: 290 -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js start face similarity -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: 294 -2022-12-29 19:36:54 STATE: test-backend-node-wasm.js start face similarity -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-12-29 19:36:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-12-29 19:36:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 284 -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: face descriptor -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} -2022-12-29 19:36:55 INFO:  test-backend-node-wasm.js test object -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js start object -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-12-29 19:36:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 290 -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js passed: centernet -2022-12-29 19:36:55 STATE: test-backend-node-wasm.js start object -2022-12-29 19:36:56 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} -2022-12-29 19:36:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:56 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:56 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2022-12-29 19:36:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 187 -2022-12-29 19:36:56 ERROR: test-backend-node-wasm.js failed: nanodet [] -2022-12-29 19:36:56 INFO:  test-backend-node-wasm.js test sensitive -2022-12-29 19:36:56 STATE: test-backend-node-wasm.js start sensitive -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive -2022-12-29 19:36:57 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:36:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 203 -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: sensitive result match -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: sensitive face result match -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: sensitive body result match -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js passed: sensitive hand result match -2022-12-29 19:36:57 INFO:  test-backend-node-wasm.js test body -2022-12-29 19:36:57 STATE: test-backend-node-wasm.js start blazepose -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose -2022-12-29 19:36:59 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-12-29 19:36:59 DATA:  test-backend-node-wasm.js result: performance: load: null total: 391 -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js passed: blazepose -2022-12-29 19:36:59 STATE: test-backend-node-wasm.js start efficientpose -2022-12-29 19:37:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:00 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-12-29 19:37:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-12-29 19:37:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 636 -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js passed: efficientpose -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js start posenet -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet -2022-12-29 19:37:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-12-29 19:37:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 265 -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js passed: posenet -2022-12-29 19:37:01 STATE: test-backend-node-wasm.js start movenet -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet -2022-12-29 19:37:02 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:37:02 DATA:  test-backend-node-wasm.js result: performance: load: null total: 210 -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: movenet -2022-12-29 19:37:02 INFO:  test-backend-node-wasm.js test face matching -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: face database 40 -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} -2022-12-29 19:37:02 INFO:  test-backend-node-wasm.js test face similarity alternative -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js start face embeddings -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:02 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:37:03 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:03 DATA:  test-backend-node-wasm.js result: performance: load: null total: 225 -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js start face embeddings -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:03 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 264 -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} -2022-12-29 19:37:04 INFO:  test-backend-node-wasm.js test face attention -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js start face attention -2022-12-29 19:37:04 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 119 -2022-12-29 19:37:04 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} -2022-12-29 19:37:04 INFO:  test-backend-node-wasm.js test detectors -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js start detectors -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:37:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 104 -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: detector result face match -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js passed: detector result hand match -2022-12-29 19:37:04 INFO:  test-backend-node-wasm.js test: multi-instance -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js start multi instance -2022-12-29 19:37:04 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: detect: random multi instance -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 95 -2022-12-29 19:37:05 INFO:  test-backend-node-wasm.js test: first instance -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start multi instance -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 104 -2022-12-29 19:37:05 INFO:  test-backend-node-wasm.js test: second instance -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start multi instance -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:37:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 105 -2022-12-29 19:37:05 INFO:  test-backend-node-wasm.js test: concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js start concurrent -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:06 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 890 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 890 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 890 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-12-29 19:37:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 891 -2022-12-29 19:37:07 INFO:  test-backend-node-wasm.js test: monkey-patch -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js event: image -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js event: detect -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: monkey patch -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: segmentation [262144] -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passeed: equal usage -2022-12-29 19:37:07 INFO:  test-backend-node-wasm.js test: input compare -2022-12-29 19:37:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-12-29 19:37:08 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-12-29 19:37:08 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 -2022-12-29 19:37:08 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} -2022-12-29 19:37:08 INFO:  test-backend-node-wasm.js tensors 4455 -2022-12-29 19:37:08 INFO:  test-backend-node-wasm.js test complete: 21078 ms -2022-12-29 19:37:08 STATE: all tests complete -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"test-node-gear.js","passed":3,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} -2022-12-29 19:37:08 INFO:  status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} -2022-12-29 19:37:08 INFO:  failures {"count":2} -2022-12-29 19:37:08 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} -2022-12-29 19:37:08 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} +2023-01-06 13:01:28 INFO:  @vladmandic/human version 3.0.1 +2023-01-06 13:01:28 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 +2023-01-06 13:01:28 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]}] +2023-01-06 13:01:28 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start +2023-01-06 13:01:29 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start +2023-01-06 13:01:29 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start +2023-01-06 13:01:30 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start +2023-01-06 13:01:31 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start +2023-01-06 13:01:32 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start +2023-01-06 13:01:33 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start +2023-01-06 13:01:46 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start +2023-01-06 13:01:47 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start +2023-01-06 13:02:12 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] +2023-01-06 13:02:12 INFO:  +2023-01-06 13:02:12 INFO:  test-node-load.js start +2023-01-06 13:02:12 INFO:  test-node-load.js load start {"human":"3.0.1","tf":"4.2.0","progress":0} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":10,"progress":0} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":29,"progress":0.12865823126207815} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":63,"progress":0.4726970548784964} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":77,"progress":0.68355493315821} +2023-01-06 13:02:12 STATE: test-node-load.js passed {"progress":1} +2023-01-06 13:02:12 INFO:  test-node-load.js load final {"progress":1} +2023-01-06 13:02:12 DATA:  test-node-load.js load interval {"elapsed":467,"progress":1} +2023-01-06 13:02:12 INFO:  +2023-01-06 13:02:12 INFO:  test-node-gear.js start +2023-01-06 13:02:12 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] +2023-01-06 13:02:13 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg +2023-01-06 13:02:13 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} +2023-01-06 13:02:13 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg +2023-01-06 13:02:13 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} +2023-01-06 13:02:13 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2023-01-06 13:02:13 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} +2023-01-06 13:02:13 INFO:  +2023-01-06 13:02:13 INFO:  test-backend-node.js start +2023-01-06 13:02:14 INFO:  test-backend-node.js test: configuration validation +2023-01-06 13:02:14 STATE: test-backend-node.js passed: configuration default validation [] +2023-01-06 13:02:14 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-01-06 13:02:14 INFO:  test-backend-node.js test: model load +2023-01-06 13:02:14 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-01-06 13:02:14 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-01-06 13:02:14 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-01-06 13:02:14 INFO:  test-backend-node.js test: warmup +2023-01-06 13:02:14 STATE: test-backend-node.js passed: create human +2023-01-06 13:02:14 INFO:  test-backend-node.js human version: 3.0.1 +2023-01-06 13:02:14 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 +2023-01-06 13:02:14 INFO:  test-backend-node.js tfjs version: 4.2.0 +2023-01-06 13:02:14 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} +2023-01-06 13:02:14 STATE: test-backend-node.js passed: set backend: tensorflow +2023-01-06 13:02:14 STATE: test-backend-node.js tensors 1785 +2023-01-06 13:02:14 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 +2023-01-06 13:02:14 STATE: test-backend-node.js passed: load models 11 +2023-01-06 13:02:14 STATE: test-backend-node.js passed: warmup: none default +2023-01-06 13:02:14 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-01-06 13:02:14 DATA:  test-backend-node.js result: performance: load: null total: null +2023-01-06 13:02:14 STATE: test-backend-node.js passed: warmup none result match +2023-01-06 13:02:14 STATE: test-backend-node.js event: image +2023-01-06 13:02:14 STATE: test-backend-node.js event: detect +2023-01-06 13:02:14 STATE: test-backend-node.js event: warmup +2023-01-06 13:02:14 STATE: test-backend-node.js passed: warmup: face default +2023-01-06 13:02:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-01-06 13:02:14 DATA:  test-backend-node.js result: performance: load: null total: 441 +2023-01-06 13:02:14 STATE: test-backend-node.js passed: warmup face result match +2023-01-06 13:02:14 STATE: test-backend-node.js event: image +2023-01-06 13:02:15 STATE: test-backend-node.js event: detect +2023-01-06 13:02:15 STATE: test-backend-node.js event: warmup +2023-01-06 13:02:15 STATE: test-backend-node.js passed: warmup: body default +2023-01-06 13:02:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:15 DATA:  test-backend-node.js result: performance: load: null total: 349 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: warmup body result match +2023-01-06 13:02:15 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-01-06 13:02:15 INFO:  test-backend-node.js test: details verification +2023-01-06 13:02:15 STATE: test-backend-node.js start default +2023-01-06 13:02:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:15 STATE: test-backend-node.js event: image +2023-01-06 13:02:15 STATE: test-backend-node.js event: detect +2023-01-06 13:02:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default +2023-01-06 13:02:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:15 DATA:  test-backend-node.js result: performance: load: null total: 321 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face length 1 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face score 1 0.93 1 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face arrays 4 478 1024 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details face liveness 0.83 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details body length 1 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details body 0.92 17 6 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details hand length 1 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details hand 0.51 0.73 point +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details hand arrays 21 5 7 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details gesture length 7 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details object length 1 +2023-01-06 13:02:15 STATE: test-backend-node.js passed: details object 0.72 person +2023-01-06 13:02:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-01-06 13:02:15 STATE: test-backend-node.js event: image +2023-01-06 13:02:16 STATE: test-backend-node.js event: detect +2023-01-06 13:02:16 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-01-06 13:02:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-01-06 13:02:16 STATE: test-backend-node.js event: image +2023-01-06 13:02:16 STATE: test-backend-node.js event: detect +2023-01-06 13:02:16 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-01-06 13:02:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:16 STATE: test-backend-node.js event: image +2023-01-06 13:02:17 STATE: test-backend-node.js event: detect +2023-01-06 13:02:17 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-01-06 13:02:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:17 STATE: test-backend-node.js event: image +2023-01-06 13:02:17 STATE: test-backend-node.js event: detect +2023-01-06 13:02:17 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-01-06 13:02:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-01-06 13:02:17 STATE: test-backend-node.js event: image +2023-01-06 13:02:18 STATE: test-backend-node.js event: detect +2023-01-06 13:02:18 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-01-06 13:02:18 INFO:  test-backend-node.js test default +2023-01-06 13:02:18 STATE: test-backend-node.js start async +2023-01-06 13:02:18 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:18 STATE: test-backend-node.js event: image +2023-01-06 13:02:18 STATE: test-backend-node.js event: detect +2023-01-06 13:02:18 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async +2023-01-06 13:02:18 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:18 DATA:  test-backend-node.js result: performance: load: null total: 306 +2023-01-06 13:02:18 STATE: test-backend-node.js passed: default result face match 1 female 0.97 +2023-01-06 13:02:18 INFO:  test-backend-node.js test sync +2023-01-06 13:02:18 STATE: test-backend-node.js start sync +2023-01-06 13:02:18 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:18 STATE: test-backend-node.js event: image +2023-01-06 13:02:19 STATE: test-backend-node.js event: detect +2023-01-06 13:02:19 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync +2023-01-06 13:02:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:19 DATA:  test-backend-node.js result: performance: load: null total: 306 +2023-01-06 13:02:19 STATE: test-backend-node.js passed: default sync 1 female 0.97 +2023-01-06 13:02:19 INFO:  test-backend-node.js test: image process +2023-01-06 13:02:19 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:19 STATE: test-backend-node.js passed: image input null [1,256,256,3] +2023-01-06 13:02:19 INFO:  test-backend-node.js test: image null +2023-01-06 13:02:19 STATE: test-backend-node.js passed: invalid input could not convert input to tensor +2023-01-06 13:02:19 INFO:  test-backend-node.js test face similarity +2023-01-06 13:02:19 STATE: test-backend-node.js start face similarity +2023-01-06 13:02:19 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:19 STATE: test-backend-node.js event: image +2023-01-06 13:02:19 STATE: test-backend-node.js event: detect +2023-01-06 13:02:19 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity +2023-01-06 13:02:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-01-06 13:02:19 DATA:  test-backend-node.js result: performance: load: null total: 305 +2023-01-06 13:02:19 STATE: test-backend-node.js start face similarity +2023-01-06 13:02:19 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:19 STATE: test-backend-node.js event: image +2023-01-06 13:02:19 STATE: test-backend-node.js event: detect +2023-01-06 13:02:19 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity +2023-01-06 13:02:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:19 DATA:  test-backend-node.js result: performance: load: null total: 315 +2023-01-06 13:02:19 STATE: test-backend-node.js start face similarity +2023-01-06 13:02:19 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:19 STATE: test-backend-node.js event: image +2023-01-06 13:02:20 STATE: test-backend-node.js event: detect +2023-01-06 13:02:20 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-01-06 13:02:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-01-06 13:02:20 DATA:  test-backend-node.js result: performance: load: null total: 281 +2023-01-06 13:02:20 STATE: test-backend-node.js passed: face descriptor +2023-01-06 13:02:20 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2023-01-06 13:02:20 INFO:  test-backend-node.js test object +2023-01-06 13:02:20 STATE: test-backend-node.js start object +2023-01-06 13:02:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:20 STATE: test-backend-node.js event: image +2023-01-06 13:02:20 STATE: test-backend-node.js event: detect +2023-01-06 13:02:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:02:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:20 DATA:  test-backend-node.js result: performance: load: null total: 317 +2023-01-06 13:02:20 STATE: test-backend-node.js passed: centernet +2023-01-06 13:02:20 STATE: test-backend-node.js start object +2023-01-06 13:02:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:21 STATE: test-backend-node.js event: image +2023-01-06 13:02:22 STATE: test-backend-node.js event: detect +2023-01-06 13:02:22 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:02:22 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:22 DATA:  test-backend-node.js result: performance: load: null total: 310 +2023-01-06 13:02:22 STATE: test-backend-node.js passed: nanodet +2023-01-06 13:02:22 INFO:  test-backend-node.js test sensitive +2023-01-06 13:02:22 STATE: test-backend-node.js start sensitive +2023-01-06 13:02:22 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:22 STATE: test-backend-node.js event: image +2023-01-06 13:02:22 STATE: test-backend-node.js event: detect +2023-01-06 13:02:22 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive +2023-01-06 13:02:22 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:22 DATA:  test-backend-node.js result: performance: load: null total: 262 +2023-01-06 13:02:22 STATE: test-backend-node.js passed: sensitive result match +2023-01-06 13:02:22 STATE: test-backend-node.js passed: sensitive face result match +2023-01-06 13:02:22 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-01-06 13:02:22 STATE: test-backend-node.js passed: sensitive body result match +2023-01-06 13:02:22 STATE: test-backend-node.js passed: sensitive hand result match +2023-01-06 13:02:22 INFO:  test-backend-node.js test body +2023-01-06 13:02:22 STATE: test-backend-node.js start blazepose +2023-01-06 13:02:24 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:24 STATE: test-backend-node.js event: image +2023-01-06 13:02:24 STATE: test-backend-node.js event: detect +2023-01-06 13:02:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose +2023-01-06 13:02:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-01-06 13:02:24 DATA:  test-backend-node.js result: performance: load: null total: 356 +2023-01-06 13:02:24 STATE: test-backend-node.js passed: blazepose +2023-01-06 13:02:24 STATE: test-backend-node.js start efficientpose +2023-01-06 13:02:25 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:25 STATE: test-backend-node.js event: image +2023-01-06 13:02:25 STATE: test-backend-node.js event: detect +2023-01-06 13:02:25 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-01-06 13:02:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-01-06 13:02:25 DATA:  test-backend-node.js result: performance: load: null total: 360 +2023-01-06 13:02:25 STATE: test-backend-node.js passed: efficientpose +2023-01-06 13:02:25 STATE: test-backend-node.js start posenet +2023-01-06 13:02:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:26 STATE: test-backend-node.js event: image +2023-01-06 13:02:26 STATE: test-backend-node.js event: detect +2023-01-06 13:02:26 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet +2023-01-06 13:02:26 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-01-06 13:02:26 DATA:  test-backend-node.js result: performance: load: null total: 304 +2023-01-06 13:02:26 STATE: test-backend-node.js passed: posenet +2023-01-06 13:02:26 STATE: test-backend-node.js start movenet +2023-01-06 13:02:26 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:26 STATE: test-backend-node.js event: image +2023-01-06 13:02:27 STATE: test-backend-node.js event: detect +2023-01-06 13:02:27 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet +2023-01-06 13:02:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:27 DATA:  test-backend-node.js result: performance: load: null total: 324 +2023-01-06 13:02:27 STATE: test-backend-node.js passed: movenet +2023-01-06 13:02:27 INFO:  test-backend-node.js test face matching +2023-01-06 13:02:27 STATE: test-backend-node.js passed: face database 40 +2023-01-06 13:02:27 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2023-01-06 13:02:27 INFO:  test-backend-node.js test face similarity alternative +2023-01-06 13:02:27 STATE: test-backend-node.js start face embeddings +2023-01-06 13:02:27 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:27 STATE: test-backend-node.js event: image +2023-01-06 13:02:28 STATE: test-backend-node.js event: detect +2023-01-06 13:02:28 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:02:28 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:28 DATA:  test-backend-node.js result: performance: load: null total: 315 +2023-01-06 13:02:28 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} +2023-01-06 13:02:28 STATE: test-backend-node.js start face embeddings +2023-01-06 13:02:28 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:28 STATE: test-backend-node.js event: image +2023-01-06 13:02:29 STATE: test-backend-node.js event: detect +2023-01-06 13:02:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:02:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:29 DATA:  test-backend-node.js result: performance: load: null total: 303 +2023-01-06 13:02:29 STATE: test-backend-node.js passed: insightface {"embedding":512} +2023-01-06 13:02:29 INFO:  test-backend-node.js test face attention +2023-01-06 13:02:29 STATE: test-backend-node.js start face attention +2023-01-06 13:02:29 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:29 STATE: test-backend-node.js event: image +2023-01-06 13:02:29 STATE: test-backend-node.js event: detect +2023-01-06 13:02:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention +2023-01-06 13:02:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:29 DATA:  test-backend-node.js result: performance: load: null total: 284 +2023-01-06 13:02:29 STATE: test-backend-node.js passed: face attention +2023-01-06 13:02:29 INFO:  test-backend-node.js test detectors +2023-01-06 13:02:29 STATE: test-backend-node.js start detectors +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:30 STATE: test-backend-node.js event: image +2023-01-06 13:02:30 STATE: test-backend-node.js event: detect +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors +2023-01-06 13:02:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:30 DATA:  test-backend-node.js result: performance: load: null total: 133 +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detector result face match +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detector result hand match +2023-01-06 13:02:30 INFO:  test-backend-node.js test: multi-instance +2023-01-06 13:02:30 STATE: test-backend-node.js start multi instance +2023-01-06 13:02:30 STATE: test-backend-node.js event: image +2023-01-06 13:02:30 STATE: test-backend-node.js event: detect +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detect: random multi instance +2023-01-06 13:02:30 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-01-06 13:02:30 DATA:  test-backend-node.js result: performance: load: null total: 129 +2023-01-06 13:02:30 INFO:  test-backend-node.js test: first instance +2023-01-06 13:02:30 STATE: test-backend-node.js start multi instance +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:02:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:30 DATA:  test-backend-node.js result: performance: load: null total: 128 +2023-01-06 13:02:30 INFO:  test-backend-node.js test: second instance +2023-01-06 13:02:30 STATE: test-backend-node.js start multi instance +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:30 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:02:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:30 DATA:  test-backend-node.js result: performance: load: null total: 128 +2023-01-06 13:02:30 INFO:  test-backend-node.js test: concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js start concurrent +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-01-06 13:02:31 STATE: test-backend-node.js event: image +2023-01-06 13:02:31 STATE: test-backend-node.js event: image +2023-01-06 13:02:31 STATE: test-backend-node.js event: image +2023-01-06 13:02:32 STATE: test-backend-node.js event: detect +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js event: detect +2023-01-06 13:02:32 STATE: test-backend-node.js event: detect +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:32 DATA:  test-backend-node.js result: performance: load: null total: 1138 +2023-01-06 13:02:32 INFO:  test-backend-node.js test: monkey-patch +2023-01-06 13:02:32 STATE: test-backend-node.js event: image +2023-01-06 13:02:32 STATE: test-backend-node.js event: detect +2023-01-06 13:02:32 STATE: test-backend-node.js passed: monkey patch +2023-01-06 13:02:33 STATE: test-backend-node.js passed: segmentation [262144] +2023-01-06 13:02:33 STATE: test-backend-node.js passeed: equal usage +2023-01-06 13:02:33 INFO:  test-backend-node.js test: input compare +2023-01-06 13:02:33 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:33 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-01-06 13:02:33 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 +2023-01-06 13:02:33 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} +2023-01-06 13:02:33 INFO:  test-backend-node.js tensors 4453 +2023-01-06 13:02:33 INFO:  test-backend-node.js test complete: 19457 ms +2023-01-06 13:02:33 INFO:  +2023-01-06 13:02:33 INFO:  test-backend-node-gpu.js start +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js test: configuration validation +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: configuration default validation [] +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js test: model load +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js test: warmup +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: create human +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js human version: 3.0.1 +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js tfjs version: 4.2.0 +2023-01-06 13:02:34 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: set backend: tensorflow +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js tensors 1785 +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: load models 11 +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: warmup: none default +2023-01-06 13:02:34 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-01-06 13:02:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: null +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js passed: warmup none result match +2023-01-06 13:02:34 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js event: warmup +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js passed: warmup: face default +2023-01-06 13:02:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-01-06 13:02:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1908 +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js passed: warmup face result match +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js event: warmup +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js passed: warmup: body default +2023-01-06 13:02:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 180 +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js passed: warmup body result match +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-01-06 13:02:36 INFO:  test-backend-node-gpu.js test: details verification +2023-01-06 13:02:36 STATE: test-backend-node-gpu.js start default +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default +2023-01-06 13:02:37 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:37 DATA:  test-backend-node-gpu.js result: performance: load: null total: 139 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face length 1 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details body length 1 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details hand length 1 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details gesture length 7 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details object length 1 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: details object 0.72 person +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:37 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-01-06 13:02:38 INFO:  test-backend-node-gpu.js test default +2023-01-06 13:02:38 STATE: test-backend-node-gpu.js start async +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 133 +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 +2023-01-06 13:02:39 INFO:  test-backend-node-gpu.js test sync +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js start sync +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 131 +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 +2023-01-06 13:02:39 INFO:  test-backend-node-gpu.js test: image process +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] +2023-01-06 13:02:39 INFO:  test-backend-node-gpu.js test: image null +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor +2023-01-06 13:02:39 INFO:  test-backend-node-gpu.js test face similarity +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js start face similarity +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 123 +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js start face similarity +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 124 +2023-01-06 13:02:39 STATE: test-backend-node-gpu.js start face similarity +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-01-06 13:02:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-01-06 13:02:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 137 +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: face descriptor +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475006597943763,0.5570889682705169],"descriptors":[1024,1024,1024]} +2023-01-06 13:02:40 INFO:  test-backend-node-gpu.js test object +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js start object +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:02:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 144 +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js passed: centernet +2023-01-06 13:02:40 STATE: test-backend-node-gpu.js start object +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:02:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 553 +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js passed: nanodet +2023-01-06 13:02:41 INFO:  test-backend-node-gpu.js test sensitive +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js start sensitive +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:41 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive +2023-01-06 13:02:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 132 +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: sensitive result match +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: sensitive face result match +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: sensitive body result match +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js passed: sensitive hand result match +2023-01-06 13:02:42 INFO:  test-backend-node-gpu.js test body +2023-01-06 13:02:42 STATE: test-backend-node-gpu.js start blazepose +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose +2023-01-06 13:02:44 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-01-06 13:02:44 DATA:  test-backend-node-gpu.js result: performance: load: null total: 300 +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js passed: blazepose +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js start efficientpose +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:44 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:45 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-01-06 13:02:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-01-06 13:02:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1104 +2023-01-06 13:02:45 STATE: test-backend-node-gpu.js passed: efficientpose +2023-01-06 13:02:45 STATE: test-backend-node-gpu.js start posenet +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet +2023-01-06 13:02:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-01-06 13:02:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 144 +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: posenet +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js start movenet +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet +2023-01-06 13:02:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 142 +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: movenet +2023-01-06 13:02:46 INFO:  test-backend-node-gpu.js test face matching +2023-01-06 13:02:46 STATE: test-backend-node-gpu.js passed: face database 40 +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829337456107771}} {"second":{"index":4,"similarity":0.5002921100080195}} {"third":{"index":4,"similarity":0.5402938601022594}} +2023-01-06 13:02:47 INFO:  test-backend-node-gpu.js test face similarity alternative +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js start face embeddings +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:02:47 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:47 DATA:  test-backend-node-gpu.js result: performance: load: null total: 232 +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} +2023-01-06 13:02:47 STATE: test-backend-node-gpu.js start face embeddings +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:02:48 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:48 DATA:  test-backend-node-gpu.js result: performance: load: null total: 203 +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} +2023-01-06 13:02:48 INFO:  test-backend-node-gpu.js test face attention +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js start face attention +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:48 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 531 +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: face attention +2023-01-06 13:02:49 INFO:  test-backend-node-gpu.js test detectors +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start detectors +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 67 +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detector result face match +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detector result hand match +2023-01-06 13:02:49 INFO:  test-backend-node-gpu.js test: multi-instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start multi instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detect: random multi instance +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 44 +2023-01-06 13:02:49 INFO:  test-backend-node-gpu.js test: first instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start multi instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 65 +2023-01-06 13:02:49 INFO:  test-backend-node-gpu.js test: second instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start multi instance +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 44 +2023-01-06 13:02:49 INFO:  test-backend-node-gpu.js test: concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js start concurrent +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:50 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:02:51 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-01-06 13:02:51 INFO:  test-backend-node-gpu.js test: monkey-patch +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js event: image +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js event: detect +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: monkey patch +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: segmentation [262144] +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passeed: equal usage +2023-01-06 13:02:51 INFO:  test-backend-node-gpu.js test: input compare +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2023-01-06 13:02:51 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 +2023-01-06 13:02:51 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} +2023-01-06 13:02:51 INFO:  test-backend-node-gpu.js tensors 4453 +2023-01-06 13:02:51 INFO:  test-backend-node-gpu.js test complete: 17768 ms +2023-01-06 13:02:52 INFO:  +2023-01-06 13:02:52 INFO:  test-backend-node-wasm.js start +2023-01-06 13:02:52 DATA:  test-backend-node-wasm.js stdout: 2023-01-06 13:02:52 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.2.0/dist/ +2023-01-06 13:02:52 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ +2023-01-06 13:02:52 INFO:  test-backend-node-wasm.js test: configuration validation +2023-01-06 13:02:52 STATE: test-backend-node-wasm.js passed: configuration default validation [] +2023-01-06 13:02:52 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-01-06 13:02:52 INFO:  test-backend-node-wasm.js test: model load +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"}] +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js test: warmup +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: create human +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js human version: 3.0.1 +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js tfjs version: 4.2.0 +2023-01-06 13:02:54 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: set backend: wasm +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js tensors 1785 +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: load models 11 +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: warmup: none default +2023-01-06 13:02:54 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-01-06 13:02:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: null +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js passed: warmup none result match +2023-01-06 13:02:54 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: warmup +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js passed: warmup: face default +2023-01-06 13:02:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-01-06 13:02:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 470 +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js passed: warmup face result match +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: warmup +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js passed: warmup: body default +2023-01-06 13:02:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 329 +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js passed: warmup body result match +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-01-06 13:02:55 INFO:  test-backend-node-wasm.js test: details verification +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js start default +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:02:55 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default +2023-01-06 13:02:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 334 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face length 1 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details body length 1 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details hand length 1 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details gesture length 7 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details object length 1 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: details object 0.72 person +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-01-06 13:02:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:57 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-01-06 13:02:58 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-01-06 13:02:59 INFO:  test-backend-node-wasm.js test default +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js start async +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async +2023-01-06 13:02:59 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:02:59 DATA:  test-backend-node-wasm.js result: performance: load: null total: 323 +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 +2023-01-06 13:02:59 INFO:  test-backend-node-wasm.js test sync +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js start sync +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:02:59 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync +2023-01-06 13:03:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:03:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 327 +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 +2023-01-06 13:03:00 INFO:  test-backend-node-wasm.js test: image process +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] +2023-01-06 13:03:00 INFO:  test-backend-node-wasm.js test: image null +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor +2023-01-06 13:03:00 INFO:  test-backend-node-wasm.js test face similarity +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js start face similarity +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity +2023-01-06 13:03:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-01-06 13:03:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 294 +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js start face similarity +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:00 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 297 +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js start face similarity +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 295 +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: face descriptor +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} +2023-01-06 13:03:01 INFO:  test-backend-node-wasm.js test object +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js start object +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-01-06 13:03:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 305 +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js passed: centernet +2023-01-06 13:03:01 STATE: test-backend-node-wasm.js start object +2023-01-06 13:03:03 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} +2023-01-06 13:03:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:03 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-01-06 13:03:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 199 +2023-01-06 13:03:04 ERROR: test-backend-node-wasm.js failed: nanodet [] +2023-01-06 13:03:04 INFO:  test-backend-node-wasm.js test sensitive +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js start sensitive +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive +2023-01-06 13:03:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 211 +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: sensitive result match +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: sensitive face result match +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: sensitive body result match +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js passed: sensitive hand result match +2023-01-06 13:03:04 INFO:  test-backend-node-wasm.js test body +2023-01-06 13:03:04 STATE: test-backend-node-wasm.js start blazepose +2023-01-06 13:03:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:07 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:08 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:08 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose +2023-01-06 13:03:08 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-01-06 13:03:08 DATA:  test-backend-node-wasm.js result: performance: load: null total: 389 +2023-01-06 13:03:08 STATE: test-backend-node-wasm.js passed: blazepose +2023-01-06 13:03:08 STATE: test-backend-node-wasm.js start efficientpose +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-01-06 13:03:09 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-01-06 13:03:09 DATA:  test-backend-node-wasm.js result: performance: load: null total: 649 +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js passed: efficientpose +2023-01-06 13:03:09 STATE: test-backend-node-wasm.js start posenet +2023-01-06 13:03:10 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:10 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet +2023-01-06 13:03:11 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-01-06 13:03:11 DATA:  test-backend-node-wasm.js result: performance: load: null total: 254 +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: posenet +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js start movenet +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet +2023-01-06 13:03:11 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:11 DATA:  test-backend-node-wasm.js result: performance: load: null total: 209 +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: movenet +2023-01-06 13:03:11 INFO:  test-backend-node-wasm.js test face matching +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: face database 40 +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} +2023-01-06 13:03:11 INFO:  test-backend-node-wasm.js test face similarity alternative +2023-01-06 13:03:11 STATE: test-backend-node-wasm.js start face embeddings +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:03:12 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:12 DATA:  test-backend-node-wasm.js result: performance: load: null total: 230 +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} +2023-01-06 13:03:12 STATE: test-backend-node-wasm.js start face embeddings +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-01-06 13:03:13 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:13 DATA:  test-backend-node-wasm.js result: performance: load: null total: 261 +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} +2023-01-06 13:03:13 INFO:  test-backend-node-wasm.js test face attention +2023-01-06 13:03:13 STATE: test-backend-node-wasm.js start face attention +2023-01-06 13:03:14 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention +2023-01-06 13:03:14 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 112 +2023-01-06 13:03:14 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} +2023-01-06 13:03:14 INFO:  test-backend-node-wasm.js test detectors +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js start detectors +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors +2023-01-06 13:03:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 103 +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: detector result face match +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js passed: detector result hand match +2023-01-06 13:03:14 INFO:  test-backend-node-wasm.js test: multi-instance +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js start multi instance +2023-01-06 13:03:14 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: detect: random multi instance +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: performance: load: null total: 98 +2023-01-06 13:03:15 INFO:  test-backend-node-wasm.js test: first instance +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start multi instance +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: performance: load: null total: 102 +2023-01-06 13:03:15 INFO:  test-backend-node-wasm.js test: second instance +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start multi instance +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:03:15 DATA:  test-backend-node-wasm.js result: performance: load: null total: 106 +2023-01-06 13:03:15 INFO:  test-backend-node-wasm.js test: concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js start concurrent +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:15 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:16 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-01-06 13:03:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 947 +2023-01-06 13:03:17 INFO:  test-backend-node-wasm.js test: monkey-patch +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js event: image +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js event: detect +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: monkey patch +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passed: segmentation [262144] +2023-01-06 13:03:17 STATE: test-backend-node-wasm.js passeed: equal usage +2023-01-06 13:03:17 INFO:  test-backend-node-wasm.js test: input compare +2023-01-06 13:03:18 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-01-06 13:03:18 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-01-06 13:03:18 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 +2023-01-06 13:03:18 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} +2023-01-06 13:03:18 INFO:  test-backend-node-wasm.js tensors 4455 +2023-01-06 13:03:18 INFO:  test-backend-node-wasm.js test complete: 25332 ms +2023-01-06 13:03:18 STATE: all tests complete +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"test-node-gear.js","passed":3,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} +2023-01-06 13:03:18 INFO:  status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} +2023-01-06 13:03:18 INFO:  failures {"count":2} +2023-01-06 13:03:18 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} +2023-01-06 13:03:18 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html index 6de3136b..8d52adb5 100644 --- a/typedoc/classes/GraphModel.html +++ b/typedoc/classes/GraphModel.html @@ -37,7 +37,7 @@ the command line converter tool and loaded via tf.loadGraphModel.
  • InferenceModel
    • +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:34
    • @@ -97,7 +97,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Optional tfio: __module

    Returns GraphModel<ModelURL>

    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:66

    • Accessors

    @@ -107,7 +107,7 @@ the command line converter tool and loaded via tf.loadGraphModel.

    Returns string[]

    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49

    • inputs

      @@ -116,7 +116,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns TensorInfo[]
    +
  • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:51

    • metadata

      @@ -125,7 +125,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
      +
    • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:54

      • modelSignature

        @@ -134,7 +134,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
        +
      • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:55

        • modelStructuredOutputKeys

          @@ -143,7 +143,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns {}
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:56

          • modelVersion

            @@ -151,7 +151,7 @@ the command line converter tool and loaded via tf.loadGraphModel.

            Returns string

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48

          • outputNodes

            @@ -159,7 +159,7 @@ the command line converter tool and loaded via tf.loadGraphModel.

            Returns string[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:50

          • outputs

            @@ -168,7 +168,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Returns TensorInfo[]
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:52

          • weights

            @@ -176,7 +176,7 @@ the command line converter tool and loaded via tf.loadGraphModel.

            Returns NamedTensorsMap

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:53

          • Methods

          @@ -189,7 +189,7 @@ the command line converter tool and loaded via tf.loadGraphModel.Doc

          Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:266

          • disposeIntermediateTensors

            @@ -201,7 +201,7 @@ KEEP_INTERMEDIATE_TENSORS is true).

            Doc

            Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:259

          • execute

            @@ -233,7 +233,7 @@ outputs array.

            Returns Tensor<Rank> | Tensor<Rank>[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:228

          • executeAsync

            @@ -264,7 +264,7 @@ array.

          Returns Promise<Tensor<Rank> | Tensor<Rank>[]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:245

          • getIntermediateTensors

            @@ -276,7 +276,7 @@ KEEP_INTERMEDIATE_TENSORS is true).

            Doc

            Returns NamedTensorsMap

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:252

          • load

            @@ -287,7 +287,7 @@ compile the inference graph.

            Returns UrlIOHandler<ModelURL> extends IOHandlerSync ? boolean : Promise<boolean>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:72

          • loadSync

            @@ -304,7 +304,7 @@ compile the inference graph.

            artifacts: ModelArtifacts

          Returns boolean

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:79

          • predict

            @@ -346,7 +346,7 @@ Currently the batch size option is ignored for graph model.

            Returns Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:165

          • predictAsync

            @@ -388,7 +388,7 @@ Currently the batch size option is ignored for graph model.

          Returns Promise<Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:206

          • save

            @@ -432,7 +432,7 @@ scheme-based string shortcut for IOHandler.

          Returns Promise<SaveResult>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@4.2.0_tkoh6rxfpzme3tc2ndqbqcrg7y/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:124
          • diff --git a/typedoc/classes/Tensor-1.html b/typedoc/classes/Tensor-1.html index 2bd4da56..d4a27d6e 100644 --- a/typedoc/classes/Tensor-1.html +++ b/typedoc/classes/Tensor-1.html @@ -40,145 +40,145 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0)
          • TensorInfo
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:116
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:131
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.d.ts:4
          • +
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          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.d.ts:20
          • @@ -384,7 +384,7 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0)
          id: number

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:157

          • Properties

          @@ -395,7 +395,7 @@ point to the same bucket (e.g. when calling array.reshape()).

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:138

          • Readonly dtype

          dtype: keyof DataTypeMap
          @@ -403,35 +403,35 @@ point to the same bucket (e.g. when calling array.reshape()).

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:144

          • Readonly id

          id: number

          Unique id of this tensor.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:133

          • kept

          kept: boolean

          Whether this tensor has been globally kept.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:148

          • Readonly rankType

          rankType: R

          The rank type for the array (see Rank enum).

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:146

          • scopeId

          scopeId: number

          The id of the scope this tensor is being tracked in.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:150

          • Readonly shape

          shape: ShapeMap[R]
          @@ -439,14 +439,14 @@ point to the same bucket (e.g. when calling array.reshape()).

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:140

          • Readonly size

          size: number

          Number of elements in the tensor.

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:142

          • Readonly strides

          strides: number[]
          @@ -455,7 +455,7 @@ point to the same bucket (e.g. when calling array.reshape()).

          numpy.ndarray.strides.html

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:156

          • Accessors

          @@ -465,7 +465,7 @@ numpy.ndarray.strides.html

        • Returns boolean

          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:244

          • rank

            @@ -473,7 +473,7 @@ numpy.ndarray.strides.html

          • Returns number

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:158

          • Methods

          @@ -493,7 +493,7 @@ numpy.ndarray.strides.html

          this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.d.ts:21

          • acos

            @@ -511,7 +511,7 @@ numpy.ndarray.strides.html

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.d.ts:21

          • acosh

            @@ -529,7 +529,7 @@ numpy.ndarray.strides.html

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.d.ts:21

          • add

            @@ -547,7 +547,7 @@ numpy.ndarray.strides.html

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:21

          • all

            @@ -569,7 +569,7 @@ numpy.ndarray.strides.html

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.d.ts:5

          • any

            @@ -591,7 +591,7 @@ numpy.ndarray.strides.html

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.d.ts:5

          • argMax

            @@ -609,7 +609,7 @@ numpy.ndarray.strides.html

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.d.ts:5

          • argMin

            @@ -627,7 +627,7 @@ numpy.ndarray.strides.html

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.d.ts:5

          • array

            @@ -639,7 +639,7 @@ asynchronously.

            Doc

            Returns Promise<ArrayMap[R]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:176

          • arraySync

            @@ -651,7 +651,7 @@ synchronously.

            Doc

            Returns ArrayMap[R]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:183

          • as1D

            @@ -664,7 +664,7 @@ synchronously.

            T extends Tensor<Rank, T>

          Returns Tensor1D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.d.ts:21

          • as2D

            @@ -684,7 +684,7 @@ synchronously.

            columns: number

          Returns Tensor2D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.d.ts:21

          • as3D

            @@ -706,7 +706,7 @@ synchronously.

            depth: number

          Returns Tensor3D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.d.ts:21

          • as4D

            @@ -730,7 +730,7 @@ synchronously.

            depth2: number

          Returns Tensor4D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.d.ts:21

          • as5D

            @@ -756,7 +756,7 @@ synchronously.

            depth3: number

          Returns Tensor5D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.d.ts:21

          • asScalar

            @@ -769,7 +769,7 @@ synchronously.

            T extends Tensor<Rank, T>

          Returns Scalar

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.d.ts:21

          • asType

            @@ -789,7 +789,7 @@ synchronously.

            dtype: keyof DataTypeMap

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.d.ts:21

          • asin

            @@ -807,7 +807,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.d.ts:21

          • asinh

            @@ -825,7 +825,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.d.ts:21

          • atan

            @@ -843,7 +843,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.d.ts:21

          • atan2

            @@ -861,7 +861,7 @@ synchronously.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.d.ts:5

          • atanh

            @@ -879,7 +879,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.d.ts:21

          • avgPool

            @@ -903,7 +903,7 @@ synchronously.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.d.ts:22

          • batchNorm

            @@ -929,7 +929,7 @@ synchronously.

            Optional varianceEpsilon: number

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.d.ts:5

          • batchToSpaceND

            @@ -949,7 +949,7 @@ synchronously.

            crops: number[][]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.d.ts:5

          • broadcastTo

            @@ -967,7 +967,7 @@ synchronously.

            shape: ShapeMap[R]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.d.ts:21

          • buffer

            @@ -983,7 +983,7 @@ synchronously.

            D extends keyof DataTypeMap = "float32"

          Returns Promise<TensorBuffer<R, D>>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:164

          • bufferSync

            @@ -999,7 +999,7 @@ synchronously.

            D extends keyof DataTypeMap = "float32"

          Returns TensorBuffer<R, D>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:169

          • bytes

            @@ -1009,7 +1009,7 @@ synchronously.

            Returns Promise<Uint8Array | Uint8Array[]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:236

          • cast

            @@ -1027,7 +1027,7 @@ synchronously.

            dtype: keyof DataTypeMap

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.d.ts:21

          • ceil

            @@ -1045,7 +1045,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.d.ts:21

          • clipByValue

            @@ -1065,7 +1065,7 @@ synchronously.

            max: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.d.ts:21

          • clone

            @@ -1086,7 +1086,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:259

          • concat

            @@ -1106,7 +1106,7 @@ synchronously.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.d.ts:5

          • conv1d

            @@ -1134,7 +1134,7 @@ synchronously.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.d.ts:6

          • conv2d

            @@ -1162,7 +1162,7 @@ synchronously.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.d.ts:5

          • conv2dTranspose

            @@ -1188,7 +1188,7 @@ synchronously.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.d.ts:5

          • cos

            @@ -1206,7 +1206,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.d.ts:21

          • cosh

            @@ -1224,7 +1224,7 @@ synchronously.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.d.ts:21

          • cumprod

            @@ -1246,7 +1246,7 @@ synchronously.

            Optional reverse: boolean

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.d.ts:5

          • cumsum

            @@ -1268,7 +1268,7 @@ synchronously.

            Optional reverse: boolean

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.d.ts:5

          • data

            @@ -1285,7 +1285,7 @@ promise of TypedArray that resolves when the computation has finish

            D extends keyof DataTypeMap = NumericDataType

          Returns Promise<DataTypeMap[D]>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:190

          • dataSync

            @@ -1302,7 +1302,7 @@ UI thread until the values are ready, which can cause performance issues.

            D extends keyof DataTypeMap = NumericDataType

          Returns DataTypeMap[D]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:234

          • dataToGPU

            @@ -1333,7 +1333,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional options: DataToGPUWebGLOption

          Returns GPUData

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:227

          • depthToSpace

            @@ -1353,7 +1353,7 @@ parameter, so can not use a user-defined size to create the buffer.

            dataFormat: "NHWC" | "NCHW"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.d.ts:5

          • depthwiseConv2d

            @@ -1381,7 +1381,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.d.ts:5

          • dilation2d

            @@ -1407,7 +1407,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dataFormat: "NHWC"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.d.ts:5

          • dispose

            @@ -1418,7 +1418,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Doc

            Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:242

          • div

            @@ -1436,7 +1436,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:21

          • divNoNan

            @@ -1454,7 +1454,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:21

          • dot

            @@ -1472,7 +1472,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:5

          • elu

            @@ -1485,7 +1485,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.d.ts:5

          • equal

            @@ -1503,7 +1503,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:5

          • erf

            @@ -1521,7 +1521,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.d.ts:21

          • euclideanNorm

            @@ -1543,7 +1543,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.d.ts:21

          • exp

            @@ -1561,7 +1561,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.d.ts:21

          • expandDims

            @@ -1579,7 +1579,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.d.ts:5

          • expm1

            @@ -1597,7 +1597,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.d.ts:21

          • fft

            @@ -1615,7 +1615,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.d.ts:21

          • flatten

            @@ -1628,7 +1628,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns Tensor1D

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.d.ts:21

          • floor

            @@ -1646,7 +1646,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.d.ts:21

          • floorDiv

            @@ -1664,7 +1664,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.d.ts:5

          • gather

            @@ -1686,7 +1686,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.d.ts:21

          • greater

            @@ -1704,7 +1704,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:5

          • greaterEqual

            @@ -1722,7 +1722,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:5

          • ifft

            @@ -1740,7 +1740,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.d.ts:21

          • irfft

            @@ -1758,7 +1758,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.d.ts:21

          • isFinite

            @@ -1776,7 +1776,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.d.ts:21

          • isInf

            @@ -1794,7 +1794,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.d.ts:21

          • isNaN

            @@ -1812,7 +1812,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.d.ts:21

          • leakyRelu

            @@ -1830,7 +1830,7 @@ parameter, so can not use a user-defined size to create the buffer.

            alpha: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.d.ts:5

          • less

            @@ -1848,7 +1848,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.d.ts:5

          • lessEqual

            @@ -1866,7 +1866,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:5

          • localResponseNormalization

            @@ -1890,7 +1890,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional beta: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.d.ts:5

          • log

            @@ -1908,7 +1908,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.d.ts:21

          • log1p

            @@ -1926,7 +1926,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.d.ts:21

          • logSigmoid

            @@ -1944,7 +1944,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.d.ts:21

          • logSoftmax

            @@ -1964,7 +1964,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.d.ts:21

          • logSumExp

            @@ -1986,7 +1986,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.d.ts:5

          • logicalAnd

            @@ -2004,7 +2004,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.d.ts:5

          • logicalNot

            @@ -2017,7 +2017,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.d.ts:5

          • logicalOr

            @@ -2035,7 +2035,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.d.ts:5

          • logicalXor

            @@ -2053,7 +2053,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.d.ts:5

          • matMul

            @@ -2075,7 +2075,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional transposeB: boolean

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.d.ts:5

          • max

            @@ -2095,7 +2095,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.d.ts:21

          • maxPool

            @@ -2119,7 +2119,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.d.ts:22

          • maximum

            @@ -2137,7 +2137,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.d.ts:5

          • mean

            @@ -2157,7 +2157,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.d.ts:5

          • min

            @@ -2177,7 +2177,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.d.ts:21

          • minimum

            @@ -2195,7 +2195,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.d.ts:5

          • mirrorPad

            @@ -2215,7 +2215,7 @@ parameter, so can not use a user-defined size to create the buffer.

            mode: "reflect" | "symmetric"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.d.ts:21

          • mod

            @@ -2233,7 +2233,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.d.ts:5

          • mul

            @@ -2251,7 +2251,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.d.ts:5

          • neg

            @@ -2269,7 +2269,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.d.ts:21

          • norm

            @@ -2291,7 +2291,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.d.ts:21

          • notEqual

            @@ -2309,7 +2309,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.d.ts:5

          • oneHot

            @@ -2326,7 +2326,7 @@ parameter, so can not use a user-defined size to create the buffer.

            offValue: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.d.ts:21

          • onesLike

            @@ -2344,7 +2344,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.d.ts:21

          • pad

            @@ -2364,7 +2364,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional constantValue: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.d.ts:21

          • pool

            @@ -2392,7 +2392,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dimRoundingMode: "round" | "floor" | "ceil"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.d.ts:22

          • pow

            @@ -2410,7 +2410,7 @@ parameter, so can not use a user-defined size to create the buffer.

            exp: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.d.ts:5

          • prelu

            @@ -2428,7 +2428,7 @@ parameter, so can not use a user-defined size to create the buffer.

            alpha: TensorLike | T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.d.ts:5

          • print

            @@ -2447,7 +2447,7 @@ parameter, so can not use a user-defined size to create the buffer.

          Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:254

          • prod

            @@ -2469,7 +2469,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.d.ts:5

          • reciprocal

            @@ -2487,7 +2487,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.d.ts:21

          • relu

            @@ -2500,7 +2500,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.d.ts:5

          • relu6

            @@ -2513,7 +2513,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.d.ts:5

          • reshape

            @@ -2531,7 +2531,7 @@ parameter, so can not use a user-defined size to create the buffer.

            shape: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.d.ts:5

          • reshapeAs

            @@ -2549,7 +2549,7 @@ parameter, so can not use a user-defined size to create the buffer.

            x: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.d.ts:21

          • resizeBilinear

            @@ -2571,7 +2571,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional halfPixelCenters: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.d.ts:5

          • resizeNearestNeighbor

            @@ -2593,7 +2593,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional halfFloatCenters: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.d.ts:5

          • reverse

            @@ -2613,7 +2613,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number | number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.d.ts:5

          • rfft

            @@ -2631,7 +2631,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: Tensor<Rank>

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.d.ts:21

          • round

            @@ -2649,7 +2649,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.d.ts:21

          • rsqrt

            @@ -2667,7 +2667,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.d.ts:21

          • selu

            @@ -2680,7 +2680,7 @@ parameter, so can not use a user-defined size to create the buffer.

            T extends Tensor<Rank, T>

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.d.ts:5

          • separableConv2d

            @@ -2708,7 +2708,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dataFormat: "NHWC" | "NCHW"

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.d.ts:5

          • sigmoid

            @@ -2726,7 +2726,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.d.ts:21

          • sign

            @@ -2744,7 +2744,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.d.ts:21

          • sin

            @@ -2762,7 +2762,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.d.ts:21

          • sinh

            @@ -2780,7 +2780,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.d.ts:21

          • slice

            @@ -2802,7 +2802,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional size: number | number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.d.ts:21

          • softmax

            @@ -2822,7 +2822,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dim: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.d.ts:21

          • softplus

            @@ -2840,7 +2840,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.d.ts:21

          • spaceToBatchND

            @@ -2860,7 +2860,7 @@ parameter, so can not use a user-defined size to create the buffer.

            paddings: number[][]

          Returns Tensor<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.d.ts:5

          • split

            @@ -2880,7 +2880,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.d.ts:5

          • sqrt

            @@ -2898,7 +2898,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.d.ts:21

          • square

            @@ -2916,7 +2916,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.d.ts:21

          • squaredDifference

            @@ -2934,7 +2934,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.d.ts:21

          • squeeze

            @@ -2952,7 +2952,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.d.ts:5

          • stack

            @@ -2972,7 +2972,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.d.ts:5

          • step

            @@ -2992,7 +2992,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional alpha: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.d.ts:21

          • stridedSlice

            @@ -3026,7 +3026,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional shrinkAxisMask: number

          Returns Tensor<Rank>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.d.ts:21

          • sub

            @@ -3044,7 +3044,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.d.ts:5

          • sum

            @@ -3064,7 +3064,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional keepDims: boolean

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.d.ts:5

          • tan

            @@ -3082,7 +3082,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.d.ts:21

          • tanh

            @@ -3100,7 +3100,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.d.ts:21

          • throwIfDisposed

            @@ -3108,7 +3108,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • Returns void

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:245

          • tile

            @@ -3126,7 +3126,7 @@ parameter, so can not use a user-defined size to create the buffer.

            b: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.d.ts:21

          • toBool

            @@ -3144,7 +3144,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.d.ts:21

          • toFloat

            @@ -3162,7 +3162,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.d.ts:21

          • toInt

            @@ -3180,7 +3180,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.d.ts:21

          • toString

            @@ -3196,7 +3196,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional verbose: boolean

          Returns string

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:265

          • topk

            @@ -3223,7 +3223,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • values: T
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.d.ts:21

          • transpose

            @@ -3241,7 +3241,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional perm: number[]

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.d.ts:21

          • unique

            @@ -3266,7 +3266,7 @@ parameter, so can not use a user-defined size to create the buffer.

          • values: T
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.d.ts:21

          • unsortedSegmentSum

            @@ -3288,7 +3288,7 @@ parameter, so can not use a user-defined size to create the buffer.

            numSegments: number

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.d.ts:21

          • unstack

            @@ -3306,7 +3306,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional axis: number

          Returns T[]

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.d.ts:5

          • variable

            @@ -3323,7 +3323,7 @@ parameter, so can not use a user-defined size to create the buffer.

            Optional dtype: keyof DataTypeMap

          Returns Variable<R>

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:266

          • where

            @@ -3343,7 +3343,7 @@ parameter, so can not use a user-defined size to create the buffer.

            x: Tensor<Rank> | TensorLike

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.d.ts:21

          • zerosLike

            @@ -3361,7 +3361,7 @@ parameter, so can not use a user-defined size to create the buffer.

            this: T

          Returns T

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.d.ts:21
          • diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html index ac06916f..6fb68a67 100644 --- a/typedoc/enums/Rank.html +++ b/typedoc/enums/Rank.html @@ -16,7 +16,7 @@
        • Rank

          • Enumeration Rank

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:61
          • @@ -38,37 +38,37 @@

          R0

          R0: "R0"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:62

          • R1

          R1: "R1"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:63

          • R2

          R2: "R2"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:64

          • R3

          R3: "R3"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:65

          • R4

          R4: "R4"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:66

          • R5

          R5: "R5"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67

          • R6

          R6: "R6"
          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:68
          • diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html index 6669b360..0263b07f 100644 --- a/typedoc/modules/Tensor.html +++ b/typedoc/modules/Tensor.html @@ -19,145 +19,145 @@

          Explict reexport of main @tensorflow/tfjs types

          +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:116
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:131
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.d.ts:21
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.d.ts:5
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.d.ts:4
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:20
          • +
        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.d.ts:20
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.d.ts:4
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        • Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.d.ts:20
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          • diff --git a/typedoc/types/Tensor4D.html b/typedoc/types/Tensor4D.html index ebe7712f..427556de 100644 --- a/typedoc/types/Tensor4D.html +++ b/typedoc/types/Tensor4D.html @@ -20,7 +20,7 @@

          Doclink

          Tensor

          +
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          • diff --git a/types/human.d.ts b/types/human.d.ts index df5fd639..0ab06037 100644 --- a/types/human.d.ts +++ b/types/human.d.ts @@ -2346,6 +2346,7 @@ export declare type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'sta * ============================================================================= */ /// +/// /** @docalias number[] */ declare interface ShapeMap { R0: number[];